This article proposes a novel hybrid metaheuristic technique based on nonsingular terminal sliding mode controller, time delay estimation method, an extended grey wolf optimization algorithm and adaptive super twisting control law. The fast convergence is assured by nonsingular terminal sliding mode controller owing to its inherent nonlinear property and no prior knowledge of the robot dynamics is required due to time delay estimation. The proposed extended grey wolf optimization algorithm determines an optimal approximation of the inertial matrix of the robot. Moreover, adaptive super twisting control based on the Lyapunov approach overcomes the disturbances and compensate the higher dynamics not achievable by the time delay estimation method. First, the fast nonsingular terminal sliding mode controller relying on time delay estimation is designed and is combined with super twisting control for chattering attenuation. The constant gain matrix of the time delay is determined by the proposed extended grey wolf optimization algorithm. Second, an adaptive law based on Lyapunov stability theorem is designed for improving tracking performance in the presence of uncertainties and disturbances. The novelty of the proposed method lies in the adaptive law where the prior knowledge of parametric uncertainties and disturbances is not needed. Moreover, the constant gain matrix of time delay estimation method is obtained using the proposed algorithm. The control method has been tested in simulation on a 3-degrees of freedom robotic manipulator in trajectory tracking mode in the presence of control disturbances and uncertainties. The results obtained confirmed the effectiveness, robustness and the superior precision of the proposed control method compared to the classical ones.
Bio-inspired optimization algorithms have recently attracted much attention in the control community. Most of these algorithms mimic particular behaviors of some animal species in such a way that allows solving optimization problems. The present paper aims at applying three metaheuristic methods for optimizing Fuzzy Logic Controllers used for quadrotor attitude stabilization. The investigated methods are Particle Swarm Optimization (PSO), BAT algorithm and Cuckoo Search (CS). These methods are applied to find the best output distribution of singleton membership functions of the Fuzzy Controllers. The quadrotor control requires measured responses, therefore, three objective functions are considered: Integral Squared Error, Integral Time-weighted Absolute Error and Integral Time-Squared Error. These metrics allow performance comparison of to compare the controllers in terms of tracking errors and speed of convergence. The simulation results indicate that BAT algorithm demonstrated higher performance than both PSO and CS. Furthermore, BAT algorithm is capable of offering 50% less computation time than CS and 10% less time than PSO. In terms of fitness, BAT algorithm achieved an average of 5% better fitness than PSO and 15% better than Cuckoo Search. According to these results, the BAT-based Fuzzy Controller exhibits superior performance compared with other algorithms to stabilize the quadrotor.
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