Observer‐based adaptive fuzzy dynamic surface control of non‐linear non‐strict feedback system

Wang, Na; Tong, Shaocheng; Li, Yongming

doi:10.1049/iet-cta.2017.0517

Cited by 20 publications

(13 citation statements)

References 32 publications

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“…Xu and Sun [32] used fuzzy logic to design an intelligent approximator within a dynamic surface based controller. Dynamic surface control has been also combined with other neural networks based schemes [33][34][35][36] and with fuzzy inference systems [37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

Bessa

Brinkmann

Duecker

et al. 2018

Mathematical Problems in Engineering

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Mechatronic systems are becoming an intrinsic part of our daily life, and the adopted control approach in turn plays an essential role in the emulation of the intelligent behavior. In this paper, a framework for the development of intelligent controllers is proposed. We highlight that robustness, prediction, adaptation, and learning, which may be considered the most fundamental traits of all intelligent biological systems, should be taken into account within the project of the control scheme. Hence, the proposed framework is based on the fusion of a nonlinear control scheme with computational intelligence and also allows mechatronic systems to be able to make reasonable predictions about its dynamic behavior, adapt itself to changes in the plant, learn by interacting with the environment, and be robust to both structured and unstructured uncertainties. In order to illustrate the implementation of the control law within the proposed framework, a new intelligent depth controller is designed for a microdiving agent. On this basis, sliding mode control is combined with an adaptive neural network to provide the basic intelligent features. Online learning by minimizing a composite error signal, instead of supervised off-line training, is adopted to update the weight vector of the neural network. The boundedness and convergence properties of all closed-loop signals are proved using a Lyapunov-like stability analysis. Numerical simulations and experimental results obtained with the microdiving agent demonstrate the efficacy of the proposed approach and its suitableness for both stabilization and trajectory tracking problems.

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Section: Introductionmentioning

confidence: 99%

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

Bessa

Brinkmann

Duecker

et al. 2018

Mathematical Problems in Engineering

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“…The nonlinear uncertain system with input delay is described as follows:

\{\begin{array}{l} {\overset{x}{true}}_{i} = x_{i + 1} + f_{i} ((), x), 1 \leq i \leq n prefix- 1 \\ {\overset{x}{true}}_{n} = u ((), t prefix- τ) + f_{n} ((), x) \\ y = x_{1} \end{array}

where

x = {[x_{1}, \dots, x_{n}]}^{T} \in R^{n}

are the state vector of the considered system. u ∈ R is the system's input, and y ∈ R is the system's output.…”

Section: Problem Formulation and Fundamental Notionsmentioning

confidence: 99%

“…It is known that the control performance will be weakened, even causes system instability while the input delays are ignored during the control design process . Recently, many research achievements of compensating input delays of nonlinear systems were presented in Reference . In Reference , an effective adaptive controller was developed for nonlinear systems suffering input delays.…”

Section: Introductionmentioning

confidence: 99%

Adaptive event‐triggered neural control for nonlinear uncertain system with input constraint

Chen

Wang

et al. 2020

Intl J Robust & Nonlinear

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In this article, the issue of developing an adaptive event-triggered neural control for nonlinear uncertain system with input delay is investigated. The radial basis function neural networks (RBFNNs) are adopted to approximate the uncertain terms, where the time-varying approximation errors are considered into the approximation system. However, the RBFNNs' weight vector is extended, which may cause the computing burdens. To save network resource, the computing burden caused by the weight vector is handled with the developed adaptive control strategy. Furthermore, in order to compensate the effect of input delay, an auxiliary system is introduced into codesign. With the help of adaptive backstepping technique, an adaptive event-triggered control approach is established. Under the proposed control approach, the effect of input delay can be compensated effectively while the considered system suffered network resource constraint, and all signals in the close-loop system can be guarantee bounded. Finally, two simulation examples are given to verify the proposed control method's effectiveness. K E Y W O R D Sadaptive control, event-triggered, input delay, neural networks, nonlinear systems INTRODUCTIONActual control systems are always constituted by sensor, controller, and actuator through the shared communication network. 1-5 The network resource normally includes communication resource and computing resource. 4,5 In traditional control, the controller's output is kept transferring to actuator. 4 It may create redundant update signals that would result in the waste of the communication network. What's more, it may cause the congested network traffic when the control system suffering the network resource constraints. 6 Such issues have prompted event-triggered strategy's development.Because the event-triggered strategy has superiority in deciding which event occurred to transmit the control signal. 6,7 The network resource can be saved by reducing of the communication rate. 7 Thus, many researchers have placed great emphasis on the design of event-triggered strategy. Many research achievements have been published. [8][9][10][11] In Reference 8, the mixed event-triggered strategy was presented for nonlinear systems. In Reference 10, the improved event-triggered mechanism was presented by using an additional internal dynamic variable. The event-triggered system was analyzed [Correction added on 12 May 2020, after first online publication: Affiliations 1 and 2 and the first and second authors were interchanged and have since been corrected in this version.] Int J Robust Nonlinear Control. 2020;30:3801-3815. wileyonlinelibrary.com/journal/rnc © 2020 John Wiley & Sons, Ltd. 3801 3802 CHEN et al.through a small gain theorem tool in Reference 11. More event-triggered strategies were introduced to different control systems. [12][13][14][15][16] Noting the above research achievements show the effectiveness of event-triggered strategy in saving network resources. It is well known that nonlinear systems exist uncertain parts in r...

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“…However, all the control methods in refs [9][10][11][12][13][14][15] had inherent ''explosion of complexity'' problems. In refs, 16,17 the authors used dynamic surface control (DSC) technology to overcome the ''explosion of complexity'' problem for SISO and MIMO non-strict feedback systems control. However, output constraint problems were not considered in refs.…”

Section: Introductionmentioning

confidence: 99%

“…However, output constraint problems were not considered in refs. [9][10][11][12][13][14][15][16][17] In practical engineering, constraints exist on the system output or states. Only by limiting the output or states to the allowable range of the process, can the safety of the equipment and operators be ensured.…”

Section: Introductionmentioning

confidence: 99%

Adaptive output feedback control for a class of uncertain non-strict feedback systems with asymmetric time-varying output constraints

Wan

Huang

2014

Advances in Mechanical Engineering

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This study investigated a novel adaptive output feedback control scheme for non-strict feedback nonlinear systems with uncertainties, disturbances, and asymmetric time-varying output constraints. Because that the states of the system are unmeasurable, we used an adaptive fuzzy state observer to obtain the estimated values of the states. To make the output and tracking error satisfy their asymmetric time-varying constraints, an asymmetric time-varying barrier Lyapunov function was adopted. To overcome the “explosion of complexity” problem, we also adopted the dynamic surface control technology. The stability of the closed-loop system was proved by the Lyapunov method, and we give two simulation examples to show the effectiveness of the proposed control method.

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Observer‐based adaptive fuzzy dynamic surface control of non‐linear non‐strict feedback system

Cited by 20 publications

References 32 publications

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

Adaptive event‐triggered neural control for nonlinear uncertain system with input constraint

Adaptive output feedback control for a class of uncertain non-strict feedback systems with asymmetric time-varying output constraints

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