Adaptive Optimal Control for a Class of Nonlinear Systems: The Online Policy Iteration Approach

He, Shuping; Fang, Haiyang; Zhang, Maoguang; Liu, Fei; Ding, Zhengtao

doi:10.1109/tnnls.2019.2905715

Cited by 144 publications

(67 citation statements)

References 39 publications

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“…By analogy, the control law of the entire closedloop system can be obtained, and the convergence of the closed-loop system is guaranteed by Lyapunov stability analysis method. Backstepping with intelligent algorithm can increase the quality of the transition process, reduce or even eliminate the uncertainty of the matching constraints, and provide a structured and systematic design method for the Lyapunov function design for complex nonlinear systems [20][21][22][23]. Sliding mode control (SMC) is another robust control method, which produces a switching control law (equivalent control law) to force the system to converge to the sliding surface within a boundary layer near the sliding surface under the convergence of the Lyapunov stability theory [24,25].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Backstepping Sliding Mode Control of Trajectory Tracking for Robotic Manipulators

Zhu

Puchen

et al. 2020

Complexity

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To achieve precise trajectory tracking of robotic manipulators in complex environment, the precise dynamic model, parameters identification, nonlinear characteristics, and disturbances are the factors that should be solved. Although parameters identification and adaptive estimate method were proposed for robotic control in many literature studies, the essential factors, such as coupling and friction, are rarely mentioned as it is difficult to build the precise dynamic model of the robotic manipulator. An adaptive backstepping sliding mode control is proposed to solve the precise trajectory tracking under external disturbances with complex environment, and the dynamic response characteristics of a two-link robotic manipulator are described in this paper. First, the Lagrange kinetic method is used to derive the precise dynamic model which includes the nonlinear factor with friction and coupling. Moreover, the dynamic model of two-link robotic manipulator is built. Second, the estimate function for the nonlinear part is selected, and backstepping algorithm is used for analyzing the stabilities of the sliding mode controller by using Lyapunov theory. Furthermore, the convergence of the proposed controller is verified subject to the external disturbance. At last, numerical simulation results are reported to demonstrate the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Adaptive Backstepping Sliding Mode Control of Trajectory Tracking for Robotic Manipulators

Zhu

Puchen

et al. 2020

Complexity

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“…The multi-machine power system is affected easily by external disturbance, which can influence the tracking performance. For the parameters uncertainty and disturbance problem, various solutions, such as robust control [31]- [33], online policy iteration [34], [35], adaptive control [36]- [39], parametric based dynamic compensator [40] and active disturbance rejection control [41]. In [31], a discrete-time sliding mode controller is designed for a class of conic-type systems, and the disturbance attenuation level achieved the prescribed performance.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Dynamic Surface Control for Multi-Machine Power System Based on Composite Learning Method and Disturbance Observer

et al. 2020

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A composite learning dynamic surface control is proposed for a class of multi-machine power systems with uncertainties and external disturbances by using fuzzy logic systems (FLSs) and disturbance observer (DOB). The main characteristics of the proposed strategy are as follows: (1) The approximation ability of FLSs for nonlinear model of multi-machine power systems is enhanced considerably by using the composite learning method and providing additional correction information for the FLSs. These findings differ considerably from previous designs that focus directly on the system's tracking performance. (2) The filtering errors caused by the utilizations of the first-order low-pass filters in dynamic surface control (DSC) are compensated effectively by designing the compensating signals in the control law design process. (3) The compound disturbances including the FLSs' approximation error and external disturbances are estimated and mitigated by constructing DOB. Finally, the proposed control algorithm is verified on the StarSim Hardware-in-loop experimental platform, and the experimental results validate the effectiveness of the proposed control strategy in suppressing disturbances and enhancing the robustness of the controller.

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“…Its application extends to many practical areas, such as sleep improvement, tone discrimination [28], and financial stability [21,23]. From the consideration of reducing control cost and control time, discontinuous controllers have been designed to stabilize a given system such as feedback control [29][30][31][32], pinning control [33], impulsive control [34], adaptive control [35][36][37][38][39][40], and intermittent control [41][42][43][44]. As for intermittent control, control time is divided into periodic and aperiodic type.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Stabilization of Malware Propagation in Wireless Sensor Network via Aperiodically Intermittent White Noise

et al. 2020

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In this paper, we propose a novel heterogeneous model to describe the propagation dynamics of malware (viruses, worms, Trojan horses, etc.) in wireless sensor networks. Our model takes into consideration different battery-level sensor nodes contrary to existing models. In order to control the spread of malware, we design an aperiodically intermittent controller driven by white noise, which has striking advantages of lower cost and more flexible control strategy. We give a distinct condition on stability in probability one using graph-theoretical Lyapunov function and stochastic analysis method. Our results show that the nonlinear malware propagation system can be stabilized by intermittent stochastic perturbation under the intermittent time related to stochastic perturbation intensity. Our theoretical results can be applied to understand the observed mechanisms of malware and design interventions to control the spread of malware. Numerical simulations illustrate our analytical results clearly.

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Adaptive Optimal Control for a Class of Nonlinear Systems: The Online Policy Iteration Approach

Cited by 144 publications

References 39 publications

Adaptive Backstepping Sliding Mode Control of Trajectory Tracking for Robotic Manipulators

Adaptive Backstepping Sliding Mode Control of Trajectory Tracking for Robotic Manipulators

Adaptive Fuzzy Dynamic Surface Control for Multi-Machine Power System Based on Composite Learning Method and Disturbance Observer

Stochastic Stabilization of Malware Propagation in Wireless Sensor Network via Aperiodically Intermittent White Noise

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