Network-Based T-S Fuzzy Dynamic Positioning Controller Design for Unmanned Marine Vehicles

Adaptive general type-2 fuzzy model-based control for nonlinear networked systems with packet dropouts

In this paper, a novel adaptive general type-2 fuzzy model-based control (AGT2-FMBC) is proposed for networked control systems (NCSs) with packet dropouts. A general type-2 fuzzy model (GT2FM) is designed to represent the nonlinear system with uncertainties online, wherein the antecedents of fuzzy rules are defined by general type-2 fuzzy sets and the consequents by Takagi-Sugeno type models. Utilizing the Bernoulli distribution, packet dropouts in the two communication channels are effectively modeled. To mitigate their negative effect, we introduce buffers at the input of both the controller and the nonlinear system. A general type-2 fuzzy controller (GT2FC) is proposed to achieve optimal tracking performance based on the parallel distributed compensation approach. The GT2FC shares the same antecedent parts with the GT2FM, while its consequent parts serve as local inverse model controllers for corresponding consequent parts of the GT2FM. To reduce computation time in the type-reducer, a direct defuzzification method is utilized, introducing two factors to determine the participation of the lower and upper bounds. We introduce a novel algorithm to provide adaptive online laws for the parameters of AGT2-FMBC, ensuring convergence through the Lyapunov stability theorem. The robustness of the proposed controller is demonstrated through its application to three nonlinear NCSs, subject to packet dropouts and uncertainties.

Safety-Critical Receding-Horizon Planning and Formation Control of Autonomous Surface Vehicles via Collaborative Neurodynamic Optimization

This article addresses the safety-critical receding-horizon planning and formation control of autonomous surface vehicles (ASVs) in the presence of model uncertainties, environmental disturbances, as well as stationary and moving obstacles. A three-level formation control architecture is proposed with a safety-critical formation trajectory generation module at its high level, a collision-free guidance module at its middle level, and an anti-disturbance control module at its low level. Specifically, a safety-critical formation trajectory generator is designed by leveraging collaborative neurodynamic optimization to plan safe formation trajectories to track a given trajectory and avoid stationary obstacles in a receding-horizon manner. Based on control barrier functions, a collision-free line-of-sight guidance law is developed to generate safe guidance commands to avoid collision with moving obstacles and other vehicles. An anti-disturbance control law is customized with a finite-time convergent observer for a vehicle to follow the guidance command signals. Simulation and hardware-in-the-loop experimental results are elaborated to validate the efficacy of the proposed method for the receding-horizon planning and formation control of ASVs.

Event-triggered neuroadaptive predefined practical finite-time control for dynamic positioning vessels: A time-based generator approach

This paper discusses the predefined practical finite-time (PPFT) dynamic positioning (DP) control problem for DP vessels subject to internal/external uncertainties. Those heterogeneity uncertainties are handled by a separate-type treatment approach. The finite-time (FT) DP control is fulfilled by a predefined FT function on the basis of a time-based generator (TBG). Under the dynamic surface control together with the TBG design framework, the convergence time and control accuracy of the DP system can be determined by the designer offline. Meanwhile, the virtual derivation and computational burden problems are dissolved by using a first-order filter and virtual parameter learning technique. To reduce mechanical wear, an event-triggering protocol between the control law and the actuator is built to reduce the operating frequency of the actuator. An event-triggered neuroadaptive PPFT control scheme is presented for DP vessels. The stability of the closed-loop DP control systems is validated via the Lyapunov theorem. Approach efficiency is confirmed by numerical examples.

Mean-square consensus of a semi-Markov jump multi-agent system based on event-triggered stochastic sampling

This paper focuses on achieving leader-follower mean square consensus in semi-Markov jump multi-agent systems. To effectively reduce communication costs and control updates, we propose an event-triggered protocol based on stochastic sampling. The stochastic sampling interval randomly switches between finite given values, while the event-triggered function depends on the stochastic sampled data from neighboring agents. Using the event-triggered strategy, we present sufficient conditions to ensure mean square consensus. Finally, we provide a numerical example demonstrating the effectiveness of the theoretical results.

Multi-Instant Observer Design of Discrete-Time Fuzzy Systems via An Enhanced Gain-Scheduling Mechanism

This article is concerned with developing a featured multi-instant Luenberger-like observer of discrete-time Takagi-Sugeno fuzzy systems with unmeasurable state variables, that is, not only to reduce the conservatism but also (at the same time) to alleviate the computational complexity over the recent approach reported in the literature. Contrary to previous approaches, an enhanced gain-scheduling mechanism is proposed for constructing much abundant working modes by online evaluating the updated variation information of normalized fuzzy weighting functions across two adjacent sampling instants and, thus, a different group of observer gain matrices with less conservatism is designed in order to employ the exclusive features for each working mode. Moreover, all the redundant terms containing both surplus and unknown system information are discriminated and removed in this study and, thus, the required computational complexity is reduced to a certain extent than the counterpart one. Finally, numerical examples are provided to illustrate the superiority of the developed approach.

Intelligent Event-Based Fuzzy Dynamic Positioning Control of Nonlinear Unmanned Marine Vehicles Under DoS Attack

This article addresses the dynamic positioning control problem of a nonlinear unmanned marine vehicle (UMV) system subject to network communication constraints and deny-of-service (DoS) attack, where the dynamics of UMV are described by a Takagi-Sugeno (T-S) fuzzy system (TSFS). In order to save limited communication resource, a new intelligent event-triggering mechanism is proposed, in which the event triggering threshold is optimized by a Q -learning algorithm. Then, a switched system approach is proposed to deal with the aperiodic DoS attack occurring in the communication channels. With a proper piecewise Lyapunov function, some sufficient conditions for global exponential stability (GES) of the closed-loop nonlinear UMV system are derived, and the corresponding observer and controller gains are designed via solving a set of matrix inequalities. A benchmark nonlinear UMV system is adopted as an example in simulation, and the simulation results validate the effectiveness of the proposed control method.

Model-Free Containment Control of Underactuated Surface Vessels Under Switching Topologies Based on Guiding Vector Fields and Data-Driven Neural Predictors

This article investigates the model-free containment control of multiple underactuated unmanned surface vessels (USVs) subject to unknown kinetic models. A novel cooperative control architecture is presented for achieving a containment formation under switching topologies. Specifically, a path-guided distributed containment motion generator (CMG) is first proposed for generating reference points according to the underlying switching topologies. Next, guiding-vector-field-based guidance laws are designed such that each USV can track its reference point, enabling smooth transitions during topology switching. Finally, data-driven neural predictors by utilizing real-time and historical data are developed for estimating total uncertainties and unknown input gains, simultaneously. Based on the learned knowledge from neural predictors, adaptive kinetic control laws are designed and no prior information on kinetic model parameters is required. By using the proposed method, the fleet is able to converge to the convex hull spanned by multiple virtual leaders under switching topologies regardless of fully unknown kinetic models. Through stability analyses, it is proven that the closed-loop control system is input-to-state stable and the tracking errors are uniformly ultimately bounded. Simulation results verify the effectiveness of the proposed cooperative control architecture for multiple underactuated USVs with fully unknown kinetic models.

H∞ Proportional-Integral State Estimation for T-S Fuzzy Systems Over Randomly Delayed Redundant Channels With Partly Known Probabilities

In this article, we consider the H_∞ proportional-integral (PI) state estimation (SE) problem for discrete-time T-S fuzzy systems subject to transmission delays, external disturbances, and redundant channels. Multiple redundant communication channels are utilized between the sensors and the remote estimator to enhance the reliability of data transmissions. In order to characterize the transmission delays in network-based communication, a family of random variables with partly known probabilities, which are independent and identically distributed, is adopted to describe the random behavior of the transmission delays with the redundant channels. The objective of this work is to put forward a PI state estimator such that the dynamics of the estimation error is exponentially mean-square stable and satisfies the prescribed H_∞ performance index of the disturbance attenuation/rejection. By employing the stochastic analysis approach, the error dynamics of the SE under the proposed state estimator is analyzed and sufficient conditions are obtained to ensure the existence of the required PI state estimator. Furthermore, the desired estimator parameters are derived by solving a nonlinear optimization problem. Finally, two simulation examples are exploited to demonstrate the validity of the proposed SE scheme.

Network-Based Line-of-Sight Path Tracking of Underactuated Unmanned Surface Vehicles With Experiment Results

This article deals with the problem of network-based path-tracking control of an underactuated unmanned surface vehicle subject to model uncertainties and unknown disturbances over a wireless network. A two-level network-based control architecture is proposed, including a local inner loop and a remote outer loop. In the remote outer loop, an event-triggered line-of-sight guidance law is designed to achieve path tracking while reducing the network burden for the remote control at the kinematic level. In the local inner loop, an extended state observer is employed to estimate the unknown disturbances due to the model uncertainties and environmental disturbances. Based on the estimated information from the extended state observer, an event-triggered anti-disturbance control law is developed to reduce the execution rate of actuators at the kinetic level. The stability of the closed-loop path-tracking system is proved based on the input-to-state stability and cascade stability theory. The effectiveness of the proposed network-based method for path tracking of the USV is verified via experiments.

Membership Function Derivatives Transformation Approach for Stability Analysis and Stabilization Control of T-S Fuzzy Systems

In this text, a membership function derivatives (MFDs) extrema-based method is proposed to relax the conservatism both in stability analysis and synthesis problems of Takagi-Sugeno fuzzy systems. By the designed algorithm, the nonpositiveness of the MFDs extrema is conquered. For an open-loop system, based on certain information of the MFs and derivatives, a series of convex stability conditions is derived. Then, an extremum-based construction method is adopted to involve the MF information. For the shape of MFDs, a coordinate transformation algorithm is proposed to involve it in the stability conditions to achieve local stable effects. For a state-feedback control system, conditions guaranteeing the stability and robustness are listed. Finally, simulation examples and comparisons are carried out to clarify the conservatism reduction results of the raised method.

Cross-dimensional formation control of second-order heterogeneous multi-agent systems

This paper is concerned with the cross-dimensional formation control of a second-order multi-dimensional heterogeneous multi-agent system. Agents are first separated into several groups according to their position/velocity vector dimensions. Then the cross-dimensional formation control problem is formulated such that agents in the same group form a time-varying formation in their own dimension and agents in different groups cooperatively move in multiple dimensions. This can make follower agents in different dimensions cooperatively track a leader. Moreover, a cross-dimensional formation protocol is designed based on full or partial information of neighboring agents. For higher-dimensional agents, full information of lower-dimensional neighbors is adopted. For lower-dimensional ones, only partial information of higher-dimensional neighbors is used. Furthermore, a necessary and sufficient condition for the second-order heterogeneous multi-agent system to achieve cross-dimensional formation is provided. Accordingly, a criterion for designing cross-dimensional formation protocol is further derived. Finally, under an undirected graph, a lower-dimensional protocol design criterion is obtained if there is no data exchange between lower- and higher-dimensional followers. The effectiveness of the obtained results is demonstrated through cross-dimensional target enclosing performance analysis for multiple robots and quadrotors.

Augmented safety guarantee-based area keeping control for an underactuated USV with environmental disturbances

This paper proposes an accurate area keeping control method with augmented safety guarantee for an underactuated unmanned surface vessel (UUSV) with environmental disturbances. It can mediate the safety and the stability during the UUSV area keeping control. Firstly, inspired by the notion of control barrier function (CBF) in general manifolds, an augmented safety guarantee is constructed to deal with the nonlinear safety constraints of the area keeping control for the UUSV. Then, the stability constraints for the UUSV area keeping control are formed by involving a control Lyapunov functions (CLF) and a weather optimal control (WOC) method. Finally, by unifying the augmented safety guarantee and the stability constraints, an accurate area keeping controller is designed through a quadratic program (QP), which can consider the safety problem into the optimal area keeping control of the UUSV under environmental disturbances. Comparing the proposed method to the WOC, simulation results validate the feasibility and the superiority of the proposed method.

Data-driven model-free resilient speed control of an autonomous surface vehicle in the presence of actuator anomalies

This paper is concerned with the resilient speed control of an autonomous surface vehicle (ASV) in the presence of actuator anomalies. A data-driven model-free resilient speed control method is presented based on available input and output data only with pulse-width-modulation inputs. Specifically, a data-driven neural predictor is designed to learn the unknown system dynamics of the speed control system of the ASV. Then, a resilient speed control law is designed based on the learned dynamics obtained from the neural network predictor, where a cost function is designed for selecting the optimal duty cycle for the motor. The stability of the data-driven neural predictor is analyzed by using input-state stability (ISS) theory. The advantage of the developed data-driven model-free resilient control method is that the optimal speed control performance can be achieved in the presence of actuator anomalies without any modeling process. Simulation results show the learning ability of the data-driven neural predictor and the effectiveness of the proposed data-driven model-free resilient speed control method for the ASV subject to actuator anomalies.

Event-Triggered Distributed State Estimation for Multiagent Systems Under DoS Attacks

This article investigates the problem of event-triggered distributed state estimation for linear multiagent systems under denial-of-service (DoS) attacks. In contrast to the previous studies where the agents have access to the measurements of their own states, an estimation algorithm is provided based on the measurements relative to the adjacent agents, and the considered DoS attacks jam each channel independently while the attack durations are constrained. To estimate the states and effectively schedule the information transmissions over the network possibly subject to malicious attacks, a prediction-based switching observer scheme with an event-triggered communication strategy is proposed, and the invalidation problem of the event-triggering mechanism caused by the DoS attacks is solved. Based on the decay rates of the Lyapunov function corresponding to different transmission modes, sufficient conditions for the stability of the estimation error dynamics are presented in the presence of DoS attacks. Finally, the effectiveness of the proposed strategy is demonstrated by simulation results.

Robust Cooperative Optimal Sliding-Mode Control for High-Order Nonlinear Systems: Directed Topologies

This article is concerned with the robust cooperative optimal control of nonlinear multiagent systems (MASs) with external disturbances and modeling uncertainties. Using the super-twisting algorithm, a continuous sliding-mode control protocol is presented for high-order nonlinear MASs with multiple inputs. The sliding-mode dynamics is modeled by the Takagi-Sugeno fuzzy approach, and the nominal control protocol that guarantees the robust optimization of the cost function is designed. Directed topologies are allowed using the presented protocol, and many assumptions about topologies are removed. Finally, three numerical examples are reported to demonstrate the effectiveness and improved performance of the presented protocol.

Event-Triggered Dynamic Positioning for Mass-Switched Unmanned Marine Vehicles in Network Environments

This article is concerned with event-triggered dynamic positioning for a mass-switched unmanned marine vehicle (UMV) in network environments. First, a switched dynamic positioning system (DPS) model for a mass-switched marine vehicle is established. The switched DPS model takes into consideration changes in the marine vehicle's mass and the resultant switching of the marine vehicle's parameters. Second, for a mass-switched UMV controlled through a communication network, a novel weighted event-triggering communication scheme considering switching features is proposed. The weighted error data of multiple sampling instants are utilized to avoid a long-time nontriggering phenomenon. The consideration of switching features guarantees the current sampled data to be transmitted if a switch occurs between the last sampling instant and the current sampling instant. Then, under the event-triggering scheme, an asynchronously switched DPS model for the mass-switched UMV is established in network environments. Based on this model, a mode-dependent DPS controller and event generator co-design method are proposed to attenuate the disturbance induced by wind, waves, and ocean currents. The DPS performance analysis demonstrates the effectiveness of the proposed method.

Stability and Tracking Control of Nonlinear Rigid-Body Ship Motions

The stability and tracking controls of a dynamic ship system are studied in this paper, in order to stabilize and track the desired path of a catamaran ship system. The ship sails on a river that is prone to currents, wind, and unknown disturbances from the surrounding environment. Hence, the ship system potentially has stability problems. This paper presents a method for controlling the trajectory of a nonlinear ship system and controlling the ship stabilization. Three steps of the fundamental equations of constrained motion (FECM) with an uncertainty control are used to examine a control force to stabilize and track the ship system. The ship system is considered with six degrees of freedom to describe the stabilization of the ship's rigid-body motion. The results show the efficiency of the control forces from the FECM, with an uncertainty controller applied to the ship system. They guarantee that the conditions of the ship system are satisfactory, and that the ship follows the desired path.

Sampled-Data Consensus of Linear Time-Varying Multiagent Networks With Time-Varying Topologies

The main purpose of this article is to investigate the consensus of linear multiagent networks with time-varying characteristics under sampled-data communications, where the time-varying characteristics include both time-varying topologies and the node's linear time-varying dynamics. By using the decoupling method, we prove that the sampled-data consensus problem of multiagent networks is equal to the stability problem of sampled-data systems. Then, the globally asymptotical consensus is investigated for multiagent networks with time-varying characteristics by virtue of the Lyapunov function method. It should be noted that when the Lyapunov function method is utilized to investigate the stability problem of control systems, it is always assumed that the derivative of the constructed Lyapunov function is not more than zero. This assumption is removed here and as a replacement, the average value of the derivative of the Lyapunov function in a period to be negative is needed.

Distributed Path Following of Multiple Under-Actuated Autonomous Surface Vehicles Based on Data-Driven Neural Predictors via Integral Concurrent Learning

This article addresses the problem of distributed path following of multiple under-actuated autonomous surface vehicles (ASVs) with completely unknown kinetic models. An integrated distributed guidance and learning control architecture is proposed for achieving a time-varying formation. Specifically, a robust distributed guidance law at the kinematic level is developed based on a consensus approach, a path-following mechanism, and an extended state observer. At the kinetic level, a model-free kinetic control law based on data-driven neural predictors via integral concurrent learning is designed such that the kinetic model can be learned by using recorded data. The advantage of the proposed method is two-folds. First, the proposed formation controllers are able to achieve various time-varying formations without using the velocities of neighboring vehicles. Second, the proposed control law is model-free without any parameter information on kinetic models. Simulation results substantiate the effectiveness of the proposed robust distributed guidance and model-free control laws for multiple under-actuated ASVs with fully unknown kinetic models.

H∞ Cluster Formation Control of Networked Multiagent Systems With Stochastic Sampling

This article is concerned with the H_∞ cluster formation control of a multiagent system (MAS) with stochastic sampling in network environments. First, based on a directed communication topology with acyclic partition, agents are separated into several clusters. The agents moving in the same cluster are expected to achieve a desired formation collaboratively, while the agents in different clusters have different formation patterns. Second, the external disturbance for each agent is taken into account. The associated H_∞ cluster formation control, whose performance bound can be obtained by considering both formation information and cluster characteristics, is introduced to measure the disturbance attenuation ability. Third, by casting a stochastic sampled-data-based H_∞ cluster formation problem into an H_∞ control problem of a stochastic system, an H_∞ cluster formation criterion is derived for the MAS with external disturbance. Finally, a lower-dimensional cluster formation criterion is obtained for the disturbance-free MAS. Cluster formation performance analysis testifies the effectiveness of the proposed design methods.

Data-Driven Adaptive Disturbance Observers for Model-Free Trajectory Tracking Control of Maritime Autonomous Surface Ships

In this article, we address the disturbance/ uncertainty estimation of maritime autonomous surface ships (MASSs) with unknown internal dynamics, unknown external disturbances, and unknown input gains. In contrast to existing disturbance observers where some prior knowledge on kinetic model parameters such as the control input gains is available in advance, reduced- and full-order data-driven adaptive disturbance observers (DADOs) are proposed for estimating unknown input gains, as well as total disturbance composed of unknown internal dynamics and external disturbances. An advantage of the proposed DADOs is that the total disturbance and input gains can be simultaneously estimated with guaranteed convergence via data-driven adaption. We apply the proposed full-order DADO for the trajectory tracking control of an MASS without kinetic modeling and present a model-free trajectory tracking control law for the ship based on the DADO and a backstepping technique. We report the simulation results to substantiate the efficacy of the proposed DADO approach to model-free trajectory tracking control of an autonomous surface ship without knowing its dynamics.

Control of Dynamic Positioning System with Disturbance Observer for Autonomous Marine Surface Vessels

The main goal of the research is to design an efficient controller for a dynamic positioning system for autonomous surface ships using the backstepping technique for the case of full-state feedback in the presence of unknown external disturbances. The obtained control commands are distributed to each actuator of the overactuated vessel via unconstrained control allocation. The numerical hydrodynamic model of CyberShip I and the model of environmental disturbances are applied to simulate the operation of the ship control system using the time domain analysis. Simulation studies are presented to illustrate the effectiveness of the proposed controller and its robustness to external disturbances.

Fuzzy Control of Uncertain Positive Markov Jump Fuzzy Systems With Input Constraint

This article considers the stochastic stability of uncertain positive Markov jump fuzzy systems (PMJFSs). The uncertainties consist of partially unknown membership functions and generally uncertain transition rates (GUTRs). In the GUTR model, each transition rate (TR) is completely unknown or only its estimate value is known. The coexistence of the partially unknown membership functions and GUTRs makes the control problem difficult to handle. Based on the T-S fuzzy scheme, a switching-type controller with input constraint which parallels as the structure of the uncertain fuzzy model is applied to ensure the positivity and stochastic stability of such systems. A linear decoupling strategy is proposed to tackle the coupling problems when designing the controller and further derives the desired controller parameters. Finally, two simulation examples are given to illustrate the effectiveness of the proposed method.

Real-Time H∞ Control of Networked Inverted Pendulum Visual Servo Systems

Aiming at the challenges of networked visual servo control systems, which rarely consider network communication duration and image processing computational cost simultaneously, we here propose a novel platform for networked inverted pendulum visual servo control using H_∞ analysis. Unlike most of the existing methods that usually ignore computational costs involved in measuring, actuating, and controlling, we design a novel event-triggered sampling mechanism that applies a new closed-loop strategy to dealing with networked inverted pendulum visual servo systems of multiple time-varying delays and computational errors. Using the Lyapunov stability theory, we prove that the proposed system can achieve stability whilst compromising image-induced computational and network-induced delays and system performance. In the meantime, we use H_∞ disturbance attenuation level γ for evaluating the computational errors, whereas the corresponding H_∞ controller is implemented. Finally, simulation analysis and experimental results demonstrate the proposed system performance in reducing computational errors whilst maintaining system efficiency and robustness.

Distributed Sliding-Mode Tracking Control of Second-Order Nonlinear Multiagent Systems: An Event-Triggered Approach

The event-triggered tracking control problem of second-order multiagent systems in consideration of system nonlinearities is investigated by utilizing the distributed sliding-mode control (SMC) approach. An event-triggered strategy is proposed to decrease the controller sampling frequency and save the network communication resources; the triggering condition is then established for leader-following multiagent systems. In this article, by utilizing the distributed event-based sliding-mode controller, the system state of second-order multiagent systems with system nonlinearities is capable of approaching the integral sliding-mode surface in finite time. A novel integral sliding-mode surface is constructed in this article to guarantee the consensus tracking performance in the existence of system nonlinearities as the state trajectories of second-order integrator systems move on the constructed sliding manifold. By employing the Lyapunov approach, sufficient conditions are deduced to ensure that the consensus tracking performance is obtained for the closed-loop system. Furthermore, it is presented that the triggering scheme can effectively reduce state updates and eliminate the Zeno behavior. A simulation example is provided to testify the validity of our proposed methodology.

Finite-horizon tracking control for a class of stochastic systems subject to input constraints and hybrid cyber attacks

This paper is concerned with the probabilistic-constrained finite-horizon tracking control problem for a class of stochastic systems subject to randomly occurring hybrid cyber attacks and input constraints. Both the randomly occurring denial-of-service (DOS) attacks and randomly occurring deception attacks are considered in an unified framework. The purpose of the current study is to design an observer-based tracking controller such that: over a finite horizon, (1) the variance of the estimation error is less than certain bound at each time step, (2) the probability of the tracking error falling in certain region should larger than a specified value and the region is minimized at each time step. To achieve those purposes, an improved multi-dimensional Chebyshev inequality method is first utilized to convert the probabilistic constraint to a deterministic one. Then an observer-based tracking control method is designed to estimate the state and design the tracking controller, which are realized through solving a set of recursive linear matrix inequalities. By using the proposed algorithm, the observer and tracking controller gains can be solved out in terms of the solution to a convex optimization problem. A simulation example is finally given to demonstrate the effectiveness and applicability of the proposed method.

Guaranteed cost control of hybrid-triggered networked systems with stochastic cyber-attacks

This paper is concerned with guaranteed cost control for a hybrid-triggered networked system subject to stochastic cyber-attacks. First, a hybrid-triggered mechanism including time-triggered mechanism and event-triggered mechanism is proposed to mitigate the pressure of network transmission, in which the switching between two mechanisms satisfies Bernoulli distribution. Second, the closed-loop system subject to the hybrid communication scheme and stochastic cyber-attacks is modelled as a stochastic system with an interval time-varying delay. Then, based on the Lyapunov-Krasovskii functional approach, two theorems are presented for guaranteeing the mean-square stability of the studied system. Finally, the effectiveness of the proposed method is demonstrated through a numerical example.

Observer-Based State Estimation of Discrete-Time Fuzzy Systems Based on a Joint Switching Mechanism for Adjacent Instants

The problem of observer-based state estimation of discrete-time fuzzy systems is investigated by constructing a joint switching mechanism for adjacent instants. Thanks to the usage of both the proposed spatial partitioning method and a set of new free matrix-valued variables, abundant information about size differences of all the normalized fuzzy weighting functions for adjacent instants can be interactively integrated into the fuzzy observer design for the first time. Compared with the recent result from three important aspects (conservatism level, online computational burden, and offline computational burden), three positive results can be obtained: 1) it provides a chance for reducing the conservatism by a large margin; 2) the required online computational burden remains unchanged as referred ones; and 3) as a tradeoff, the required offline computational burden increases to some extent but is still affordable from the view of complexity analysis. Finally, two numerical simulations have been given to validate the effectiveness of our developed theoretical results.

Event-Triggered Consensus of Linear Multiagent Systems With Time-Varying Communication Delays

In this paper, the event-triggered consensus problem of linear multiagent systems with time-varying communication delays is addressed. Different from the existing event-triggered consensus results with communication delays, more general nonuniform time-varying communication delays are considered. To avoid the asynchronous phenomenon caused by nonuniform delays, a novel periodic switching controller is developed. Based on this controller, the resulting consensus error system can be modeled as a periodic switching system. Furthermore, the exponential stability of the consensus error system is derived by utilizing the Lyapunov approach and the dwell-time analysis method. Finally, an illustrative example is presented to demonstrate the effectiveness of the developed method.

Network-Based Modeling and Proportional-Integral Control for Direct-Drive-Wheel Systems in Wireless Network Environments

This paper focuses on the network-based modeling and proportional-integral (PI) control for a continuous-time direct-drive-wheel system in a wireless network environment. The developed system can simplify configuration, reduce bus cables, and realize vehicle height adjustment. A novel network-based model is first established by constructing a PI control system and taking network-induced delays and stochastic packet dropouts into account. By using two different artificial delays to characterize the update of proportional and integral control signals, the network-based PI control system is modeled as a stochastic impulsive system with two input delays and reset equations at updating instants. Then, through involving the reset states and the relationship among two delayed states and the current state in the discontinuous Lyapunov-Krasovskii functional and actively introducing the upper bounds of nonzero network-induced delays, some exponential mean-square stability and H_∞ performance conditions with less conservatism are derived in terms of tractable linear matrix inequalities. An algorithm is presented to determine the minimum H_∞ performance and the corresponding PI control parameters by combining a particle swarm optimization technique with the performance condition. These results can be extended to a network-based PI control of general continuous-time linear systems. A ZigBee-based network simulation platform is finally built and some simulation results are provided to validate the proposed methods.

Output Feedback Stabilization of Networked Control Systems Under a Stochastic Scheduling Protocol

This paper investigates the output feedback stabilization problem for networked control systems under a stochastic scheduling protocol. First, an independent and identically distributed (i.i.d) scheduling protocol is introduced to orchestrate the signal transmission via a communication network. Taking into account the i.i.d protocol, network-induced delay, and packet dropout, a stochastic impulsive delayed model is presented for the studied system. Second, by use of the Lyapunov-Krasovskii functional approach, sufficient conditions for guaranteeing the stability of the studied system in the mean-square sense are obtained in the form of matrix inequalities. Moreover, an optimization algorithm is investigated to obtain the suitable dynamic output feedback controller and optimal i.i.d protocol parameters simultaneously. Finally, two numerical examples are presented to show the validity of the proposed method.

Observer Design of Discrete-Time Fuzzy Systems Based on an Alterable Weights Method

This paper proposes an improvement on observer design of discrete-time fuzzy systems based on an alterable weights method. Different from the recent result, a more effective ranking-based switching mechanism is developed by introducing a bank of alterable weights for the sake of making use of the size difference information of the normalized fuzzy weighting functions more freely than before. Therefore, a positive result can be provided in this paper, that is, less conservative conditions of designing feasible fuzzy observers can be obtained than those existing results, while the computational cost of designing feasible fuzzy observers is even less than the up-to-date one. Finally, two numerical examples are given to show the progressiveness of the proposed method.

Distributed Event-Triggered Estimation Over Sensor Networks: A Survey

An event-triggered mechanism is of great efficiency in reducing unnecessary sensor samplings/transmissions and, thus, resource consumption such as sensor power and network bandwidth, which makes distributed event-triggered estimation a promising resource-aware solution for sensor network-based monitoring systems. This paper provides a survey of recent advances in distributed event-triggered estimation for dynamical systems operating over resource-constrained sensor networks. Local estimates of an unavailable state signal are calculated in a distributed and collaborative fashion based on only invoked sensor data. First, several fundamental issues associated with the design of distributed estimators are discussed in detail, such as estimator structures, communication constraints, and design methods. Second, an emphasis is laid on recent developments of distributed event-triggered estimation that has received considerable attention in the past few years. Then, the principle of an event-triggered mechanism is outlined and recent results in this subject are sorted out in accordance with different event-triggering conditions. Third, applications of distributed event-triggered estimation in practical sensor network-based monitoring systems including distributed grid-connected generation systems and target tracking systems are provided. Finally, several challenging issues worthy of further research are envisioned.

Networked controller and observer design of discrete-time systems with inaccurate model parameters

This paper develops a novel off-policy Q-learning method to find the optimal observer gain and the optimal controller for achieving optimality of network-communication based linear discrete-time systems using only measured data. The primary advantage of this off-policy Q-learning method is that it can work for the linear discrete-time systems with inaccurate system model, unmeasurable system states and network-induced delays. To this end, an optimization problem for networked control systems composed of a plant, a state observer and a Smith predictor is formulated first. The Smith predictor is employed to not only compensate network-induced delays, but also make the separation principle hold, thus the observer and controller can be designed separately. Then, the off-policy Q-learning is implemented for learning the optimal observer gain and the optimal controller combined with the Smith predictor, such that a novel off-policy Q-learning algorithm is derived using only input, output and delayed estimated state of systems, not the inaccurate system matrices. The convergences of the iterative observer gain and the iterative controller gain are rigorously proven. Finally, simulation results are given to verify the effectiveness of the proposed method.

A hybrid transmission scheme for networked control systems

Due to its obvious advantage on the efficient use of resources, the event-triggered mechanism is quickly applied in networked control systems (NCSs). However, when some event-triggered data packets are lost during network transmissions, it will become very challenging to analyse the NCSs since two consecutively event-triggered data are internally correlated and the relation will be destroyed when one of the data get lost. With the consideration, this paper proposes a hybrid communication mechanism for NCSs with data loss. Once a measurement that was event-triggered to be released got lost, the following measurements will be instantly transmitted to the controller. A networked controller is obtained for the NCS to guarantee the uniform ultimate boundedness of the system state. Finally, the validness and merits of the developed approach are illustrated through two well studied examples.

Off-Policy Interleaved Q -Learning: Optimal Control for Affine Nonlinear Discrete-Time Systems

In this paper, a novel off-policy interleaved Q-learning algorithm is presented for solving optimal control problem of affine nonlinear discrete-time (DT) systems, using only the measured data along the system trajectories. Affine nonlinear feature of systems, unknown dynamics, and off-policy learning approach pose tremendous challenges on approximating optimal controllers. To this end, on-policy Q-learning method for optimal control of affine nonlinear DT systems is reviewed first, and its convergence is rigorously proven. The bias of solution to Q-function-based Bellman equation caused by adding probing noises to systems for satisfying persistent excitation is also analyzed when using on-policy Q-learning approach. Then, a behavior control policy is introduced followed by proposing an off-policy Q-learning algorithm. Meanwhile, the convergence of algorithm and no bias of solution to optimal control problem when adding probing noise to systems are investigated. Third, three neural networks run by the interleaved Q-learning approach in the actor-critic framework. Thus, a novel off-policy interleaved Q-learning algorithm is derived, and its convergence is proven. Simulation results are given to verify the effectiveness of the proposed method.

A Fuzzy Comprehensive CS-SVR Model-based health status evaluation of radar

The purpose of Fuzzy Comprehensive CS-SVR Model (FCCS-SVR) is to evaluate and monitor the health status of a radar equipment and then keep its safe operation. Due to reasons such as few samples, slow changes and the nonlinear structure of data of fault monitoring signal, the health status evaluation of a radar system is quite difficult. By establishing the evaluation index system of a radar, the combination of AHP method and Entropy weight method is studied in this paper. In order to evaluate the value of health status, several optimization algorithms including PSO, GA, BA and CS are used for optimizing the parameters of SVR model. Meanwhile, in order to avoid the problem that the system is at the edge of the state, a radar health assessment method based on the combination of Fuzzy Comprehensive Evaluation and Cuckoo Search-Support Vector Regression (CS-SVR), which is named as Fuzzy Comprehensive CS-SVR (FCCS-SVR), is further proposed. The result of case analysis reflects that the state evaluation of the radar system is realized. The system performance analysis shows that the use of FCCS-SVR evaluation method provides a high recognition rate and can accurately assess the health status of the radar system.

Robust exponential point stabilization control of the high-speed underactuated unmanned marine vehicle with model asymmetry

Point stabilization control of a class of asymmetric underactuated high-speed unmanned marine vehicle is discussed, and a robust exponential stabilization control algorithm is proposed based on homogeneous theory, average system theory, and nonlinear backstepping technology. Firstly, point stabilization control problem of a high-speed underactuated unmanned marine vehicle with model asymmetry is formulated, and then global differential homeomorphism transformation is designed, in order to overcome the difficulties caused by unmanned marine vehicle with model asymmetry. Secondly, the control system is transformed into the standard form of homogeneous interference system by output state variable transformation design and input transformation design. A novel interference function is designed, and then difficulties caused by the higher order velocities in damping coefficients are solved, via homogeneous stability design and homogeneity degree analyzing the expansion of the designed new state variables. Thirdly, by introducing the virtual input of backstepping and the average system theory, point stabilization controller for the underactuated high-speed unmanned marine vehicle is proposed based on homogeneous theory, which could achieve global and periodic time-varying robust exponential stability, and then stability of the point stabilization control algorithm is proved by using homogeneous stability theory and average system stability theory. At last, the effectiveness and accuracy of the control algorithm proposed is verified by semi-physical simulation experiment carried out in our laboratory.