Fuzzy descriptor systems and nonlinear model following control

Taniguchi, Tsuyoshi; Tanaka, Kazuo; Wang, H.O.

doi:10.1109/91.868950

Cited by 310 publications

(174 citation statements)

References 18 publications

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“…∆ x i is a vector depends on the i th operating point. hi(ξ(t)) quantifies the relative contribution of each local model to construct the global model [18]. The weighting function satisfies the property of the convex sum…”

Section: Multi-models Singular Systemmentioning

confidence: 99%

State and Unknown Inputs Estimations for Multi-Models Descriptor Systems

Mechmeche¹,

Hamdi²,

Rodrigues³

et al. 2012

AJCAM

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In this note, the problem of states and unknown inputs estimation of nonlinear descriptor system is considered.The methodology is based on the use of Proportional Integral and Unknown Input Observers. The considered nonlinear descriptor system is transformed into an equivalent multi-models form by using the Takagi-Sugeno (T-S) approach. In this paper, the design methods of both proportional integral observers and unknown inputs observers for descriptor multi-models are described in detail. Sufficient conditions of stability analysis and gain matrices determination are performed by resolving a set of Linear Matrices Inequalities (LMIs). The design method offers all the degrees of design freedom, which can be utilized to achieve various desired system specifications and performances and, thus, has great potentials in applications. A numerical example is employed to show the design procedure of these two observers and illustrate the effect of the proposed approach.

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Section: Multi-models Singular Systemmentioning

confidence: 99%

State and Unknown Inputs Estimations for Multi-Models Descriptor Systems

Mechmeche¹,

Hamdi²,

Rodrigues³

et al. 2012

AJCAM

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“…These ones are most of the time quadratic ones, nevertheless interesting results can also be found using piecewise quadratic functions (Feng 2003, Johansson et al 1999 or non quadratic Lyapunov functions (Blanco et al 2001, Guerra & Vermeiren 2004. At last, TS fuzzy descriptors have been studied for the stabilization and the observation points of view and some results are given in , Taniguchi et al 2000. Most of the time an interesting way to solve the different problems addressed is to write the obtained conditions in a LMI form (Linear Matrix Inequalities) (Boyd et al 1994).…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless using this specific descriptor structure, it is then necessary to derive conditions of stabilization. Some results can be found in , Taniguchi et al 2000. Hence, this work focuses on continuous TS fuzzy models using a descriptor form and the stabilization results will be studied through a quadratic Lyapunov function.…”

Section: Introductionmentioning

confidence: 99%

Stabilization of Fuzzy Takagi - Sugeno Descriptor Models; Application to a Double Inverted Pendulum

Guerra¹,

Delprat²,

Labiod³

2005

Cutting Edge Robotics

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“…A key challenge in the analysis of fuzzy controller is finding out the exact rule-base (Tanaka and Wang, 2001). Obviously, it is difficult for human experts to examine all the input/output data from a complex system to find the proper fuzzy rule-base (Taniguchi et al, 2000;Palm, 1992) for the system. Such an approach requires a large number of repetitions and is therefore tedious and time consuming.…”

Section: Introductionmentioning

confidence: 99%

Design of prisoner's dilemma-based fuzzy C-means computed torque controller for PUMA-560 robot manipulator

Kumar

Sudha

Pushpalatha³

2017

IJISDC

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Robots are highly nonlinear and chaotic in position control. The present paper mainly presents the position control of PUMA-560 Robot manipulator. Computed torque controller (CTC) is one of the solutions for position control of robot manipulators. The main drawback of controller is that it fails to operate under different dynamic operating conditions. To overcome this difficulty, intelligent controllers have gained importance. In this paper a novel approach, design of prisoner's dilemma-based fuzzy C-means controller to control the position of robot manipulator is presented. This controller is employed at the inputs of computed torques for obtaining the desired position. Fuzzy C-means controller with computed torques is realised by validating the clusters to choose most contributed rules. Thus the unfired rules are eliminated from the actual rule-base. Hence, a compact fuzzy controller with minimum rule-base, fuzzy C-means computed torque controller (FCMCTC), is designed. The concept of prisoner's dilemma is introduced in this paper to improve the fuzzy strategy. The interrelations between inputs and outputs of a fuzzy linguistic model are assigned using payoff matrix through prisoner's dilemma. Simulation results prove the efficacy of proposed controller when compared to proportional derivative computed torque controller (PD-CTC), normal FLC and that of the reference signal.

show abstract

Fuzzy descriptor systems and nonlinear model following control

Cited by 310 publications

References 18 publications

State and Unknown Inputs Estimations for Multi-Models Descriptor Systems

State and Unknown Inputs Estimations for Multi-Models Descriptor Systems

Stabilization of Fuzzy Takagi - Sugeno Descriptor Models; Application to a Double Inverted Pendulum

Design of prisoner's dilemma-based fuzzy C-means computed torque controller for PUMA-560 robot manipulator

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