Data-driven controller adaptation and parameter estimation in autonomous underwater vehicles by genetic algorithm enhanced unfalsification approach

Abstract The paper presents an enhanced method for unknown parameter estimation and nonlinear controller adaptation that combines the concept of unfalsification with the genetic algorithm (GA). This approach is based on the measured data and employs a bank of nonlinear controllers designed to dynamically adjust to the system’s evolving conditions. The controllers in the bank can be switched to meet the system’s requirements. This method is applied to an autonomous underwater vehicle (AUV) with uncertain parameters. Using the unfalsification method, these uncertain parameters are estimated, and a suitable controller is selected from the bank to guide the AUV along a desired trajectory. Additionally, an artificial intelligence technique, such as the GA, is employed to update the controller bank, resulting in versatile and optimised candidates. The simulation results obtained in the MATLAB/Simulink environment show that in the environment considered in this paper, the Adaptive Unfalsification algorithm in conjunction with GA estimates the unknown parameter values better than the sole GA-optimised values. Also, the convergence of the actual trajectory of the AUV using the Adaptive Unfalsification algorithm in conjunction with GA is faster and better than the sole GA-optimised algorithm. Furthermore, a survey of experimental results from established literature is included to evaluate the practical implementation of the proposed design, which concludes that within a reasonable time, the Adaptive Unfalsification algorithm in conjunction with GA can be implemented in commercially available processors.