Adaptive Tracking Control of Surface Vessel Using Optimized Backstepping Technique

Abstract

In this paper, a tracking control approach for surface vessel is developed based on the new control technique named optimized backstepping (OB), which considers optimization as a backstepping design principle. Since surface vessel systems are modeled by second-order dynamic in strict feedback form, backstepping is an ideal technique for finishing the tracking task. In the backstepping control of surface vessel, the virtual and actual controls are designed to be the optimized solutions of corresponding subsystems, therefore the overall control is optimized. In general, optimization control is designed based on the solution of Hamilton-Jacobi-Bellman equation. However, solving the equation is very difficult or even impossible due to the inherent nonlinearity and complexity. In order to overcome the difficulty, the reinforcement learning (RL) strategy of actor-critic architecture is usually considered, of which the critic and actor are utilized for evaluating the control performance and executing the control behavior, respectively. By employing the actor-critic RL algorithm for both virtual and actual controls of the vessel, it is proven that the desired optimizing and tracking performances can be arrived. Simulation results further demonstrate effectiveness of the proposed surface vessel control.

Cooperative Multi-Vessel Systems in Urban Waterway Networks

“…In existing research, widely used control design approaches fall into either one of the following methods or a combination thereof: Proportional-Integral-Derivative [13], Lyapunov-based control design [14], sliding mode control [15], Intelligent approaches, such as fuzzy logic, neural networks, and genetic algorithms [16], Optimization-based methods [17].…”

“…Therefore, it is larger or equal to the passing time of each vessel at each block, i.e., the sum of the arrival time of vessel i at block m (s im ) and the time vessel i need to pass through block m (t im ) in (15). Equation (16) represents that, for each vessel i , there is a earliest arrival time Ea i ; t ia is determined by (17), where d im is the length of the path that vessel i need to pass through block m. Equation 18is for the sequential and nowait constraint. T i,m→n is the time needed from block m to n, which also relates to the distance between block m and n (d i,m→n ) and velocity limitations (v i,max and v i,min ), see (19).…”

Backstepping Sliding Mode Course Control for the Hovercraft With Yaw Rate Constraint and System Uncertainty

“…Due to the parameter uncertainties and high nonlinearity of underactuated surface ships, some so-called robust control algorithms have been proposed, such as sliding mode control [8][9][10], neural network control [11][12][13], robust adaptive control [14][15],  H robust control [16], backstepping techniques [17][18], fuzzy control [19][20]. In ship motion control, the above methods are very effective in dealing with environmental disturbances and model uncertainties.…”

Fuzzy Adaptive Backstepping Control of Nonlinear Uncertain Systems With Unmeasured States and Input Saturation

“…However, it also has following issues. First, the virtual controllers that repeatedly seek higher derivatives will cause computational expansion problems; the design process then requires accurate system functions, which may not be available in engineering [10]. To solve the problem of computational expansion, the existing literature proposes the dynamic surface control (DSC), which simplifies the computational complexity by passing the virtual control variable through a first-order filter [11].…”