This paper addresses the problem of high-precision trajectory tracking for a nonlinear three-link robotic manipulator operating under parametric uncertainties and external disturbances. Conventional PID and classical adaptive control methods often demonstrate limited robustness and suboptimal energy efficiency when applied to dynamically coupled multi-link systems. To overcome these limitations, a Hybrid Adaptive-Optimization Control Framework is proposed. The approach integrates Adaptive Computed Torque Control with a Modified Particle Swarm Optimization algorithm for systematic controller gain tuning. The manipulator dynamics are derived using the Euler – Lagrange formulation and implemented in MATLAB through numerical time-domain integration. Controller parameters are optimized offline using a multi-objective cost function that incorporates trajectory tracking error, control effort, and energy consumption. The optimized gains are then applied within an online adaptive compensation structure to enhance robustness against modeling uncertainties. The simulation results show that the proposed approach provides a reduction in the mean square error by approximately 26 % compared to the standard adaptive control, a reduction in the settling time, a reduction in the normalized energy consumption and a reduction in torque pulsation, which confirms the improvement in the accuracy, robustness and energy efficiency of the system.
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La Min Maung Maung
National Research University «Moscow Institute of Electronic Technology»
Moscow, Russian Federation
Lwan Moe Aung
National Research University «Moscow Institute of Electronic Technology»
Moscow, Russian Federation
