In this paper, the problem of adaptive optimized tracking control design is addressed for a class of nonlinear systems in strict-feedback form. The system under consideration contains input delay and has unmeasurable ...
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In this paper, the problem of adaptive optimized tracking control design is addressed for a class of nonlinear systems in strict-feedback form. The system under consideration contains input delay and has unmeasurable and restricted states within predefined compact sets. First, neural networks (NNs) are employed to approximate the unknown nonlinear dynamics, and an adaptive neural network (NN) state observer is constructed to compensate for the absence of state information. Additionally, by utilizing an auxiliary system compensation method alongside the backsteppingtechnique, the impact of input delay is eliminated, and the generation of intermediate variables is prevented. Second, tan-type barrier optimal cost functions are established for each subsystem within the backstepping method to prevent the state variables from exceeding preselected sets. Moreover, by establishing both actor and critic NNs to execute a reinforcement learning algorithm, the optimal controller and optimal performance index function are evaluated, while relaxing the persistence of excitation condition. According to the Lyapunov stability theorem, it is demonstrated that all signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB), and the output signal accurately tracks a reference trajectory with the desired precision. Finally, a practical simulation example is provided to verify the effectiveness of the proposed control strategy, demonstrating its potential for real-world implementation.
This article proposes an adaptive neural network (NN) output feedback optimized control design for a class of strict-feedback nonlinear systems that contain unknown internal dynamics and the states that are immeasurab...
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This article proposes an adaptive neural network (NN) output feedback optimized control design for a class of strict-feedback nonlinear systems that contain unknown internal dynamics and the states that are immeasurable and constrained within some predefined compact sets. NNs are used to approximate the unknown internal dynamics, and an adaptive NN state observer is developed to estimate the immeasurable states. By constructing a barrier type of optimal cost functions for subsystems and employing an observer and the actor-critic architecture, the virtual and actual optimal controllers are developed under the framework of backsteppingtechnique. In addition to ensuring the boundedness of all closed-loop signals, the proposed strategy can also guarantee that system states are confined within some preselected compact sets all the time. This is achieved by means of barrier Lyapunov functions which have been successfully applied to various kinds of nonlinear systems such as strict-feedback and pure-feedback dynamics. Besides, our developed optimal controller requires less conditions on system dynamics than some existing approaches concerning optimal control. The effectiveness of the proposed optimal control approach is eventually validated by numerical as well as practical examples.
In this article, we present an innovative approach for controlling nonlinear switched systems (NSSs) with strict feedback utilizing adaptive neural networks (ANNs). Our methodology encompasses several facets, addressi...
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In this article, we present an innovative approach for controlling nonlinear switched systems (NSSs) with strict feedback utilizing adaptive neural networks (ANNs). Our methodology encompasses several facets, addressing key challenges inherent to these systems. To commence, we tackle the constrained nature of NSSs with strict feedback by designing a barrier Lyapunov function. This function ensures that all states within the switched systems remain within prescribed constraints. Additionally, we harness neural networks (NNs) to approximate the unknown nonlinear functions inherent to the system. Furthermore, we deploy an ANN state observer to estimate unmeasurable states. Our approach then proceeds to develop a cost function for the subsystem. Building upon this, we apply the Hamiltonian-Jacobi-Bellman (HJB) solution in conjunction with observer and behavior critic architectures, all rooted in backstepping control (BC) principles. This integration yields both a virtual optimal controller and a real optimal controller. Furthermore, we introduce a novel ANN event-triggered control (ETC) strategy tailored explicitly for strictly feedback systems. This strategy proves highly effective in reducing the utilization of communication resources and eliminating the occurrence of Zeno behavior. Our analysis provides formal proof that all states within the closed-loop system exhibit half-leaf consistency and are ultimately bounded, regardless of arbitrary switching conditions. Finally, we substantiate the efficacy and viability of our control scheme through comprehensive numerical simulations.
This article studies the adaptive optimized leader-follower consensus control problem for a class of discrete-time multi-agent systems with asymmetric input saturation constraints and hybrid faults based on the optimi...
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This article studies the adaptive optimized leader-follower consensus control problem for a class of discrete-time multi-agent systems with asymmetric input saturation constraints and hybrid faults based on the optimized backstepping technique. Different from the conventional saturation model, we consider an individual asymmetric satu-ration constraint for each actuator instead of a common upper and lower bound for all actuators. Besides, a set of hybrid faults is also considered, with the main focus on the partial fault and bias fault. To eliminate the effects of saturation and faults, simplified smooth function is constructed to approximate the asymmetric saturation model, and designed compensation signals are used to cope with the two main types of faults to improve the fault-tolerance and system performance. Subsequently, long-term strategic utility functions and virtual control signals are approximated to the optimal levels by adopting the actor-critic neural network (NN) framework, and the actor-critic NN weights are adjusted in the light of a gradient descent method. According to the forward difference Lyapunov function approach, it is proved that the closed-loop system can be stabilized and all errors are semiglobally uniformly ultimately bounded. Finally, the validity of the proposed control scheme is demonstrated through two simulation examples. & COPY;2023 Elsevier B.V. All rights reserved.
This paper addresses a novel adaptive fault -tolerant control (FTC) design based on adaptive dynamic programming (ADP) technique for hypersonic vehicle (HSV) subject to actuator fault and state constrains. The total c...
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This paper addresses a novel adaptive fault -tolerant control (FTC) design based on adaptive dynamic programming (ADP) technique for hypersonic vehicle (HSV) subject to actuator fault and state constrains. The total control input is constructed by the combination of backsteppingbased adaptive FTC and the ADP -based adaptive optimal control to improve the tracking performance and fault -tolerant capacity. By introducing a barrier Lyapunov function (BLF) to deal with the state constraints, a backstepping-based FTC scheme is designed to transform the tracking control problem into an equivalent optimal control problem. Subsequently, an adaptive optimal control strategy is developed by using the ADP technique to provide a supplementary control action. A critic network is constructed to solve the Hamilton-Jacobi-Bellman (HJB) equation online. The convergence properties of the closed -loop system are developed by utilizing the Lyapunov stability theory. Finally, simulation results are carried out to illustrate the effectiveness and efficiency of the proposed adaptive ADP -based FTC strategy.
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