Event-Triggered Adaptive Fuzzy Tracking Control for Uncertain Nonlinear Systems Preceded by Unknown Prandtl-Ishlinskii Hysteresis

Adaptive Neural Control for Hysteretic Nonlinear Systems With Hysteresis Neural Direct Inverse Compensator and Its Application

Aiming at high precision control for a class of hysteretic nonlinear systems, a new hysteresis direct inverse compensator-based adaptive output feedback control scheme is designed in this article. First, a novel long short-term memory neural network (LSTMNN)-based hysteresis inverse compensator is established to compensate the asymmetric hysteresis nonlinearity, where the LSTMNN is used as the prediction mechanism for model operator weights, rather than the overall mapping of hysteresis input and output. Second, by designing the modified high-gain K-Filter states observer and the error transformed function, the unmeasurable states are estimated with arbitrarily small estimation error and the prespecified tracking performance is achieved. Lastly, the biconical dielectric elastomer actuator (DEA) motion platform is constructed. Then, the effectiveness of the proposed LSTMNN-based hysteresis inverse compensator and control scheme are verified on the experimental platform. The experimental results illustrate the effectiveness and advantages of proposed control scheme.

Event-Triggered Near-Optimal Control for Unknown Discrete-Time Nonlinear Systems Using Parallel Control

This article uses parallel control to investigate the problem of event-triggered near-optimal control (ETNOC) for unknown discrete-time (DT) nonlinear systems. First, to achieve parallel control, an augmented nonlinear system (ANS) with an augmented performance index (API) is proposed to introduce the control input into the feedback system. The control stability relationship between the ANS and the original system is analyzed, and it is shown that, by choosing a proper API, optimal control of the ANS with the API can be seen as near-optimal control of the original system with the original performance index (OPI). Second, based on parallel control, a novel event-triggered scheme is proposed, and then a novel ETNOC method is developed using the time-triggered optimal value function of the ANS with the API. The control stability is proved, and an upper bound, which is related to the design parameter, is provided for the actual performance index in advance. Then, to implement the developed ETNOC method for unknown DT nonlinear systems, a novel online learning algorithm is developed without reconstructing unknown systems, and neural network (NN) and adaptive dynamic programming (ADP) techniques are employed in the developed algorithm. The convergence of the signals in the closed-loop system (CLS) is shown using the Lyapunov approach, and the assumption of boundedness of input dynamics is not required. Finally, two simulations justify the theoretical conjectures.

Robust Adaptive Neural Control for Wing-Sail-Assisted Vehicle via the Multiport Event-Triggered Approach

This article presents a robust adaptive neural control algorithm for the wing-sail-assisted vehicle to track the desired waypoint-based route, where the event-triggered mechanism is with the multiport form. The main features of the proposed algorithm are three-fold: 1) the communication burden, in the channel from the sensor to the controller as well as the actuator, has been reduced for the merits of the multiport event-triggered approach. The feedback error signals and the control input will be updated only on the event-triggered time point; 2) for the wing-sail-assisted vehicle, the thrust force is provided by devices with the propeller and the sail. From this consideration, the proper sail force compensation is derived on the basis of information about the current heading angle and the wind direction. The corresponding control law can guarantee the energy-saving for the propeller; and 3) in the algorithm, the system uncertainties are remodeled by the neural-network approximator. Furthermore, by fusion of the robust neural damping and dynamic surface control (DSC) techniques, the corresponding gain-related adaptive law is developed to address constraints of the gain uncertainty and the environmental disturbances. Through the Lyapunov theorem, all signals of the closed-loop control system have been proved to be with the semiglobal uniform ultimate bounded (SGUUB) stability, including the triggered time point and the intermediate triggered interval. Finally, the numerical simulation and the practical experiment are illustrated to verify the effectiveness of the proposed strategy.

Dynamic Event-Triggered Platooning Control of Automated Vehicles Under Random Communication Topologies and Various Spacing Policies

This article addresses the problem of dynamic event-triggered platooning control of automated vehicles over a vehicular ad-hoc network (VANET) subject to random vehicle-to-vehicle communication topologies. First, a novel dynamic event-triggered mechanism is developed to determine whether or not the sampled data packets of each vehicle should be released into the VANET for intervehicle cooperation. More specifically, the threshold parameter in the triggering condition is dynamically adjusted over time according to the vehicular data variations, the dynamic threshold updating laws, and the bandwidth occupancy indication. Second, a unified platooning control framework is established to account for various spacing policies, randomly switching communication topologies, unknown leader control input, and external disturbances. Then, a new scheduling and platooning control co-design approach is presented such that the controlled vehicular platoon can successfully track the leader vehicle under random communication topologies and different spacing policies, including constant spacing, constant time headway spacing, and variable time headway spacing, meanwhile maintaining efficient bandwidth-aware resource management. Finally, comparative studies are provided to substantiate the effectiveness and merits of the proposed co-design approach.