Neural-Network-Based Output-Feedback Control Under Round-Robin Scheduling Protocols

Finite-Time Stability Control of Uncertain Nonlinear Systems With Self-Limiting Control Terms

In this brief, we define a self-limiting control term, which has the function of guaranteeing the boundedness of variables. Then, we apply it to a finite-time stability control problem. For nonstrict feedback nonlinear systems, a finite-time adaptive control scheme, which contains a piecewise differentiable function, is proposed. This scheme can eliminate the singularity of derivative of a fractional exponential function. By adding a self-limiting term to the controller and the virtual control law of each subsystem, the boundedness of the overall system state is guaranteed. Then the unknown continuous functions are estimated by neural networks (NNs). The output of the closed-loop system tracks the desired trajectory, and the tracking error converges to a small neighborhood of the equilibrium point in finite time. The theoretical results are illustrated by a simulation example.

Asynchronous Fault Detection for Memristive Neural Networks With Dwell-Time-Based Communication Protocol

This article studies the asynchronous fault detection filter problem for discrete-time memristive neural networks with a stochastic communication protocol (SCP) and denial-of-service attacks. Aiming at alleviating the occurrence of network-induced phenomena, a dwell-time-based SCP is scheduled to coordinate the packet transmission between sensors and filter, whose deterministic switching signal arranges the proper feedback switching information among the homogeneous Markov processes (HMPs) for different scenarios. A variable obeying the Bernoulli distribution is proposed to characterize the randomly occurring denial-of-service attacks, in which the attack rate is uncertain. More specifically, both dwell-time-based SCP and denial-of-service attacks are modeled by means of compensation strategy. In light of the mode mismatches between data transmission and filter, a hidden Markov model (HMM) is adopted to describe the asynchronous fault detection filter. Consequently, sufficient conditions of stochastic stability of memristive neural networks are devised with the assistance of Lyapunov theory. In the end, a numerical example is applied to show the effectiveness of the theoretical method.

Robust MPC for polytopic uncertain systems via a high-rate network with the round-robin scheduling

This article is concerned with the robust model predictive control (RMPC) problem for polytopic uncertain systems under the round-robin (RR) scheduling in the high-rate communication channel. From a set of sensors to the controller, several sensors transmit the data to the remote controller via a shared high-rate communication network, data collision might happen if these sensors start transmissions at the same time. For the sake of preventing data collision in the high-rate communication channel, a communication scheduling known as RR is used to arrange the data transmission order, where only one node with token is allowed to send data at each transmission instant. In accordance with the token-dependent Lyapunov-like approach, the aim of the problem addressed is to design a set of controllers in the framework of RMPC such that the asymptotical stability of the closed-loop system is guaranteed. By taking the effect of the underlying RR scheduling in the high-rate communication channel into consideration, sufficient conditions are obtained by solving a terminal constraint set of an auxiliary optimization problem. In addition, an algorithm including both off-line and online parts is provided to find a sub-optimal solution. Finally, two simulation examples are used to demonstrate the usefulness and effectiveness of the proposed RMPC strategy.

Adaptive Neural State Estimation of Markov Jump Systems Under Scheduling Protocols and Probabilistic Deception Attacks

The neural-network (NN)-based state estimation issue of Markov jump systems (MJSs) subject to communication protocols and deception attacks is addressed in this article. For relieving communication burden and preventing possible data collisions, two types of scheduling protocols, namely: 1) the Round-Robin (RR) protocol and 2) weighted try-once-discard (WTOD) protocol, are applied, respectively, to coordinate the transmission sequence. In addition, considering that the communication channel may suffer from mode-dependent probabilistic deception attacks, a hidden Markov-like model is proposed to characterize the relationship between the malicious signal and system mode. Then, a novel adaptive neural state estimator is presented to reconstruct the system states. By taking the influence of deception attacks into performance analysis, sufficient conditions under two different scheduling protocols are derived, respectively, so as to ensure the ultimately boundedness of the estimate error. In the end, simulation results testify the correctness of the adaptive neural estimator design method proposed in this article.

Composite Observer-Based Optimal Attitude-Tracking Control With Reinforcement Learning for Hypersonic Vehicles

This article proposes an observer-based reinforcement learning (RL) control approach to address the optimal attitude-tracking problem and application for hypersonic vehicles in the reentry phase. Due to the unknown uncertainty and nonlinearity caused by parameter perturbation and external disturbance, accurate model information of hypersonic vehicles in the reentry phase is generally unavailable. For this reason, a novel synchronous estimation is proposed to construct a composite observer for hypersonic vehicles, which consists of a neural-network (NN)-based Luenberger-type observer and a synchronous disturbance observer. This solves the identification problem of nonlinear dynamics in the reference control and realizes the estimation of the system state when unknown nonlinear dynamics and unknown disturbance exist at the same time. By synthesizing the information from the composite observer, an RL tracking controller is developed to solve the optimal attitude-tracking control problem. To improve the convergence performance of critic network weights, concurrent learning is employed to replace the traditional persistent excitation condition with a historical experience replay manner. In addition, this article proves that the weight estimation error is bounded when the learning rate satisfies the given sufficient condition. Finally, the numerical simulation demonstrates the effectiveness and superiority of the proposed approaches to attitude-tracking control systems for hypersonic vehicles.

A Time-Delay Feedback Neural Network for Discriminating Small, Fast-Moving Targets in Complex Dynamic Environments

Discriminating small moving objects within complex visual environments is a significant challenge for autonomous micro-robots that are generally limited in computational power. By exploiting their highly evolved visual systems, flying insects can effectively detect mates and track prey during rapid pursuits, even though the small targets equate to only a few pixels in their visual field. The high degree of sensitivity to small target movement is supported by a class of specialized neurons called small target motion detectors (STMDs). Existing STMD-based computational models normally comprise four sequentially arranged neural layers interconnected via feedforward loops to extract information on small target motion from raw visual inputs. However, feedback, another important regulatory circuit for motion perception, has not been investigated in the STMD pathway and its functional roles for small target motion detection are not clear. In this article, we propose an STMD-based neural network with feedback connection (feedback STMD), where the network output is temporally delayed, then fed back to the lower layers to mediate neural responses. We compare the properties of the model with and without the time-delay feedback loop and find that it shows a preference for high-velocity objects. Extensive experiments suggest that the feedback STMD achieves superior detection performance for fast-moving small targets, while significantly suppressing background false positive movements which display lower velocities. The proposed feedback model provides an effective solution in robotic visual systems for detecting fast-moving small targets that are always salient and potentially threatening.

Output-Feedback Control for Fuzzy Singularly Perturbed Systems: A Nonhomogeneous Stochastic Communication Protocol Approach

In this study, the output-feedback control (OFC) strategy design problem is explored for a type of Takagi-Sugeno fuzzy singular perturbed system. To alleviate the communication load and improve the reliability of signal transmission, a novel stochastic communication protocol (SCP) is proposed. In particular, the SCP is scheduled based on a nonhomogeneous Markov chain, where the time-varying transition probability matrix is characterized by a polytope-structure-based set. Different from the existing homogeneous Markov SCP, a nonhomogeneous Markov SCP depicts the data transmission in a more reasonable manner. To detect the actual network mode, a hidden Markov process observer is addressed. By virtue of the hidden Markov model with partly unidentified detection probabilities, an asynchronous OFC law is formulated. By establishing a novel Lyapunov-Krasovskii functional with a singular perturbation parameter and a nonhomogeneous Markov process, a sufficient condition is exploited to guarantee the stochastic stability of the resulting system, and the solution for the asynchronous controller is portrayed. Eventually, the validity of the attained methodology is expressed through a practical example.

Security control for nonlinear systems under quantization and Round-Robin protocol subject to deception attacks

This paper is concerned with the security control problem for the nonlinear systems with the effects of quantization, communication protocol and deception attacks based on the Takagi-Sugeno (T-S) fuzzy model. The measurement output and control input signals are quantized by dynamic quantizers simultaneously, and the quantized signals are transmitted through the communication channels scheduled by Round-Robin (RR) protocol. Moreover, two Bernoulli processes are utilized to characterize the deception attacks occurring on the different channels respectively. The mode-dependent observer-based controller and dynamic quantizers are designed such that the security in probability of the closed-loop system with the prescribed quadratic cost index can be guaranteed. Then, sufficient design conditions are obtained for the controller gains and quantizers' parameters based on the linear matrix inequalities (LMIs) approach. In the end, a numerical example is carried out to demonstrate the validity of the proposed method.

Transparent Digital Twin for Output Control Using Belief Rule Base

A transparent digital twin (DT) is designed for output control using the belief rule base (BRB), namely, DT-BRB. The goal of the transparent DT-BRB is not only to model the complex relationships between the system inputs and output but also to conduct output control by identifying and optimizing the key parameters in the model inputs. The proposed DT-BRB approach is composed of three major steps. First, BRB is adopted to model the relationships between the inputs and output of the physical system. Second, an analytical procedure is proposed to identify only the key parameters in the system inputs with the highest contribution to the output. Being consistent with the inferencing, integration, and unification procedures of BRB, there are also three parts in the contribution calculation in this step. Finally, the data-driven optimization is performed to control the system output. A practical case study on the Wuhan Metro System is conducted for reducing the building tilt rate (BTR) in tunnel construction. By comparing the results following different standards, the 80% contribution standard is proved to have the highest marginal contribution that identifies only 43.5% parameters as the key parameters but can reduce the BTR by 73.73%. Moreover, it is also observed that the proposed DT-BRB approach is so effective that iterative optimizations are not necessarily needed.

Nonfragile H∞ Synchronization of BAM Inertial Neural Networks Subject to Persistent Dwell-Time Switching Regularity

This article concentrates on the synchronization of discrete-time persistent dwell-time (PDT) switched bidirectional associative memory inertial neural networks with time-varying delays. Through the use of the switched system theory related to the PDT, the convex optimization technique together with some straightforward decoupling methods, an appropriate mode-dependent controller with nonfragility is developed to acclimatize itself to some practical circumstances. Simultaneously, sufficient conditions of ensuring the H_∞ performance and exponential stability for the resulting switched synchronization error system are derived. Finally, a numerical example is utilized to show the validity of the model constructed and the influence of the PDT on the H_∞ performance. In addition, an image encryption example is employed to show the potential application prospect of the investigated system.

Trajectory Consensus for Coordination of Multiple Curvature-Bounded Vehicles

This article addresses the trajectory consensus problem of coordinating the trajectories of vehicles at multiple future time points. The objective is the consensus of the geometry of the vehicles' planned trajectories. The geometric feature of trajectories is parameterized by a set of trajectory states defined as required lengths along the trajectory to reduce the distance to its ending point to specific values. To solve this special consensus problem involving coupled state variables, the conventional consensus model is extended by attaching it to a mapping from the state variables to the trajectory's geometry. This mapping is established using a homotopic structure that creates a compact and efficient form for the mapping. The geometry of the homotopic structure is based on the shapes of its envelopes, and the elements in the structure are derived from their deformation. Through a homotopic search in the structure, an asymptotic consensus of trajectory states is achieved. Simulation results show the proposed coupled state consensus method can achieve better performance on the consensus of multiple vehicles than the conventional isolated state consensus method.

Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network

The Internet of Medical Things (IoMT) workflow applications have been rapidly growing in practice. These internet-based applications can run on the distributed healthcare sensing system, which combines mobile computing, edge computing and cloud computing. Offloading and scheduling are the required methods in the distributed network. However, a security issue exists and it is hard to run different types of tasks (e.g., security, delay-sensitive, and delay-tolerant tasks) of IoMT applications on heterogeneous computing nodes. This work proposes a new healthcare architecture for workflow applications based on heterogeneous computing nodes layers: an application layer, management layer, and resource layer. The goal is to minimize the makespan of all applications. Based on these layers, the work proposes a secure offloading-efficient task scheduling (SEOS) algorithm framework, which includes the deadline division method, task sequencing rules, homomorphic security scheme, initial scheduling, and the variable neighbourhood searching method. The performance evaluation results show that the proposed plans outperform all existing baseline approaches for healthcare applications in terms of makespan.

Fault-Tolerant Optimal Control for Discrete-Time Nonlinear System Subjected to Input Saturation: A Dynamic Event-Triggered Approach

This paper investigates the dynamic event-triggered fault-tolerant optimal control strategy for a class of output feedback nonlinear discrete-time systems subject to actuator faults and input saturations. To save the communication resources between the sensor and the controller, the so-called dynamic event-triggered mechanism is adopted to schedule the measurement signal. A neural network-based observer is first designed to provide both the system states and fault information. Then, with consideration of the actuator saturation phenomenon, the adaptive dynamic programming (ADP) algorithm is designed based on the estimates provided by the observer. To reduce the computational burden, the optimal control strategy is implemented via the single network adaptive critic architecture. The sufficient conditions are provided to guarantee the boundedness of the overall closed-loop systems. Finally, the numerical simulations on a two-link flexible manipulator system are provided to verify the validity of the proposed control strategy.

SQM-LRU: A Harmony Dual-Queue Management Algorithm to Control Non-Responsive LTF Flow and Achieve Service Differentiation

The increase in network applications diversity and different service quality requirements lead to service differentiation, making it more important than ever. In Wide Area Network (WAN), the non-responsive Long-Term Fast (LTF) flows are the main contributors to network congestion. Therefore, detecting and suppressing non-responsive LTF flows represent one of the key points for providing data transmission with controllable delay and service differentiation. However, the existing single-queue management algorithms are designed to serve only a small number of applications with similar requirements (low latency, high throughput, etc.). The lack of mechanisms to distinguish different traffic makes it difficult to implement differentiated services. This paper proposes an active queue management scheme, namely, SQM-LRU, which realizes service differentiation based on Shadow Queue (SQ) and improved Least-Recently-Used (LRU) strategy. The algorithm consists of three essential components: First, the flow detection module is based on the SQ and improved LRU. This module is used to detect non-responsive LTF flows. Second, different flows will be put into corresponding high or low priority sub-queues depending on the flow detection results. Third, the dual-queue adopts CoDel and RED, respectively, to manage packets. SQM-LRU intends to satisfy the stringent delay requirements of responsive flow while maximizing the throughput of non-responsive LTF flow. Our simulation results show that SQM-LRU outperforms traditional solutions with significant improvement in flow detection and reduces the delay, jitter, and Flow Completion Time (FCT) of responsive flow. As a result, it reduced the FCT by up to 50% and attained 95% of the link utilization. Additionally, the low overhead and the operations incur O(1) cost per packet, making it practical for the real network.

Decentralized Event-Triggered Control for a Class of Nonlinear-Interconnected Systems Using Reinforcement Learning

In this article, we propose a novel decentralized event-triggered control (ETC) scheme for a class of continuous-time nonlinear systems with matched interconnections. The present interconnected systems differ from most of the existing interconnected plants in that their equilibrium points are no longer assumed to be zero. Initially, we establish a theorem to indicate that the decentralized ETC law for the overall system can be represented by an array of optimal ETC laws for nominal subsystems. Then, to obtain these optimal ETC laws, we develop a reinforcement learning (RL)-based method to solve the Hamilton-Jacobi-Bellman equations arising in the discounted-cost optimal ETC problems of the nominal subsystems. Meanwhile, we only use critic networks to implement the RL-based approach and tune the critic network weight vectors by using the gradient descent method and the concurrent learning technique together. With the proposed weight vectors tuning rule, we are able to not only relax the persistence of the excitation condition but also ensure the critic network weight vectors to be uniformly ultimately bounded. Moreover, by utilizing the Lyapunov method, we prove that the obtained decentralized ETC law can force the entire system to be stable in the sense of uniform ultimate boundedness. Finally, we validate the proposed decentralized ETC strategy through simulations of the nonlinear-interconnected systems derived from two inverted pendulums connected via a spring.

An Analysis on Optimal Attack Schedule Based on Channel Hopping Scheme in Cyber-Physical Systems

In this paper, we investigate the issue of security on the remote state estimation in cyber-physical systems (CPSs), where a wireless sensor utilizes the channel hopping scheme to transmit the data to the remote estimator over multiple channels in the presence of periodic denial-of-service attacks. Assume that the jammer can interfere with a subset of channels at each attack time in active period. For an energy-constraint jammer, the problem of how to select the number of channels at each attack time to maximally deteriorate the CPS performance is investigated. Based on the index of average estimation error, we introduce two different attack strategies, which include selecting identical number of channels and unequal number of channels at each attack time, and further show theoretically that the attack effect by selecting unequal number of channels is better than that of selecting identical number of channels. By formulating the problem of selecting the number of channels as integer programming problems, we present the corresponding algorithm to approximate the optimal attack schedule for both cases. The numerical results are presented to validate the theoretical results and the effectiveness of the proposed algorithms.

Efficient Representation and Approximation of Model Predictive Control Laws via Deep Learning

We show that artificial neural networks with rectifier units as activation functions can exactly represent the piecewise affine function that results from the formulation of model predictive control (MPC) of linear time-invariant systems. The choice of deep neural networks is particularly interesting as they can represent exponentially many more affine regions compared to networks with only one hidden layer. We provide theoretical bounds on the minimum number of hidden layers and neurons per layer that a neural network should have to exactly represent a given MPC law. The proposed approach has a strong potential as an approximation method of predictive control laws, leading to a better approximation quality and significantly smaller memory requirements than previous approaches, as we illustrate via simulation examples. We also suggest different alternatives to correct or quantify the approximation error. Since the online evaluation of neural networks is extremely simple, the approximated controllers can be deployed on low-power embedded devices with small storage capacity, enabling the implementation of advanced decision-making strategies for complex cyber-physical systems with limited computing capabilities.

A Scalable Algorithm for Event-Triggered State Estimation With Unknown Parameters and Switching Topologies Over Sensor Networks

An event-triggered distributed state estimation problem is investigated for a class of discrete-time nonlinear stochastic systems with unknown parameters over sensor networks (SNs) subject to switched topologies. An event-triggered communication strategy is employed to govern the information broadcast and reduce the unnecessary resource consumption. Based on the adopted communication strategy, a distributed state estimator is designed to estimate the plant states and also identify the unknown parameters. In the framework of input-to-state stability, sufficient conditions with an average dwell time are established to ensure the boundedness of estimation errors in mean-square sense. In addition, the gains of the designed estimators are dependent on the solution of a set of matrix inequalities whose dimensions are unrelated to the scale of underlying SNs, thereby fulfill the scalability requirement. Finally, an illustrative simulation is utilized to verify the feasibility of the proposed design scheme.

Neural-Network-Based Consensus Control for Multiagent Systems With Input Constraints: The Event-Triggered Case

In this paper, the neural-network (NN)-based consensus control problem is investigated for a class of discrete-time nonlinear multiagent systems (MASs) with a leader subject to input constraints. Relative measurements related to local tracking errors are collected via some smart sensors. A local nonquadratic cost function is first introduced to evaluate the control performance with input constraints. Then, in view of the relative measurements, an NN-based observer under the event-triggered mechanism is designed to reconstruct the dynamics of the local tracking errors, where the adopted event-triggered condition has a time-dependent threshold and the weight of NNs is updated via a new adaptive tuning law catering to the employed event-triggered mechanism. Furthermore, an ideal control policy is developed for the addressed consensus control problem while minimizing the prescribed local nonquadratic cost function. Moreover, an actor-critic NN scheme with online learning is employed to realize the obtained control policy, where the critic NN is a three-layer structure with powerful approximation capability. Through extensive mathematical analysis, the consensus condition is established for the underlying MAS, and the boundedness of the estimated errors is proven for actor and critic NN weights. In addition, the effect from the adopted event-triggered mechanism on the local cost is thoroughly discussed, and the upper bound of the corresponding increment is derived in comparison with time-triggered cases. Finally, a simulation example is utilized to illustrate the usefulness of the proposed controller design scheme.

Distributed Secure Cooperative Control Under Denial-of-Service Attacks From Multiple Adversaries

This paper develops a fully distributed framework to investigate the cooperative behavior of multiagent systems in the presence of distributed denial-of-service (DoS) attacks launched by multiple adversaries. In such an insecure network environment, two kinds of communication schemes, that is, sample-data and event-triggered communication schemes, are discussed. Then, a fully distributed control protocol with strong robustness and high scalability is well designed. This protocol guarantees asymptotic consensus against distributed DoS attacks. In this paper, "fully" emphasizes that the eigenvalue information of the Laplacian matrix is not required in the design of both the control protocol and event conditions. For the event-triggered case, two effective dynamical event-triggered schemes are proposed, which are independent of any global information. Such event-triggered schemes do not exhibit Zeno behavior even in the insecure environment. Finally, a simulation example is provided to verify the effectiveness of theoretical analysis.

Necessary and Sufficient Conditions for Consensus of Continuous-Time Multiagent Systems With Markovian Switching Topologies and Communication Noises

This paper investigates the mean square consensus problem for continuous-time multiagent systems with randomly switching topologies and noises. The switching is governed by a time-homogeneous Markov process, and each topology corresponds to a state of the process. Meanwhile, the communication noises are also considered for practical applications. We introduce a time-varying gain which can reduce the effect of communication noises. It is shown that the effect of Markovian switching topologies mainly depends on the union of topologies associated with the positive recurrent states of the Markov process. Then, necessary and sufficient conditions can be obtained under a control protocol with time-varying gain. Moreover, we extend our result to the cases where the topological structure is semi-Markovian switching and the elements of transition rate matrix are partly unknown. Finally, we give an example to illustrate the validity of our results.

Torus-Event-Based Fault Diagnosis for Stochastic Multirate Time-Varying Systems With Constrained Fault

In this paper, the torus-event-based fault detection and isolation (FDI) problem is investigated for a class of time-varying multirate systems. An ellipsoidal constraint is first adopted to describe the fault in a more practical pattern, and a novel torus-event-triggering scheme is proposed to improve the unilateral triggering mechanism. The aim is to design the torus-event-based fault detection filter and fault isolation estimators such that both the prescribed variance constraint on the estimation error and the desired H_∞ performance on the disturbance are guaranteed over the finite horizon. Especially, the residual evaluation function is employed to detect the fault, and the residual matching function is developed to isolate the fault. Furthermore, three optimization problems are provided to seek separately the minimal parameters on the H_∞ performance level, the upper bound of the estimation error variance, and the triggering torus. Finally, two simulation examples are utilized to show the effectiveness of the FDI scheme proposed in this paper.

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.

Distributed Set-Membership Filtering for Multirate Systems Under the Round-Robin Scheduling Over Sensor Networks

In this paper, the distributed set-membership filtering problem is dealt with for a class of time-varying multirate systems in sensor networks with the communication protocol. For relieving the communication burden, the round-Robin (RR) protocol is exploited to orchestrate the transmission order, under which each sensor node only broadcasts partial information to both the corresponding local filter and its neighboring nodes. In order to meet the practical transmission requirements as well as reduce communication cost, the multirate strategy is proposed to govern the sampling/update rate of the plant, the sensors, and the filters. By means of the lifting technique, the augmented filtering error system is established with a unified sampling rate. The main purpose of the addressed filtering problem is to design a set of distributed filters such that, in the simultaneous presence of the RR transmission protocol, the multirate mechanism, and the bounded noises, there exists a certain ellipsoid that includes all possible error states at each time instant. Then, the desired distributed filter gains are obtained by minimizing such an ellipsoid in the sense of the minimum trace of the weighted matrix. The proposed resource-efficient filtering algorithm is of a recursive form, thereby facilitating the online implementation. A numerical simulation example is given to demonstrate the effectiveness of the proposed protocol-based distributed filter design method.

H∞ Containment Control of Multiagent Systems Under Event-Triggered Communication Scheduling: The Finite-Horizon Case

This paper investigates the finite-horizon H_∞ containment control issue for a general discrete time-varying linear multiagent systems with multileaders. All followers in such a system are driven into a convex hull spanned by multiple leaders, which can be transformed into a problem of tracking a virtual trajectory generated by these leaders. For this purpose, a local state observer is put forward to estimate the state of each agent itself. Then, the estimated state is transmitted to corresponding neighbors governing by an innovation-based event-triggered scheduling protocol. The purpose of the addressed problem is to design both an event-based distributed controller and a state observer such that a prescribed H_∞ containment index can be achieved over a given finite horizon. First, with the help of the completing the square method, a sufficient condition is established to ensure the desired H_∞ containment performance. Then, by resort to a novel nominal energy cost index combined with Moore-Penrose pseudoinverse method, the desired controller and observer parameters are obtained by solving two coupled backward recursive Riccati difference equations. Two positive scalars in proposed nominal energy cost index provide a tradeoff among the controlled tracking errors, the energy of transformed control inputs, and the precision of estimated states. Finally, a simulation example is given to illustrate the usefulness of the proposed theoretical results.

Event-Triggered Recursive Filtering for Shift-Varying Linear Repetitive Processes

This paper addresses the recursive filtering problem for shift-varying linear repetitive processes (LRPs) with limited network resources. To reduce the resource occupancy, a novel event-triggered strategy is proposed where the concern is to broadcast those necessary measurements to update the innovation information only when certain events appear. The primary goal of this paper is to design a recursive filter rendering that, under the event-triggered communication mechanism, an upper bound (UB) on the filtering error variance is ensured and then optimized by properly determining the filter gains. As a distinct kind of 2-D systems, the LRPs are cast into a general Fornasini-Marchesini model by using the lifting technique. A new definition of the triggering-shift sequence is introduced and an event-triggered rule is then constructed for the transformed system. With the aid of mathematical induction, the filtering error variance is guaranteed to have a UB which is subsequently optimized with appropriate filter parameters via solving two series of Riccati-like difference equations. Theoretical analysis further reveals the monotonicity of the filtering performance with regard to the event-triggering threshold. Finally, an illustrative simulation is given to show the feasibility of the designed filtering scheme.

Model Predictive Control via Output Feedback Neural Network for Improved Multi-Window Greenhouse Ventilation Control

Maintaining environmental conditions for proper plant growth in greenhouses requires managing a variety of factors; ventilation is particularly important because inside temperatures can rise rapidly in warm climates. The structure of the window installed in a greenhouse is very diverse, and it is difficult to identify the characteristics that affect the temperature inside the greenhouse when multiple windows are driven, respectively. In this study, a new ventilation control logic using an output feedback neural-network (OFNN) prediction and optimization method was developed, and this approach was tested in multi-window greenhouses used for strawberry production. The developed prediction model used 15 inputs and achieved a highly accurate performance (R² of 0.94). In addition, the method using an algorithm based on an OFNN was proposed for optimizing considered six window-opening behavior. Three case studies confirmed the optimization performance of OFNN in the nonlinear model and verified the performance through simulations. Finally, a control system based on this logic was used in a field experiment for six days by comparing two greenhouses driven by conventional control logic and the developed control logic; a comparison of the results showed RMSEs of 3.01 °C and 2.45 °C, respectively. It confirmed the improved control performance in comparison to a conventional ventilation control system.

l2-l∞ state estimation for delayed artificial neural networks under high-rate communication channels with Round-Robin protocol

In this paper, the l₂-l_∞ state estimation problem is addressed for a class of delayed artificial neural networks under high-rate communication channels with Round-Robin (RR) protocol. To estimate the state of the artificial neural networks, numerous sensors are deployed to measure the artificial neural networks. The sensors communicate with the remote state estimator through a shared high-rate communication channel. In the high-rate communication channel, the RR protocol is utilized to schedule the transmission sequence of the numerous sensors. The aim of this paper is to design an estimator such that, under the high-rate communication channel and the RR protocol, the exponential stability of the estimation error dynamics as well as the l₂-l_∞ performance constraint are ensured. First, sufficient conditions are given which guarantee the existence of the desired l₂-l_∞ state estimator. Then, the estimator gains are obtained by solving two sets of matrix inequalities. Finally, numerical examples are provided to verify the effectiveness of the developed l₂-l_∞ state estimation scheme.