Model‐free adaptive control for a class of nonlinear systems with uniform quantizer

Bu, Xuhui; Yu, Qiongxia; Hou, Zhongsheng; Liang, Jiaqi

doi:10.1002/rnc.5107

Cited by 18 publications

(10 citation statements)

References 39 publications

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Section: Preliminaries and Problem Formulationmentioning

confidence: 99%

“…Assumptions 1 and 2 for practical nonlinear systems are acceptable, 21 for instance, refrigeration control systems, heating furnace temperature control systems, crane control systems, and belt conveyor speed control systems. 27 Lemma 1. If the dynamics (1) satisfies Assumptions 1 and 2, it can be transformed into an equivalent dynamic linearization data model as…”

Section: Problem Formulationmentioning

confidence: 99%

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A novel bipartite consensus tracking scheme for unknown nonlinear multi-agent systems: Theoretical analysis and applications

Zhao

2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article proposes a novel distributed data-driven bipartite consensus tracking scheme for bipartite consensus tracking problems of multi-agent systems with bounded disturbances and coopetition networks. The proposed scheme only uses the input/output data of each agent without requiring the agents’ dynamics. We obtain the equivalent dynamic linearization data model for a controlled plant using the dynamic linearization technique based on the pseudo partial derivative. Considering the cooperative and competitive interactions among agents, the proposed method ensures that agents with adversarial relationships implement bipartite consensus tracking tasks even if only a subset of agents can access the information from the virtual leader. Moreover, the strict proof process of convergence properties reveals that the tracking error coverages to a small range around the origin. We also establish a set of software and hardware platform to demonstrate the effectiveness of the proposed distributed data-driven bipartite consensus tracking method.

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Section: Preliminaries and Problem Formulationmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

A novel bipartite consensus tracking scheme for unknown nonlinear multi-agent systems: Theoretical analysis and applications

Zhao

2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…Among these data‐driven methods, model‐free adaptive control (MFAC) approach has aroused a lot of attention because only the input and output data are used without employing explicit or implicit knowledge of the mathematic model. The MFAC method was first proposed by Hou 7 and has been continuously investigated and extended in these years 8‐11 . As a method to linearize nonlinear nonaffine system during MFAC, dynamic linearization (DL) technique has many advantages, such as data‐driven, just a few arguments are required to be renewed, and the obtained model is equal to the original system without any approximation 12 .…”

Section: Introductionmentioning

confidence: 99%

“…The MFAC method was first proposed by Hou 7 and has been continuously investigated and extended in these years. [8][9][10][11] As a method to linearize nonlinear nonaffine system during MFAC, dynamic linearization (DL) technique has many advantages, such as data-driven, just a few arguments are required to be renewed, and the obtained model is equal to the original system without any approximation. 12 Nevertheless, the data model obtained by the DL technique is proven to be over-linearized because no explicit nonlinear term is contained.…”

mentioning

confidence: 99%

Discrete‐time extended state observer‐based model‐free adaptive sliding mode control with prescribed performance

Huang

Dong

Zhang

et al. 2022

Intl J Robust & Nonlinear

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To tackle the trajectory tracking problem and achieve high control accuracy in many actual nonlinear systems with unknown disturbance, a novel discrete-time extended state observer-(DESO) based model-free adaptive sliding mode control strategy with prescribed tracking performance is studied, which only relies on the input/output data of the system rather than explicit model information. Firstly, a compact-form dynamic linearization method is used to reconstruct the discrete-time nonlinear process, where the time-varying parameter linearly connected with the control input is obtained by an adaptive method and the unknown nonlinear term is estimated by a DESO. Then, by considering the prescribed performance and using an unconstrained vector transformed from the constrained tracking error, one model-free sliding mode controller is designed. In addition, a rigorous stability analysis is presented to show the boundedness of the sliding mode function and the prescribed transient-state and steady-state performance of the output tracking error. Finally, the simulations with comparing results verify the effectiveness and superiority of the developed control scheme. K E Y W O R D Sdiscrete-time extended state observer, discrete-time system, model-free adaptive control, prescribed performance, sliding mode control INTRODUCTIONWith the continuous development of engineering technologies, the complexity of the practical system is intensively increasing, which leads to accurate system modeling becoming one of the most difficult tasks. 1 Thus, the control strategies based on mathematical models are not suitable for such complicated systems. To overcome this problem, data-driven control is proposed where only the input and output data are used. Data-driven methods have been applied in many practical scenes, for instance, quadrotor vehicles, 2 automated vehicles, 3 continuum robots, 4 and other industrial process systems. 5,6 Among these data-driven methods, model-free adaptive control (MFAC) approach has aroused a lot of attention because only the input and output data are used without employing explicit or implicit knowledge of the mathematic

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“…With the development of controller design, the problems including stabilization time, control accuracy, and difficulty of obtaining model information have received considerable attention 1‐3 . Constructing a controller that can achieve stabilization within any time and relies on little model knowledge has triggered a great interest 4,5 …”

Section: Introductionmentioning

confidence: 99%

An α‐variable model‐free prescribed‐time control for nonlinear system with uncertainties and disturbances

Wang

Tian

2022

Intl J Robust & Nonlinear

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In this article, an 𝛼-variable model-free prescribed-time controller (𝛼(t)-MFPTC) is proposed for a nonlinear system with uncertainties and disturbances. First, an ultra-local model is employed to formulate the plant dynamic by using input and output data. Second, to observe the state variables and compensate for the lumped uncertainties, a linear extended state observer (LESO) is designed.Then, a corresponding LESO-based 𝛼-fixed model-free controller (LESO-iPD) is proposed. Third, based on LESO-iPD, a prescribed-time sub-controller (PTC) is adopted to converge tracking error within a prescribed finite time. Furthermore, an adaptive RBF neural network compensator is constructed to approximate and compensate for LESO error. Correspondingly, an 𝛼-fixed model-free prescribed-time controller (𝛼-MFPTC) is proposed. Fourth, based on 𝛼-MFPTC, a tracking error-based 𝛼-variable method is applied to improve the controller performance, and an 𝛼-variable model-free prescribed-time controller (𝛼(t)-MFPTC) is subsequently proposed. Moreover, stability and prescribed-time convergence of closed-loop system with 𝛼(t)-MFPTC are analyzed by using the Lyapunov theorem. Ultimately, to demonstrate the performance and effectiveness of the proposed control strategy, the numerical simulation with sliding mode control, LESO-iPD, 𝛼-MFPTC, and 𝛼(t)-MFPTC and co-simulation results on quadrotor have been obtained.

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Model‐free adaptive control for a class of nonlinear systems with uniform quantizer

Cited by 18 publications

References 39 publications

A novel bipartite consensus tracking scheme for unknown nonlinear multi-agent systems: Theoretical analysis and applications

A novel bipartite consensus tracking scheme for unknown nonlinear multi-agent systems: Theoretical analysis and applications

Discrete‐time extended state observer‐based model‐free adaptive sliding mode control with prescribed performance

An α‐variable model‐free prescribed‐time control for nonlinear system with uncertainties and disturbances

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