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This article considers the problem of adaptive prescribed performance tracking control via slow switching for a general class of uncertain switched nonlinear systems (SNSs) with unmodeled dynamics (UDs) and nonstrict-feedback structure. The UDs are not in their form of the input-to-state practical stability and their state information is unmeasurable. By reassigning the function variables, the coupling effects between UDs and the prescribed performance function are eliminated through the iterative process. In virtue of the neural networks (NNs) approximation capability, a novel adaptive backstepping procedure is proposed without adding extra first-order filters. By choosing an appropriate slow switching law, all the signals of the closed-loop system are bounded, and the system output tracks the reference signal with a prescribed performance level (PPL). [ABSTRACT FROM AUTHOR]

In this paper, an event-triggered adaptive approximately optimal tracking control approach is proposed for a class of non-affine nonlinear single-input single-output (SISO) systems via output feedback. With the help of fuzzy logic systems (FLSs), a fuzzy state observer is designed for the estimation of internal states by approximating unknown nonlinear functions, where a low-pass filter (LPF) is added for the algebraic loop problem. Then, the output feedback control approach, in the backstepping framework and the event-triggered mechanism, is presented. It contains the adaptive backstepping control and the approximately optimal control via adaptive dynamic programming (ADP) technology, and the computation cost and transmission load are reduced while guaranteeing that the performance index of the system is approximately minimised. Finally, two simulation examples are provided to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper studies an adaptive neural control for nonlinear multiple‐input multiple‐output systems with dynamic uncertainties, hysteresis input, and time delay. The studied systems are composed of N nonlinear time‐delay subsystems and the interconnection terms are contained in every equation of each subsystem. Adaptive neural control algorithms are developed by introducing a well‐defined smooth function. The unknown time‐varying delays and the unmodeled dynamics are dealt with by constructing appropriate Lyapunov–Krasovskii functions and introducing an available dynamic signal. The main advantage of the proposed controllers is that they contain fewer parameter estimates that need to be updated online. Consequently, the accuracy of ultimate tracking errors asymptotically approaches a pre‐defined bound, and all signals in the closed‐loop systems are also ensured to be uniformly ultimately bounded. Finally, a simulation example is provided to illustrate the effectiveness and merits of the proposed adaptive neural network control schemes. [ABSTRACT FROM AUTHOR]

Summary: This paper focuses on the leader‐following consensus control problem of stochastic multiagent systems with unknown input dead‐zone nonlinearity, unknown actuator failures, and nonlinear dynamics. A distributed adaptive neural consensus tracking controller is presented for stochastic multiagent systems directions under directed graphs, which can achieve predefined synchronization error bounds. By mainly activating an auxiliary robust control component for pulling back the transient escaped from the neural active region, a multiswitching robust neuroadaptive controller in the neural approximation domain can achieve globally uniformly ultimately bounded tracking stability of multiagent systems recently. By constructing a smooth dead‐zone inverse and applying the dynamic surface control technique, distributed consensus controllers are developed to guarantee that the trajectories of synchronization error converge to a small neighborhood of the origin. Two simulation examples are provided to illustrate the effectiveness and advantage of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the influences of unknown disturbances are first analyzed, and the structural properties of the disturbances are given. By appropriately applying Fourier series approximation, a novel continuously differentiable nonlinear friction model is synthesized by modifying the traditional piecewise continuous LuGre model, then a desired compensation version of the proposed adaptive repetitive controller is developed for precise tracking control of servo systems to compensate for spatial periodic disturbance and random disturbance. To further reduce noise sensitivity and improve tracking accuracy, the desired compensation robust control term is also constructed to effectively attenuate the effect of approximation errors, and thus a theoretically asymptotic tracking performance is achieved by the proposed controller, which is very important for the high accuracy tracking control of servo systems. Extensive comparative experimental results are obtained to verify the high‐performance nature of the proposed control strategies. [ABSTRACT FROM AUTHOR]

This paper focuses on a class of nonlinear switching multi-agent systems about event-triggered adaptive tracking control. Compared with existing results, the event-triggered mechanism is first employed in nonlinear switched multi-agent systems with unknown parameters, and it can reduce the amount of computation and the frequency of the controller updates. By utilizing the backstepping technology, adaptive laws are designed to analyze the unknown parameters. In the sequel, all signals of multi-agent systems are bounded, the tracking errors converge to a small neighborhood of the origin. Finally, simulation results verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

An event-triggered resilient control framework is proposed for a class of uncertain nonlinear systems under DoS attacks. Unknown dynamics are approximated by fuzzy logic systems (FLSs), while a switched adaptive state observer is presented to reconstruct unavailable states in the presence of intermittent attacks. Then, the backstepping method is developed for the control scheme; additionally, a relative threshold triggered scheme is designed. Furthermore, in theory, we prove the proposed control scheme guarantees all closed-loop signals remain bounded, and the error signal converges to a small area of the origin under DoS attacks. Finally, a power networked system and a numerical example are used to verify the effectiveness of the theoretical claims in our proposed scheme. [ABSTRACT FROM AUTHOR]