A Self-Tuning Proportional-Integral-Derivative Controller for an Autonomous Underwater Vehicle, Based On Taguchi Method

Problem statement: Conventional Proportional-Integral-Derivative (PID) controllers exhibit moderately good performance once the PID gains are properly tuned. However, when the dynamic characteristics of the system are time dependent or the operating conditions of the system vary, it is necessary to retune the gains to obtain desired performance. This situation has renewed the interest of researchers and practitioners in PID control. Self-tuning of PID controllers has emerged as a new and active area of research with the advent and easy availability of algorithms and computers. This study discusses self-tuning (auto-tuning) algorithm for control of autonomous underwater vehicles. Approach: Self-tuning mechanism will avoid time consuming manual tuning of controllers and promises better results by providing optimal PID controller settings as the system dynamics or operating points change. Most of the self-tuning methods available in the literature were based on frequency response characteristics and search methods. In this study, we proposed a method based on Taguchi’s robust design method for self-tuning of an autonomous underwater vehicle controller. The algorithm, based on this method, tuned the controller gains optimally and robustly in real time with less computation effort by using desired and actual state variables. It can be used for the Single-Input Single-Output (SISO) systems as well as Multi-Input Multi-Output (MIMO) systems without mathematical models of plants. Results: A simulation study of the AUV control on the horizontal plane (yaw plane control) was used to demonstrate and validate the performance and effectiveness of the proposed scheme. Simulation results of the proposed self-tuning scheme are compared with the conventional PID controllers which are tuned by Ziegler-Nichols (ZN) and Taguchi’s tuning methods. These results showed that the Integral Square Error (ISE) is significantly reduced from the conventional controllers. The robustness of this proposed self-tuning method was verified and results are presented through numerical simulations using an experimental underwater vehicle model under different working conditions. Conclusion/Recommendations: By using this scheme, the PID controller gains are optimally adjusted automatically online with respect to the system dynamics or operating condition changes. This technique found to be more effective than conventional tuning methods and it is even very convenient when mathematical models of plants are not available. Computer simulations showed that the proposed method has very good tracking performance and robustness even in the presence of disturbances. The simple structure, robustness and ease of computation of the proposed method make it very attractive for real time implementation for controlling of underwater vehicle and it offers a chance to extend the same technique to the three dimensional vehicle tracking control as well.