Double-Loop PID-Type Neural Network Sliding Mode Control of an Uncertain Autonomous Underwater Vehicle Model Based on a Nonlinear High-Order Observer with Unknown Disturbance

Abstract

An unknown nonlinear disturbance seriously affects the trajectory tracking of autonomous underwater vehicles (AUVs). Thus, it is critical to eliminate the influence of such disturbances on AUVs. To address this problem, this paper proposes a double-loop proportional–integral–differential (PID) neural network sliding mode control (DLNNSMC). First, a double-loop PID sliding mode surface is proposed, which has a faster convergence speed than other PID sliding mode surfaces. Second, a nonlinear high-order observer and a neural network are combined to observe and compensate for the nonlinear disturbance of the AUV system. Then, the bounded stability of an AUV closed-loop system is analyzed and demonstrated using the Lyapunov method, and the time-domain method is used to verify that the velocity- and position-tracking errors of AUVs converge to zero exponentially. Finally, the radial basis function (RBF) neural network PID sliding mode control (RBFPIDSMC) and the RBF neural network PID sliding mode control (RBFPDSMC) are compared with this method in two trajectory tracking control simulation experiments. In the first experiment, the average Euclidean distance of the position-tracking error for this method was reduced by approximately 73.6% and 75.3%, respectively, compared to those for RBFPDSMC and RBFPIDSMC. In the second experiment, the average Euclidean distance of the position tracking error for this method was reduced by approximately 86.8% and 88.8%, respectively. The two experiments showed that the proposed control method has a strong anti-jamming ability and tracking effect. The simulation results obtained in the Gazebo environment validated the superiority of this method.

Trajectory Tracking and Obstacle Avoidance of Robotic Fish Based on Nonlinear Model Predictive Control

“…In this context, the initial state of the robotic fish is defined as ξ = [0 0 π/2 8 5] T . The NMPC controller has a state variable dimension of n s = 5, a prediction horizon of N p = 10, a control horizon of N c = 3, and a sampling time of T = 0.05 s. The safety margin between the robotic fish and obstacles is set at 0.3 m. The centroids of obstacles 1, 2, and 3 are located at coordinates (5,8), (2,5), and (5, 2) respectively, with each obstacle having a radius of 0.2 m.…”

“…Up to this point, the predominant techniques employed for trajectory tracking of underwater robots consist of traditional proportional integral derivative (PID) control [5,6], sliding mode control, fuzzy control [7,8], neural network control [9,10], intelligent control [11,12], and the pure pursuit (PP) method. The PID method, being a highly prevalent control strategy, has been extensively employed in the domain of underwater robot trajectory tracking.…”

Selection of the Depth Controller for the Biomimetic Underwater Vehicle

“…The first one is about a PID controller that calculates the error value e(k) as the difference between the set depth value and the value received from the depth sensor and applies a correction based on proportional, integral and derivative terms (denoted P, I, and D, respectively). Hence the name [22,23]. The action of the PID controller is described by the following formula presented in the discrete form:…”

Observer-Based Adaptive Control for Trajectory Tracking of AUVs with Input Saturation

“…A high-gain observer is employed for unknown internal and external disturbances, which is compensated for by output feedback control law [18]. An extended states observer is developed for system uncertainties in path-following control of under-actuated AUVs [19,20]. However, it may cause differential peak problems due to high gain, which affects control performance.…”