Direct adaptive self-structuring fuzzy controller for nonaffine nonlinear system

Park, Jang-Hyun; Park, Gwi-Tae; Kim, Seong-Hwan; Moon, Chae-Joo

doi:10.1016/j.fss.2005.01.003

Cited by 115 publications

(69 citation statements)

References 18 publications

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“…Takagi-Sugeno 퍼지논리계통을 이용한 적응 퍼지 제어 계통 [7]을 통하여 이 문제를 해결하고자 한다. 제안한 제어기법 의 유용성을 확인하고자 무인 자율 이동 보트의 축소 모델 인 모델 보트에 대하여 시뮬레이션 연구를 수행하였다.…”

Section: 본 논문에서는 비어파인 비선형 계통을 제어할 수 있는unclassified

An Adaptive Fuzzy Control System for the Speed Control of the Autonomous Surface Vehicle with Nonaffine Nonlinear Dynamics

Park¹,

Lee²

2012

Journal of Institute of Control, Robotics and Systems

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Abstract:In this paper, an adaptive fuzzy control system is proposed for the speed control of the ASV (Autonomous Surface Vehicle) with nonaffine nonlinear system dynamics. We consider the turning speed of the screw propeller to be the control input instead of thrust so that we do not have to know the exact function between turning speed and thrust. But in this case, the ASV becomes a nonaffine nonlinear system because thrust is a nonlinear function of the turning speed. To solve this problem, we propose a Takagi-Sugeno fuzzy-model-based control system and simulation studies are performed. Simulation results show the effectiveness of the proposed control scheme.

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Section: 본 논문에서는 비어파인 비선형 계통을 제어할 수 있는unclassified

An Adaptive Fuzzy Control System for the Speed Control of the Autonomous Surface Vehicle with Nonaffine Nonlinear Dynamics

Park¹,

Lee²

2012

Journal of Institute of Control, Robotics and Systems

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“…The previous research concentrated mainly on affine systems and there are limited studies about nonaffine ones. Some of these studies have used neural networks (NNs) [3,4] or fuzzy logic systems [5,6] for approximating the ideal control input. According to some other researchers, the original system in nonaffine form is transformed into an affine one, and then the obtained control input for this affine system can be applied directly to the transformed system [7,8].…”

Section: Introductionmentioning

confidence: 99%

Adaptive control of pure-feedback systems in the presence of parametric uncertainties

Asadi¹,

Shandiz²

2017

Turk J Elec Eng & Comp Sci

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Abstract:The purpose of this study is to investigate an adaptive control approach for completely nonaffine pure-feedback systems with linear/nonlinear parameterization. In this approach, the parameter separation technique and the idea of the positive function of linearly connected parameters are coupled effectively with the combination of backstepping and time-scale separation. Subsequently, a fast dynamical equation is derived from the original subsystem, where the solution is sought to approximate the corresponding ideal virtual/actual control inputs. Furthermore, the adaptation law of unknown parameters can be derived based on Lyapunov theory in the backstepping technique and there is no need to design a state predictor for this purpose. Therefore, it results in higher accuracy and avoids complexity. The closed-loop stability and the state regulation of these systems are proved. Finally, the simulation results are provided to demonstrate the effectiveness of the proposed approach.

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“…Some relevant adaptive methods have been proposed in [1,5,[7][8][9][10][11][12][13][14]. However, they assume a known model or at least a known structure of the plant model.…”

Section: Introductionmentioning

confidence: 99%

Evaluation-Function-based Model-free Adaptive Fuzzy Control

Naba

2016

J. Eng. Technol. Sci.

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Abstract. Designs of adaptive fuzzy controllers (AFC) are commonly based on the Lyapunov approach, which requires a known model of the controlled plant. They need to consider a Lyapunov function candidate as an evaluation function to be minimized. In this study these drawbacks were handled by designing a model-free adaptive fuzzy controller (MFAFC) using an approximate evaluation function defined in terms of the current state, the next state, and the control action. MFAFC considers the approximate evaluation function as an evaluative control performance measure similar to the state-action value function in reinforcement learning. The simulation results of applying MFAFC to the inverted pendulum benchmark verified the proposed scheme's efficacy. Keywords: adaptive fuzzy control; evaluation function; Lyapunov approach; modelfree adaptive control; reinforcement learning. IntroductionMany designs of adaptive fuzzy controllers (AFC) are based on the Lyapunov approach, assuming the existence of a Lyapunov function for the control problem to be solved. The Lyapunov function getting smaller is seen as an indication of better control performance. The most important stage in the Lyapunov approach is to ensure that the first derivative of the Lyapunov function candidate (LFC) is either negative-definite or semi-definite [1][2][3][4]. This stage requires a known plant model or, at least, a known plant model structure.The regular approach in model-based design of AFCs is that the engineer a priori defines the LFC simply in terms of the distance between the current state and the goal state. A smaller error is always assumed to indicate better control performance. However, in many control problems a smaller error does not indicate better control performance, i.e. it is not always evaluative. Thus, action selection based on smaller errors can misdirect and lead to non-goal states. Generally speaking, errors are more instructional than evaluative [5]. For this reason, they are not universally suitable as an evaluative measure of control performance. This is a flaw in model-based design of AFCs.

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Direct adaptive self-structuring fuzzy controller for nonaffine nonlinear system

Cited by 115 publications

References 18 publications

An Adaptive Fuzzy Control System for the Speed Control of the Autonomous Surface Vehicle with Nonaffine Nonlinear Dynamics

An Adaptive Fuzzy Control System for the Speed Control of the Autonomous Surface Vehicle with Nonaffine Nonlinear Dynamics

Adaptive control of pure-feedback systems in the presence of parametric uncertainties

Evaluation-Function-based Model-free Adaptive Fuzzy Control

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