An optimal MRAC–ASMC scheme for robot manipulators based on the artificial bee colony algorithm

Abstract

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.

Path Planning of Agricultural Information Collection Robot Integrating Ant Colony Algorithm and Particle Swarm Algorithm

“…) In formula ( 9), ( , ) d i b represents the repulsion distance between the free grid and the obstacle grid. The above is an improvement on the selection probability of the ACA.However, the performance of ACA is not only affected by selection probability, but also by pheromone updates [26]. The local pheromone update of the path can make the algorithm less prone to convergence.…”