Performance Analysis of Active Disturbance Rejection Controlled Robotic Manipulator based on Evolutionary Algorithm

Abstract

Active disturbance rejection control (ADRC) is a unique control strategy that combines the effectiveness of error driven PID controller, usefulness of state observer and strength of nonlinear feedback. This control algorithm, not only actively (online) estimates but also compensates the effects of unknown internal and external disturbances, present inherently in the plant with the help of a well-tuned extended state observer (ESO). Although, the classical solution to the parameter tuning performed by using parameterization technique provides good solution, it is not optimal for having desired performance specifications. Consequently, it became imperative to have intelligent tuning technique to achieve optimized solution to parameter tuning problem. In this regards, the Evolutionary Algorithms (EA), inspired by natural system and based on swarm intelligence, are proven to be the best tool to find the optimized solution of multidimensional problems. This paper presents an application of an EA optimized ADRC controller on an uncertain 2-DoF revolute-prismatic (RP) robotic manipulator for efficient trajectory tracking and parametric robustness. Eventually, the conventional ADRC design problem is converted into a special optimization problem for finding the optimal controller tuning parameters. To accomplish this task, two well-known EA’s viz. Particle Swarm Optimization (PSO) and Bacteria Foraging Optimization (BFO) are implemented and performance of EA based controller-plant configuration is individually analyzed for each algorithm. The results of this note illustrate the benefits and weakness of the EA for implementing ADRC on MIMO systems. The performance of both the optimization techniques is compared in terms of computational time and convergence efficiency. Further, the optimized controllers are tested for the robustness in presence of disturbance and sensor noise to imitate real engineering. MATLAB based simulation results are presented and compared to demonstrate the effectiveness of both the EA's in designing an ADRC controller for improving manipulator tracking ability.