Identification-Based Robust Motion Control of an AUV: Optimized by Particle Swarm Optimization Algorithm

Abstract

In this paper, the problem of identification-based robust motion control of an Autonomous Underwater Vehicle (AUV) is investigated. The unknown system parameters are estimated by using an adaptive parameter identifier, whose gains are optimized by Particle Swarm Optimization (PSO) algorithm. Removing the trial and error procedure and ensuring the convergence property together with fast response, are the benefits of such identification scheme. On the other hand, the system uncertainties, hydrodynamic parameter variations and external disturbances which affect the identified dynamical model, are also taken into account. The cross-coupling effects between subsystems are also considered as model uncertainties. Such uncertain model is then adopted in control synthesis procedure, in the steering and diving modes. In order to achieve the robust stability and performance, two robust control strategies are presented here to solve the motion control problem. First, an Hź$H_{\infty }$ mixed sensitivity problem is formulated in which the weighting functions are selected based on an optimization criterion, by using PSO algorithm. Controller order reduction is also applied to the resulting diving and steering controllers, using the Hankel norm approximation. Then, an Adaptive Sliding Mode Control (ASMC), whose sliding surface coefficients are optimized by PSO algorithm, is developed for the identified AUV model. Possessing the robustness properties with respect to system perturbations, the developed Sliding Mode Control (SMC) removes the complexity of uncertain model representation and the limitations on choosing the weighting functions in the Hź$H_{\infty }$ control problem. The upper bounds of perturbations are not required to be known in the proposed control schemes. The simulation results are also presented to demonstrate the performance of the proposed identification-based control methods.