Discrete-Time Extended State Observer-Based Model-Free Adaptive Control Via Local Dynamic Linearization

Chi, Ronghu; Yu, Hui; Zhang, Shuhua; Huang, Biao; Hou, Zhongsheng

doi:10.1109/tie.2019.2947873

Cited by 75 publications

(64 citation statements)

References 26 publications

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“…Therefore, because high-order terms are omitted, there are inevitably unmodeled dynamics. Inaccurate modeling will reduce the system performance [55]. State-dependent Riccati equation control (SDRE) technology can synthesize nonlinear feedback control by allowing the nonlinearity in the system state, and at the same time, it can provide great design flexibility for the control system design of nonlinear dynamic system, and avoid the error caused by traditional linearization treatment [56].…”

Section: System Nonlinearity Factorsmentioning

confidence: 99%

“…In addition, ref. [55] developed a local compact form dynamic linearization (local-CFDL) to transform the original nonlinear nonaffine system into an affine structure consisting of both an unknown residual nonlinear time-varying term and a linearly parametric term affine to the control input. The local-CFDL model can be rewritten in a compact form:…”

Section: System Nonlinearity Factorsmentioning

confidence: 99%

“…It suppresses the influence of external disturbances, environmental changes, and the effect of the system coupling with itself. Adaptive controls also decrease modeling errors and their influences [55].…”

Section: Hierarchical Controlmentioning

confidence: 99%

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AUV Trajectory Tracking Models and Control Strategies: A Review

2021

JMSE

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Autonomous underwater vehicles (AUVs) have been widely used to perform underwater tasks. Due to the environmental disturbances, underactuated problems, system constraints, and system coupling, AUV trajectory tracking control is challenging. Thus, further investigation of dynamic characteristics and trajectory tracking control methods of the AUV motion system will be of great importance to improve underwater task performance. An AUV controller must be able to cope with various challenges with the underwater vehicle, adaptively update the reference model, and overcome unexpected deviations. In order to identify modeling strategies and the best control practices, this paper presents an overview of the main factors of control-oriented models and control strategies for AUVs. In modeling, two fields are considered: (i) models that come from simplifications of Fossen’s equations; and (ii) system identification models. For each category, a brief description of the control-oriented modeling strategies is given. In the control field, three relevant aspects are considered: (i) significance of AUV trajectory tracking control, (ii) control strategies; and (iii) control performance. For each aspect, the most important features are explained. Furthermore, in the aspect of control strategies, mathematical modeling study and physical experiment study are introduced in detail. Finally, with the aim of establishing the acceptability of the reported modeling and control techniques, as well as challenges that remain open, a discussion and a case study are presented. The literature review shows the development of new control-oriented models, the research in the estimation of unknown inputs, and the development of more innovative control strategies for AUV trajectory tracking systems are still open problems that must be addressed in the short term.

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Section: System Nonlinearity Factorsmentioning

confidence: 99%

Section: System Nonlinearity Factorsmentioning

confidence: 99%

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AUV Trajectory Tracking Models and Control Strategies: A Review

2021

JMSE

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“…However, it is most difficult to obtain a mechanistic mathematical model of a real plant because it is generally very complex with strong nonlinear dynamics, nonaffine control structure, large uncertainties and so on (Chi et al, 2020b; Hou and Lei, 2020; Hou and Xiong, 2019). Therefore, how to show the convergence of the networked ILC of nonaffine nonlinear systems with channel noise without relying on any model information becomes more interesting in practical applications.…”

Section: Introductionmentioning

confidence: 99%

Iterative learning control for nonlinear nonaffine networked systems with stochastic noise in communication channels

Liu

Chi

2021

Transactions of the Institute of Measurement and Control

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This article investigates the convergence analysis of networked iterative learning control for nonlinear nonaffine systems firstly by considering stochastic noise introduced by the network channels. The convergence analysis is under a data-driven framework, which does not rely on any mechanism model information. To deal with the nonlinearity, both the state transition technique and the differential mean value principle are used to formulate the iterative dynamics of system states, tracking errors and input signals using a lifted matrix expression, respectively. In terms of the contraction mapping principle, the tracking error is shown to be iteratively convergent under the sense of mathematical expectation. Since the [Formula: see text]-norm is not used in the analysis, the convergence property of the tracking error is not affected by the operation interval and a good transient performance can be ensured in theory. Simulation studies test the theoretical results.

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“…[24], Chi etc. [25] proposed an extended state observer-based model-free adaptive control by local dynamic linearization. A disturbance observer-based adaptive tracking control with actuator saturation has developed in [26].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach Based on Terminal Sliding Mode Control for Optimization of Nonlinear Systems

Zhang

Fan

et al. 2020

IEEE Access

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It is difficult for many discrete nonlinear systems to quickly meet control requirements and establish accurate dynamic models. This study presents a control strategy that combines model-free adaptive control and discrete terminal sliding mode control to solve these problems. A novel discrete terminal sliding surface is designed in this strategy. It not only avoids the establishment of system models but also achieves quick response. Firstly, a perturbation observer is established to estimate perturbation of the system and time-varying parameter pseudo-gradient is estimated from input and output data of the system. Secondly, the controller combining the partial form dynamic linearization model free adaptive control and discrete terminal sliding mode control is designed. The stability and convergence of the proposed strategy are proved in theory. Thirdly, the proposed strategy is compared with PID technique and the method of combining model free adaptive control(MFAC) with discrete sliding mode control in chemical system and water tank system respectively. Simulation examples demonstrate that the proposed strategy is superior to others in terms of fast response. This study provides a reference for solving the control problem of discrete nonlinear systems. INDEX TERMS Dynamic models of nonlinear systems, perturbation observer, data-driven control, discrete terminal sliding mode control.

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Discrete-Time Extended State Observer-Based Model-Free Adaptive Control Via Local Dynamic Linearization

Cited by 75 publications

References 26 publications

AUV Trajectory Tracking Models and Control Strategies: A Review

AUV Trajectory Tracking Models and Control Strategies: A Review

Iterative learning control for nonlinear nonaffine networked systems with stochastic noise in communication channels

A Novel Approach Based on Terminal Sliding Mode Control for Optimization of Nonlinear Systems

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