Enhanced Fireworks Algorithm-Auto Disturbance Rejection Control Algorithm for Robot Fish Path Tracking

Abstract

The robot fish is affected by many unknown internal and external interference factors when it performs path tracking in unknown waters. It was proposed that a path tracking method based on the EFWA-ADRC (enhanced fireworks algorithmauto disturbance rejection control) to obtain high-quality tracking effect. ADRC has strong adaptability and robustness. It is an effective method to solve the control problems of nonlinearity, uncertainty, strong interference, strong coupling and large time lag. For the optimization of parameters in ADRC, the enhanced fireworks algorithm (EFWA) is used for online adjustment. It is to improve the anti-interference of the robot fish in the path tracking process. The multi-joint bionic robot fish was taken as the research object in the paper. It was established a path tracking error model in the Serret-Frenet coordinate system combining the mathematical model of robotic fish. It was focused on the forward speed and steering speed control rate. It was constructed that the EFWA-ADRC based path tracking system. Finally, the simulation and experimental results show that the control method based on EFWAADRC and conventional ADRC makes the robotic fish track the given path at 2:8s and 3:3s respectively, and the tracking error is kept within plus or minus 0:09m and 0:1m respectively. The new control method tracking steady-state error was reduces by 10% compared with the conventional ADRC. It was proved that the proposed EFWA-ADRC controller has better control effect on the controlled system, which is subject to strong interference.

Modeling and Control Design for an Autonomous Underwater Vehicle Based on Atlantic Salmon Fish

“…Consequently, this makes it ineffective for the multi-objective optimization of complex MIMO control systems. To ensure the invariance against model uncertainties and disturbances, sliding mode control [18]- [20] and auto disturbance rejection control [21] schemes are developed. Moreover, fuzzy logic control [22], quantized fuzzy tracking control with event-trigger approach [23], H ∞ control [24], the line-of-sight approach [25], [26], model predictive control [27] are among a few more control techniques employed to regulate robotic fish movements.…”