Observer-Based Neuro-Adaptive Optimized Control of Strict-Feedback Nonlinear Systems With State Constraints

This article investigates the adaptive resilient control problem for a class of nonlinear time‐delay systems (TDSs) with unknown state‐dependent deception attacks. For the purpose of overcoming the issue of “explosion of complexity” existing in the traditional backstepping control design and improving filter performance, an improved fractional‐order command filtered backstepping technique is adopted to obtain the filtered signals of the virtual control laws. Furthermore, the corresponding auxiliary system and attack compensator are constructed to compensate the effects of input delay and unknown sensor deception attacks. Stability results prove that all the signals in the closed‐loop systems are semi‐globally uniformly ultimately bounded (SGUUB) by using the proposed adaptive resilient controller. Finally, two simulation examples are provided to show the effectiveness of the developed control method.

Section: Discussionmentioning

confidence: 99%

Adaptive resilient control design for nonlinear time‐delay systems against unknown state‐dependent deception attacks

Song

Zhang

Park

et al. 2021

“…However, its provided stability proof compromises the attractive model‐free feature of RL since a mathematical form of dynamics is required to present the rigorous system stability analysis. Even though the required explicit knowledge of dynamics could be avoided by using add‐on techniques such as NNs, 10‐12 fuzzy models, 13 Gaussian process (GP), 14 or observers, 15 the accompanying identification processes further increase computational complexity and parameter tuning efforts. This motivates us to develop a novel, computationally simple RL‐based control strategy, which exhibits both a model‐free feature and a provable system stability guarantee, to accomplish the robust optimal stabilization of continuous‐time nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Model‐free incremental adaptive dynamic programming based approximate robust optimal regulation

Wang

Liu

et al. 2022

This article presents a new formulation for model‐free robust optimal regulation of continuous‐time nonlinear systems. The proposed reinforcement learning based approach, referred to as incremental adaptive dynamic programming (IADP), utilizes measured input‐state data to allow the design of the approximate optimal incremental control strategy, stabilizing the controlled system incrementally under model uncertainties, environmental disturbances, and input saturation. By leveraging the time delay estimation (TDE) technique, we first use sensor data to reduce the requirement of a complete dynamics, where input‐state data is adopted to construct an incremental dynamics which reflects the system evolution in an incremental form. Then, the resulting incremental dynamics serves to design the approximate optimal incremental control strategy based on adaptive dynamic programming, which is implemented as a simplified single critic structure to get the approximate solution to the value function of the Hamilton–Jacobi–Bellman equation. Furthermore, for the critic neural network, experience data are used to design an off‐policy weight update law with guaranteed weight convergence. Rather importantly, we incorporate a TDE error bound related term into the cost function, whereby the unintentionally introduced TDE error is attenuated during the optimization process. The proofs of system stability and weight convergence are provided. Numerical simulations are conducted to validate the effectiveness and superiority of our proposed IADP, especially regarding the reduced control energy expenditure and the enhanced robustness.

“…Note that a famous structure motivated by reinforcement learning (named as the actor-critic structure) has been developed to guarantee the implementation of developed ADP approaches, where the critic network and the action network are, respectively, employed to approximate the cost function and the ideal control sequence. [9][10][11] It is worth mentioning that the corresponding control is actually near-optimal control due to the approximation errors of the ADP structure.…”

Section: Introductionmentioning

confidence: 99%

Neural‐network‐based control for discrete‐time nonlinear systems with denial‐of‐service attack: The adaptive event‐triggered case

Wang

Ding

et al. 2021

110

This article investigates a neural network (NN)-based control problem for unknown discrete-time nonlinear systems with a denial-of-service (DoS) attack and an adaptive event-triggered mechanism (ETM). The considered DoS attacks are described by the occurrence frequency and durations and hence more general in comparison with existing stochastic models. To the addressed problem, a novel adaptive rule adjusting the triggering threshold of ETM is constructed to govern the communication schedule, and an NN-based observer is designed to identify the system dynamics where a piecewise update rule of NN weights is adopted to handle the challenge of the complex time series coming from both ETM and DoS attacks. In light of this kind of protocol-and attack-induced switched systems, a sufficient condition dependent on the occurrence frequency and durations of DoS attacks is elaborately established via the analysis of input-to-state stability. Furthermore, an iteration adaptive dynamic programming approach is proposed to handle the addressed control issue, and the boundedness is discussed to both the estimation errors of the Luenberger-type observer and the identified errors of NN weights of observer networks as well as actor-critic networks with the help of the Lyapunov theory. Finally, a simulation example is utilized to illustrate the usefulness of the proposed controller design scheme.