Predictor-Based Neural Dynamic Surface Control for Uncertain Nonlinear Systems in Strict-Feedback Form

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.

Fast Finite-Time Observer-Based Event-Triggered Consensus Control for Uncertain Nonlinear Multiagent Systems with Full-State Constraints

This article studies a class of uncertain nonlinear multiagent systems (MASs) with state restrictions. RBFNNs, or radial basis function neural networks, are utilized to estimate the uncertainty of the system. To approximate the unknown states and disturbances, the state observer and disturbance observer are proposed to resolve those issues. Moreover, a fast finite-time consensus control technique is suggested in order to accomplish fast finite-time stability without going against the full-state requirements. It is demonstrated that every signal could be stable and boundless, and an event-triggered controller is considered for the saving of resources. Ultimately, the simulated example demonstrates the validity of the developed approach.

LPVS guidance and adaptive event-triggered control for an underactuated surface vessel with the prevention of obstacle's vicious maneuvering

This paper investigates the adaptive neural event-triggered control for an underactuated surface vessel (USV), considering constraints of the obstacle's vicious maneuvering and the limited communication channel. In the algorithm, a novel logical phantom virtual ship (LPVS) guidance principle is developed to generate the global path following reference and the obstacle avoidance order in the simulation results, where the corresponding operation comply to the suggestion in international regulations for prevention collision at sea (COLREGs). The improved design of velocity obstacle (VO) method can guarantee its predictive capability to prevent the obstacle's vicious maneuvering. As for the control module, the adaptive event-triggered control algorithm is proposed by employing the robust neural damping technique and the input event-triggered mechanism. And the derived adaptive law can effectively solve perturbations from the gain uncertainty and the external disturbances. Through the theoretical analysis, all signals of the closed-loop control system are with the semi-globally uniform ultimate bounded (SGUUB) stability. The simulation experiments have been presented to verify the obstacle avoidance effectiveness and the burden-some superiority of the algorithm.

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.

Observer-Based Fixed-Time Adaptive Fuzzy Consensus DSC for Nonlinear Multiagent Systems

This article studies the output-feedback fixed-time fuzzy consensus control problem for nonlinear multiagent systems (MASs) under the directed communication topologies. Since the controlled systems contain the unmeasurable states and unknown dynamics, the unmeasurable states are reconstructed via linear state observers, and fuzzy logic systems are utilized to identify the unknown internal dynamics. By constructing the integral type Lyapunov function, a fixed-time adaptive fuzzy consensus control scheme is developed by introducing the nonlinear filter technique into the backstepping recursive technique adaptive control algorithm. The presented consensus control method can not only guarantee the controlled system is semi-global practical fixed-time stable (SGPFTS), but also avoid the singular problem in existing backstepping recursive control design methods. Finally, an application of unmanned surface vehicles is provided to verify the effectiveness of the presented fixed-time fuzzy consensus control method.

Deterministic learning-based neural control for output-constrained strict-feedback nonlinear systems

This paper studies learning from adaptive neural control of output-constrained strict-feedback uncertain nonlinear systems. To overcome the constraint restriction and achieve learning from the closed-loop control process, there are several significant steps. Firstly, a state transformation is introduced to convert the original constrained system output into an unconstrained one. Then an equivalent n-order affine nonlinear system is constructed based on the transformed unconstrained output state in norm form by the system transformation method. By combining dynamic surface control (DSC) technique, an adaptive neural control scheme is proposed for the transformed system. Then all closed-loop signals are uniformly ultimately bounded and the system output tracks the expected trajectory well with satisfying the constraint requirement. Secondly, the partial persistent excitation condition of the radial basis function neural network (RBF NN) could be verified to achieve. Therefore, the uncertain dynamics can be precisely approximated by RBF NN. Subsequently, the learning ability of RBF NN is achieved, and the knowledge acquired from the neural control process is stored in the form of constant neural networks (NNs). By reutilizing the knowledge, a novel learning controller is established to improve the control performance when facing the similar or same control task. The proposed learning control (LC) scheme can avoid repeating the online adaptation of neural weight estimates, which saves computing resources and improves transient performance. Meanwhile, the LC method significantly raises the tracking accuracy and the speed of error convergence while satisfying of the constraint condition simultaneously. Simulation studies demonstrate the efficiency of this proposed control scheme.

Adaptive Control of Uncertain Nonlinear Systems via Event-Triggered Communication and NN Learning

This article concentrates on adaptive tracking control of strict-feedback uncertain nonlinear systems with an event-based learning scheme. A novel neural network (NN) learning law is proposed to design the adaptive control scheme. The NN weights information driven by the prediction-error-based control process is intermittently transmitted in the event-triggered context to the NN learning law mainly for signal tracking. The online stored sampled data of NN driven by the tracking error are utilized in the event context to update the learning law. With the adaptive control and NN learning law updated via the event-triggered communication, the improvements of NN learning capability, tracking performance, and system computing resource saving are guaranteed. In addition, it is proved that the minimum time interval for triggering errors of the two types of events is bounded and the Zeno behavior is strictly excluded. Finally, simulation results illustrate the effectiveness and good performance of the proposed control method.

Online recorded data-based finite-time composite neural trajectory tracking control for underactuated MSVs

This paper presents an online recorded data-based composite neural finite-time control scheme for underactuated marine surface vessels (MSVs) subject to uncertain dynamics and time-varying external disturbances. The underactuation problem of the MSVs was solved by introducing the line-of-sight (LOS) method. The uncertain dynamics of MSVs are approximated by the composite neural networks (NNs). A modified prediction error signal is designed by virtue of online recorded data. The weight updating law of NN is driven by both tracking error and prediction error, introducing additional correction information to the weights of NN, thus improving the learning ability of the NN. Furthermore, disturbance observers can be devised to estimate the compound disturbances consisting of the approximation errors of NNs and external disturbances. Moreover, the smooth function is inserted into the design of the control scheme, and the finite-time composite neural trajectory tracking control of MSVs is achieved. The stability of the MSVs trajectory tracking closed-loop control system is guaranteed rigorously by the Lyapunov approach, and the tracking error will converge to the set of residuals around zero within a finite time. The simulation tests on an MSV verify the effectiveness of the proposed control scheme.

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.

Secure predictor-based neural dynamic surface control of nonlinear cyber-physical systems against sensor and actuator attacks

This paper addresses a secure predictor-based neural dynamic surface control (SPNDSC) issue for a cyber-physical system in a nontriangular form suffering from both sensor and actuator deception attacks. To avoid the algebraic loop problem, only partial states are employed as input vectors of neural networks (NNs) for approximating unknown dynamics, and compensation terms are further developed to offset approximation errors from NNs. With introduction of nonlinear gain functions and attack compensators, adverse effects of an intelligent adversary are alleviated effectively. Furthermore, we present stability analysis and prove the ultimate boundedness of all signals in the closed-loop system. The effectiveness of the proposed control strategy is illustrated by two examples.

Full-state constrained neural control and learning for the nonholonomic wheeled mobile robot with unknown dynamics

The adaptive learning and control are proposed for the full-state(FS) constrained NWMR system with external destabilization. First, the constrained state is reformulated as the unconstrained state. Then, approximating the unknown dynamics in the closed-loop (CL) system is conducted via radial basis function (RBF) NN. Also, a sliding term is designed to deal with the external destabilization and the neural network training error. The derived adaptive neural controller can realize the asymptotic stability of a robot system without violating FS constraints. Moreover, the neural weights are converged so that the unknown dynamics are expressed by the constant weights in the CL system. It is also applicable to other similar control tasks. Lastly, the proposed algorithm is simulated and validated.

Predictor-based practical fixed-time adaptive sliding mode formation control of a time-varying delayed uncertain fully-actuated surface vessel using RBFNN

This paper focuses on fixed-time formation control (FTFC) of a fully-actuated surface vessel (FASV) considering complex unknowns, including fully unknown dynamics and disturbances, input saturation and time-varying delays. First, using prediction idea to address time delay, a novel state predictor (SP) strategy combining with state transformation (ST) technique is devised for each FASV to predict the evolution of system states such that fixed-time stability can be ensured while solving the delay problem. Besides, the uncertainties in the transformed system are attentively considered. In addition, aiming to distinctly identify complex unknowns, predictor-based neural network is injected into the foregoing delay processing method. Finally, using time base generator (TBG), a new adaptive terminal sliding mode (ATSM) is incorporated into FTFC strategy which in turn contributes to decreasing control inputs and acquiring smooth convergence process. Simulation results and comparisons are thoroughly provided to testify the effectiveness and superiority of the designed FTFC scheme.

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.

Predictor-Based Neural Dynamic Surface Control for Bipartite Tracking of a Class of Nonlinear Multiagent Systems

This article is concerned with bipartite tracking for a class of nonlinear multiagent systems under a signed directed graph, where the followers are with unknown virtual control gains. In the predictor-based neural dynamic surface control (NDSC) framework, a bipartite tracking control strategy is proposed by the introduction of predictors and the minimal number of learning parameters (MNLPs) technology along with the graph theory. Different from the traditional NDSC, the predictor-based NDSC utilizes prediction errors to update the neural network for improving system transient performance. The MNLPs technology is employed to avoid the problem of "explosion of learning parameters". It is proved that all closed-loop signals steered by the proposed control strategy are bounded, and the system achieves bipartite consensus. Simulation results verify the efficiency and effectiveness of the strategy.

System Transformation-Based Neural Control for Full-State-Constrained Pure-Feedback Systems via Disturbance Observer

In this article, a novel disturbance observer-based adaptive neural control (ANC) scheme is proposed for full-state-constrained pure-feedback nonlinear systems using a new system transformation method. A nonlinear transformation function in a uniformed design framework is constructed to convert the original states with constrained bounds into the ones without any constraints. By combining an auxiliary first-order filter, an augmented nonlinear system without any state constraint is derived to circumvent the difficulty of the controller design caused by the nonaffine input signal. Based on the augmented nonlinear system, a nonlinear disturbance observer (NDO) is designed to enhance the disturbance rejection ability. Subsequently, the NDO-based ANC scheme is presented by combining the second-order filters with backstepping. The proposed scheme confines all states within the predefined bounds, eliminates the condition on both the known sign and bounds of control gains, improves the robustness of the closed-loop system, and alleviates the computational burden. Two simulation examples are performed to show the validity of the presented scheme.

Composite learning tracking control for underactuated marine surface vessels with output constraints

In this paper, a composite learning control scheme was proposed for underactuated marine surface vessels (MSVs) subject to unknown dynamics, time-varying external disturbances and output constraints. Based on the line-of-sight (LOS) approach, the underactuation problem of the MSVs was addressed. To deal with the problem of output constraint, the barrier Lyapunov function-based method was utilized to ensure that the output error will never violate the constraint. The composite neural networks (NNs) are employed to approximate unknown dynamics. The prediction errors can be obtained using the serial-parallel estimation model (SPEM). Both the prediction errors and the tracking errors were employed to construct the NN weight updating. Using approximation information, the disturbance observers were designed to estimate unknown time-varying disturbances. The stability analysis via the Lyapunov approach indicates that all signals of unmanned marine surface vessels are uniformly ultimate boundedness. The simulation results verify the effectiveness of the proposed control scheme.

Adaptive Neural Network Finite-Time Dynamic Surface Control for Nonlinear Systems

This article addresses the problem of finite-time neural network (NN) adaptive dynamic surface control (DSC) design for a class of single-input single-output (SISO) nonlinear systems. Such designs adopt NNs to approximate unknown continuous system functions. To avoid the "explosion of complexity" problem, a novel nonlinear filter is developed in control design. Under the framework of adaptive backstepping control, an NN adaptive finite-time DSC design algorithm is proposed by adopting a smooth projection operator and finite-time Lyapunov stable theory. The developed control algorithm means that the tracking error converges to a small neighborhood of origin within finite time, which further verifies that all the signals of the controlled system possess globally finite-time stability (GFTS). Finally, both numerical and practical simulation examples and comparing results are provided to elucidate the superiority and effectiveness of the proposed control algorithm.

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.

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.

Output-Feedback Flocking Control of Multiple Autonomous Surface Vehicles Based on Data-Driven Adaptive Extended State Observers

This article addresses an output-feedback flocking control problem for a swarm of autonomous surface vehicles (ASVs) to follow a leading ASV guided via a parameterized path. The leading and following ASVs are subject to completely unknown model parameters, external disturbances, and unmeasured velocities. A data-driven adaptive anti-disturbance control method is proposed for establishing a flocking behavior without any prior knowledge of model parameters. Specifically, a data-driven adaptive extended state observer (ESO) is proposed such that unknown input gains, unmeasured velocities, and total disturbance are simultaneously estimated. For the leading ASV, an output-feedback path-following control law is developed to follow a predefined parameterized path. For following ASVs, an output-feedback flocking control law is developed based on an artificial potential function for collision avoidance and connectivity preservation, in addition to a distributed ESO for estimating the velocity of the leading ASV through a cooperative estimation network. The simulation results are discussed to substantiate the efficacy of the proposed path-guided output-feedback ASV flocking control based on data-driven adaptive ESOs without measured velocity information.

Distributed Cooperative Learning Control of Uncertain Multiagent Systems With Prescribed Performance and Preserved Connectivity

For an uncertain multiagent system, distributed cooperative learning control exerting the learning capability of the control system in a cooperative way is one of the most important and challenging issues. This article aims to address this issue for an uncertain high-order nonlinear multiagent system with guaranteed transient performance and preserved initial connectivity under an undirected and static communication topology. The considered multiagent system has an identical structure and the uncertain agent dynamics are estimated by localized radial basis function (RBF) neural networks (NNs) in a cooperative way. The NN weight estimates are rigorously proven to converge to small neighborhoods of their common optimal values along the union of all agents' trajectories by a deterministic learning theory. Consequently, the associated uncertain dynamics can be locally accurately identified and can be stored and represented by constant RBF networks. Using the stored knowledge on identified system dynamics, an experience-based distributed controller is proposed to improve the control performance and reduce the computational burden. The theoretical results are demonstrated on an application to the formation control of a group of unmanned surface vehicles.

Guaranteed Synchronization Performance Control of Nonlinear Time-Delay MIMO Multiagent Systems With Actuator Faults

This paper addresses the synchronization control problem of leader-follower multiagent systems with each follower described by a class of high-order nonlinear multiple-input-multiple-output (MIMO) dynamics in the presence of time delays and actuator faults. A distributed synchronization scheme with guaranteed synchronization performance based on the radial basis function neural network (RBF NN) is introduced. We propose an augmented quadratic Lyapunov function by incorporating the lower bounds of control gain matrices and the actuator healthy indicator, and the problems caused by the unknown time-varying control gain matrices, actuator faults, and coupling terms among agents are solved. Meanwhile, the output of followers can track that of the leader and the steady state, and the transient performance of synchronization can be guaranteed, while all the other signals in the closed-loop system are guaranteed to be bounded. Finally, numerical analysis has been carried out to verify the effectiveness of the proposed controller.

Multilayered Sampled-Data Iterative Learning Tracking for Discrete Systems With Cooperative-Antagonistic Interactions

The tracking for discrete systems is discussed by designing two kinds of multilayered iterative learning schemes with cooperative-antagonistic interactions in this paper. The definition of the signed graph is presented and iterative learning schemes are then designed to be multilayered and have cooperative-antagonistic interactions. Moreover, considering the limited bandwidth of information storage, the state information of these controllers is updated in light of previous learning iterations but not just dependent on the last iteration. Two simple criteria are addressed to discuss the tracking of discrete systems with multilayered and cooperative-antagonistic iterative schemes. The simulation results are shown to demonstrate the validity of the given criteria.

Identification and Control of Nonlinear Systems Using Neural Networks: A Singularity-Free Approach

In this paper, identification and control for a class of nonlinear systems with unknown constant or variable control gains are investigated. By reformulating the original system dynamic equation into a new form with a unit control gain and introducing a set of filtered variables, a novel neural network (NN) estimator is constructed and a new estimation error is used to update the augmented weights. Based on the identification results, two singularity-free NN indirect adaptive controllers are developed for nonlinear systems with unknown constant control gains or variable control gains, respectively. Because the singularity problem is eradicated, the proposed methods remove limitations on parameter estimates that are used to guarantee the positiveness of the estimated control gain. Consequently, a more accurate estimation result can be achieved and the system state can track the given reference signal more precisely. The effectiveness of the proposed identification and control algorithms are tested and the superiority of the proposed singularity-free approach is demonstrated by simulation results.

Neural Learning Control of Strict-Feedback Systems Using Disturbance Observer

This paper studies the compound learning control of disturbed uncertain strict-feedback systems. The design is using the dynamic surface control equipped with a novel learning scheme. This paper integrates the recently developed online recorded data-based neural learning with the nonlinear disturbance observer (DOB) to achieve good "understanding" of the system uncertainty including unknown dynamics and time-varying disturbance. With the proposed method to show how the neural networks and DOB are cooperating with each other, one indicator is constructed and included into the update law. The closed-loop system stability analysis is rigorously presented. Different kinds of disturbances are considered in a third-order system as simulation examples and the results confirm that the proposed method achieves higher tracking accuracy while the compound estimation is much more precise. The design is applied to the flexible hypersonic flight dynamics and a better tracking performance is obtained.

Bounded Neural Network Control for Target Tracking of Underactuated Autonomous Surface Vehicles in the Presence of Uncertain Target Dynamics

This paper is concerned with the target tracking of underactuated autonomous surface vehicles with unknown dynamics and limited control torques. The velocity of the target is unknown, and only the measurements of line-of-sight range and angle are obtained. First, a kinematic control law is designed based on an extended state observer, which is utilized to estimate the uncertain target dynamics due to the unknown velocities. Next, an estimation model based on a single-hidden-layer neural network is developed to approximate the unknown follower dynamics induced by uncertain model parameters, unmodeled dynamics, and environmental disturbances. A bounded control law is designed based on the neural estimation model and a saturated function. The salient feature of the proposed controller is twofold. First, only the measured line-of-sight range and angle are used, and the velocity information of the target is not required. Second, the control torques are bounded with the bounds known as a priori. The input-to-state stability of the closed-loop system is analyzed via cascade theory. Simulations illustrate the effectiveness of the proposed bounded controller for tracking a moving target.

Observer-Based Adaptive Fuzzy Fault-Tolerant Optimal Control for SISO Nonlinear Systems

This paper investigates adaptive fuzzy output feedback fault-tolerant optimal control problem for a class of single-input and single-output nonlinear systems in strict feedback form. The considered nonlinear systems contain unknown nonaffine nonlinear faults and unmeasured states. Fuzzy logic systems are used to approximate cost function and unknown nonlinear functions, respectively. It is assumed that the states of the systems to be controlled are unmeasurable, thus an adaptive state observer is developed. To solve the nonaffine nonlinear fault control design problem, filtered signals are introduced into the adaptive backstepping control design procedures, and in the framework of adaptive critic technique and fault-tolerant control technique, a novel adaptive fuzzy fault-tolerant optimal control scheme is developed. The stability of the closed-loop system is proved by using Lyapunov stability theory. The simulation results verify the effectiveness of the proposed control strategy.

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

This paper is concerned with a Takagi-Sugeno (T-S) fuzzy dynamic positioning controller design for an unmanned marine vehicle (UMV) in network environments. Network-based T-S fuzzy dynamic positioning system (DPS) models for the UMV are first established. Then, stability and stabilization criteria are derived by taking into consideration an asynchronous difference between the normalized membership function of the T-S fuzzy DPS and that of the controller. The proposed stabilization criteria can stabilize states of the UMV. The dynamic positioning performance analysis verifies the effectiveness of the networked modeling and the controller design.

Online Recorded Data-Based Composite Neural Control of Strict-Feedback Systems With Application to Hypersonic Flight Dynamics

This paper investigates the online recorded data-based composite neural control of uncertain strict-feedback systems using the backstepping framework. In each step of the virtual control design, neural network (NN) is employed for uncertainty approximation. In previous works, most designs are directly toward system stability ignoring the fact how the NN is working as an approximator. In this paper, to enhance the learning ability, a novel prediction error signal is constructed to provide additional correction information for NN weight update using online recorded data. In this way, the neural approximation precision is highly improved, and the convergence speed can be faster. Furthermore, the sliding mode differentiator is employed to approximate the derivative of the virtual control signal, and thus, the complex analysis of the backstepping design can be avoided. The closed-loop stability is rigorously established, and the boundedness of the tracking error can be guaranteed. Through simulation of hypersonic flight dynamics, the proposed approach exhibits better tracking performance.