Fuzzy Optimized MFAC Based on ADRC in AUV Heading Control

Abstract

The control issue of Autonomous Underwater Vehicles (AUV) is very challenging since the precise mathematical model of AUV is hard to establish due to its strong coupling and time-varying features. Meanwhile, AUV movement is easily interfered with by ocean currents and waves, causing anti-interference performance of traditional Proportional-Integral-Derivative (PID) control to be unsatisfactory. Aiming to solve those problems, an algorithm of fuzzy optimized model-free adaptive control (MFAC) based on auto-disturbance rejection control (ADRC) was proposed and used in AUV heading control. The MFAC is used to overcome the difficulty with establishing a precise mathematical model, and the ADRC is introduced to handle the interference of currents and waves. In this paper, MFAC and ADRC are combined. First, the MFAC is performed based only on the I/O data of the controlled object, which is simple to implement with low calculation complexity and strong robustness. Then, a tracking differentiator (TD) is employed to track the input signal to overcome the antinomy of rapidity and hypertonicity in MFAC. After that, an extended-state observer (ESO) is added to control the variables of MFAC to estimate all the disturbances, which can greatly improve the anti-interference ability of the system. Due to the complexity and diversity of the marine environment, a fuzzy optimized MFAC based on ADRC is proposed to improve the adaptability of AUV to the marine environment. Simulations and experiments were carried out to verify the control effect of this algorithm in complex sea conditions.

3-Dimensional Modeling and Attitude Control of Multi-Joint Autonomous Underwater Vehicles

“…Rodriguez et al [20] combine sliding mode control with adaptive control and propose a sliding mode adaptive controller, which is compared with non-adaptive control and PD control to verify the effectiveness of the controller. References [21][22][23][24][25] have made improvements based on the active disturbance rejection controller, combining sliding mode controller, self-searching optimal algorithm, or other methods, to improve the accuracy and anti-interference performance of the AUV motion control. In addition to the above methods, in recent years, scholars are also studying the application of reinforcement learning [26,27] and artificial intelligence algorithms [28] in the field of AUV control.…”

Antidisturbance Control for AUV Trajectory Tracking Based on Fuzzy Adaptive Extended State Observer

“…During the last several decades, various control methods have been widely researched for trajectory tracking control problem of AUVs, such as the proportional-derivative (PD) control [ 5 ], the proportional integral derivative (PID) control [ 6 ], backstepping control (BSC) [ 7 ], sliding mode control (SMC) [ 8 ], fuzzy logic control (FLC) [ 9 ], neural-network-based control (NNC) [ 10 ], predictive control [ 11 ], adaptive control [ 12 ] and active disturbance rejection control (ADRC) [ 13 ]. Among them, the PD control and the PID control are the most used methods in practice due to their design simplicity and fine performance.…”

MISO Model Free Adaptive Control of Single Joint Rehabilitation Robot Driven by Pneumatic Artificial Muscles

“…Compared with other model free control methods, MFAC algorithm has the following advantages. The training of neural network model needs huge data, so it is a challenge for data acquisition and processing [13]. PID control belongs to the category of model free control, but for the system of high nonlinearity and time-variability, the effect of PID control is not ideal [14].…”