Direct adaptive fuzzy control for a class of MIMO nonlinear systems

Labiod, Salim; Guerra, Thierry Marie

doi:10.1080/00207720701500583

Cited by 87 publications

(38 citation statements)

References 18 publications

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“…(28) with respect to the adjustable parameters θ . Accordingly, by applying the gradient method [7,29], the minimizing trajectory θ (t) is generated by the following differential equation:θ…”

Section: Theorem 1 Consider a Class Of Underactuated Systems Given Bymentioning

confidence: 99%

“…the parameter adaptive laws are designed in such a way as to ensure the convergence of a Lyapunov function. However, for an effective adaptation, it is more judicious to directly base the parameter adaptation process on the identification error between the unknown function and its adaptive fuzzy approximation [29]. This paper presents a direct decoupling adaptive fuzzy control scheme for a class of UMSs with 2 DOF.…”

Section: Introductionmentioning

confidence: 99%

“…This paper presents a direct decoupling adaptive fuzzy control scheme for a class of UMSs with 2 DOF. The proposed adaptation idea is based on the results in [29]. In this direct approach, since fuzzy systems are used to approximate unknown ideal controllers, the adjustable parameters of the used fuzzy systems are updated using a gradient descent algorithm that is designed to minimize the error between the unknown ideal controller and the fuzzy controller.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Direct adaptive fuzzy sliding mode decoupling control for a class of underactuated mechanical systems

Nafa¹,

Labiod²,

Chekireb³

2013

Turk J Elec Eng & Comp Sci

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Abstract:Motivated by the dynamic characteristics of underactuated mechanical systems with 2 degrees of freedom, a decoupling adaptive fuzzy sliding mode decoupling controller (DAFSMDC) is presented in this paper. By exploiting the universal approximation property of fuzzy logic systems and the sliding mode control method, this paper proposes a new decoupling strategy of the system into 2 second-order subsystems and introduces an adaptive control algorithm that guarantees the convergence of both subsystems. Since fuzzy systems are used to approximate an unknown ideal controller, the adjustable parameters of the used fuzzy systems are updated using a gradient descent algorithm that is designed to minimize the error between the unknown ideal controller and the fuzzy controller. Based on Lyapunov stability theory, proofs and conditions are then given to ensure the stability of the closed-loop system. Two examples are provided to illustrate the effectiveness and potential of the DAFSMDC technique for the stabilization of underactuated mechanical systems.

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Section: Theorem 1 Consider a Class Of Underactuated Systems Given Bymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Direct adaptive fuzzy sliding mode decoupling control for a class of underactuated mechanical systems

Nafa¹,

Labiod²,

Chekireb³

2013

Turk J Elec Eng & Comp Sci

Self Cite

View full text Add to dashboard Cite

show abstract

“…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.…”

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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“…Although fuzzy control theory has been successfully employed in many control applications [1][2][3][4][5][6][7][8][9] , its control strategies were mostly designed for SISO systems, and for few MIMO systems with only simulation studies. Lian and Lin 10 proposed a mixed fuzzy controller (MFC) for a MIMO two-link robotic system to overcome the coupling effects between the degrees of freedom.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of an Integrated Fuzzy Logic Controller for a Multi-input Multi-output System

Patil¹,

Bhaskar²,

Sudheer³

2011

DSJ

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The design and real time implementation of an integrated fuzzy logic controller (IFLC) for a multipleinput multiple-output (MIMO) system is presented. The design of IFLC for an uncoupled MIMO system has been discussed. This study develops a combination of fuzzy and PID controllers (PIDC) to improve the control performance of a two-input-two-output (TITO: angular position, and rotational speed) system. These parameters play a vital role in radar-tracking system for military applications. To verify the applicability of proposed controller, two-motor unit plant along with indigenously designed multi-channel analog interface board of 16-bit precision is used. The proposed MIMO control system is interfaced to a PC through its parallel port. The performance of the system is studied by subjecting it to various standard test signals. The IFLC performs better than the other two controllers in tracking the input command for linear as well as nonlinear inputs such as step, square, triangular, and sine waves is observed.

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Direct adaptive fuzzy control for a class of MIMO nonlinear systems

Cited by 87 publications

References 18 publications

Direct adaptive fuzzy sliding mode decoupling control for a class of underactuated mechanical systems

Direct adaptive fuzzy sliding mode decoupling control for a class of underactuated mechanical systems

Evaluation-Function-based Model-free Adaptive Fuzzy Control

Design and Implementation of an Integrated Fuzzy Logic Controller for a Multi-input Multi-output System

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