Abstract
This work presents a novel fuzzy adaptive sliding mode-based feedback linearization controller for trajectory tracking of a flexible robot manipulator. To reach this goal, after deriving the dynamical equations of the robot, the feedback linearization approach is utilized to change the nonlinear dynamics to a linear one and find the control law. Then, the sliding mode control strategy is implemented to design a stabilizer for trajectory tracking of the flexible robot. In order to adaptively tune the parameters of the designed controller, the gradient descent approach and the chain derivative rule are employed. Moreover, the Takagi–Sugeno–Kang fuzzy system is applied to regulate the controller gains. Finally, a multiobjective particle swarm optimization algorithm is used to find the optimum fuzzy rules. The conflicting objective functions considered as the integrals of the absolute values of the state error and the control effort should be minimized, simultaneously. The simulation results illustrate the effectiveness and capability of the introduced scenario in comparison with other methods.
Highlights
In the recent years, the study of the flexible robots has been widely developed. e main reasons for this attraction could be mentioned as reaching to exact solutions and accurate performances
A novel control method as a combination of the feedback linearization scheme, sliding mode control, adaptation laws, and fuzzy systems has been introduced in this work
The feedback linearization method has been successfully utilized to change the nonlinear states of the system to their linear forms
Summary
The study of the flexible robots has been widely developed. e main reasons for this attraction could be mentioned as reaching to exact solutions and accurate performances. The sliding mode control, initially introduced by Utkin in 1997 [11] as a robust, powerful, and nonlinear approach, is utilized for the considered flexible robot manipulator. Peza-Solıs et al intended modeling a single flexible-link robot using the finite difference method and sliding mode control [17]. Two different sliding mode control approaches for the trajectory control of a flexible-link robot were investigated in the theory, simulation, and experiments by Hisseine and Lohmann [18]. E motivation of this research is to design a novel combination of the fuzzy logic, adaptation laws, and sliding mode concepts with the feedback linearization approach and the multiobjective particle swarm optimization. En, the robust and nonlinear sliding mode scheme is successfully applied to control the system states from the initial conditions to the desired values.
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