Model Reference Adaptive Control with Artificial Neural Network Compensator of 6 DOF Autonomous Underwater Vehicle

The perturbed and noisy environment of the underwater and complex nonlinear under-actuated dynamic system of the Autonomous Underwater Vehicle (AUV) turns out the design of the usually intelligent and adjustable controller, more challenging. In this paper, the hybrid Model Reference Adaptive Control (MRAC) along with the Artificial Neural Network (ANN) compensator for trajectory tracking of the 6 Degree of Freedom (DOF) AUV is illustrated. 4 Input-6 Output (4I6O) nonlinear under-actuated dynamic system in two individual approaches is divided into first, 4 subsystems and based on the inherent dynamics of the each subsystem, the partial or inverse linearization technique is employed for each one, and second, the coupled linearized model. The stability of the each closed-loop subsystems, and hence the complete controlled model is insured according to the Lyapounve’s stability theory for both approaches. To further increasing the robustness of the closed loop system in such agitated environment, an ANN compensator benefiting online backpropagation learning algorithm to tune the network's parameters is incorporated with each controllers. The results of the simulations of the hybrid MRAC along with ANN compensator in Matlab Simulink environment, clearly indicates the outperformance of the ANN compensated control method versus its non-ANN compensated counterpart in terms of increasing the robustness as well as more accurate trajectory tracking performance of the control system subjected to the continual applied noises for both coupled and decoupled dynamical systems.