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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.

In this paper, an optimal multi-adaptive robust controller is proposed for robot manipulators using the gradient descent method and artificial bee colony. At first, Model Reference Adaptive Control (MRAC) and Sliding Mode Control (SMC) are separately designed for handling a robot manipulator with two revolute (2R) joints. Further, the coefficients of the sliding surfaces and control efforts are updated via a suitable adaptive mechanism based on the gradient descent method. In addition, in order to minimize the weighted summation of Integral Time Absolute Error (ITAE), some constant parameters of the controllers are determined by the artificial bee colony optimization algorithm. Finally, comparisons and performance tests are illustrated to demonstrate the effectiveness and superiority of the proposed control scheme for trajectory tracking in comparison with other traditional approaches.

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