A fuzzy-PDC-based control for robotic systems

Lin, Chih‐Min; Mon, Yi-Jen

doi:10.1016/s0020-0255(01)00105-0

Cited by 6 publications

(6 citation statements)

References 11 publications

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“…TS fuzzy model, which is also called Type-III fuzzy model, is a fuzzy dynamic model that captures the dynamics of the nonlinear system as a set of local linear models smoothly connected by fuzzy membership functions. Interestingly, TS fuzzy models are universal function approximators that can approximate any smooth nonlinear function to the required level of preciseness in any convex compact surface (Lin and Mon, 2001). Moreover, TS model based fuzzy control offers systematic tools for stability analysis and controller design (Xu et al, 2013).…”

Section: Ts Fuzzy Control For Stabilization and Trackingmentioning

confidence: 99%

Nonlinear control design using Takagi-Sugeno fuzzy applied to under-actuated visual servo system

Jonnalagadda

Elumalai

Singh

et al. 2020

Transactions of the Institute of Measurement and Control

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This paper presents the Takagi-Sugeno (TS) fuzzy control design for nonlinear stabilization and tracking control of a ball on plate system. To deal with the plant nonlinearity and the fuzzy convergence issue, we formulate the parallel distributed compensator (PDC) TS fuzzy model to characterize the global behaviour of the nonlinear system and synthesize a feasible control framework using a velocity compensation scheme. The nonlinear dynamics of the ball on plate system is obtained using the Euler-Lagrangian energy based approach. To identify the moving objects in the video stream, a background subtraction algorithm using thresholding technique is formulated. Moreover, the stability analysis of the TS fuzzy control is reduced to linear matrix inequality (LMI) problem and solved using the Lyapunov direct method. The potential benefits of the proposed control structure for real time test cases are experimentally assessed using hardware in loop (HIL) testing on a ball on plate system. Experimental results substantiate that the TS fuzzy scheme can significantly improve not only the tracking performance but also the robustness of the closed loop system.

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Section: Ts Fuzzy Control For Stabilization and Trackingmentioning

confidence: 99%

Nonlinear control design using Takagi-Sugeno fuzzy applied to under-actuated visual servo system

Jonnalagadda

Elumalai

Singh

et al. 2020

Transactions of the Institute of Measurement and Control

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“…Once the fuzzy system is found, its stability needs to be checked based on the following theorem (Lin and Mon, 2001) that provides a sufficient stability condition:…”

Section: Control Approachmentioning

confidence: 99%

“…The concept of the PDC is that for each sub-model of the T-S fuzzy model a linear state feedback controller is designed (Lin and Mon, 2001; Tseng et al, 2001). Each linear controller is a consequence or a conclusion of a specified fuzzy rule.…”

Section: Control Approachmentioning

confidence: 99%

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Optimal fuzzy tracking control with obstacles avoidance for a mobile robot based on Takagi-Sugeno fuzzy model

Akka

Khaber

2018

Transactions of the Institute of Measurement and Control

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This paper presents a trajectory tracking control approach for a mobile robot in the presence of static obstacles on the reference trajectory. The tracking control is developed based on the fuzzy parallel distributed compensation scheme where the linear quadratic regulator is used as a state feedback controller for each subsystem. The obstacle avoidance task is accomplished by applying a linear quadratic regulator on the overall open loop Takagi-Sugeno (T-S) fuzzy system, in which the weighting matrices are adjusted dynamically using a fuzzy controller. A fuzzy fusion controller is designed to combine the tracking and the obstacle avoidance velocities in order to perform both tasks simultaneously. Simulation results illustrate the effectiveness of the proposed control strategy.

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“…10 and 11 that it is very difficult to apply this approach when we have a great numbers of rules describing the system (Lin and Mon, 2001).…”

Section: Theoremmentioning

confidence: 99%

Thermal Control of a Greenhouse by Variation in Ventilation Rate using a Fuzzy Parallel Distributed Compensation Controller with an RST Regulator in Each Rule

K.¹

2012

American Journal of Applied Sciences

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Problem statement:The greenhouse has uncontrollable inputs that affect its climate, which arises the difficulty to regulate its inside temperature. The solution can be found using a multi-system approach like the Takagi-Sugeno System from which a design of a Parallel Distributed Compensation (PDC) controller is performed. However, a stability problem arises and was negotiatesd in general based on a Lyapunov criterion. The latter isn't appropriate in our case because of the great number of rules describing the greenhouse. Approach: An alternative solution is proposed using a PDC controller with a local RST regulator in each rule. The synthesis of each one is determined using pole placement avoiding the cross-coupling (that may cause instability) between the local regulators and the sub-models related to differents rules. Results: The proposed fuzzy controller was applied to an experimental greenhouse and was able to lead the inside temperature to the desired value despite the externals perturbations. Conclusion: The presented study offer a simple solution to the stability problem when using PDC controller and so can be mush more implementable than the others stabilization methods presented in the literature. In the agriculture field, it can replace the on-off control action that is widely used in the greenhouses because of the processus complexity.

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A fuzzy-PDC-based control for robotic systems

Cited by 6 publications

References 11 publications

Nonlinear control design using Takagi-Sugeno fuzzy applied to under-actuated visual servo system

Nonlinear control design using Takagi-Sugeno fuzzy applied to under-actuated visual servo system

Optimal fuzzy tracking control with obstacles avoidance for a mobile robot based on Takagi-Sugeno fuzzy model

Thermal Control of a Greenhouse by Variation in Ventilation Rate using a Fuzzy Parallel Distributed Compensation Controller with an RST Regulator in Each Rule

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