Jiawei Li’s research while affiliated with Huazhong Agricultural University and other places

To address the path planning problem for automated guided vehicles (AGVs) in challenging and complex industrial environments, a hybrid optimization approach is proposed, integrating a Kalman filter with grey wolf optimization (GWO), as well as incorporating partially matched crossover (PMX) mutation operations and roulette wheel selection. Paths are first optimized using GWO, then refined with Kalman filter corrections every ten iterations. Moreover, roulette wheel selection guides robust parent path selection, while an elite strategy and partially matched crossover (PMX) with mutation generate diverse offspring. Extensive simulations and experiments were carried out under a densely packed goods scenario and complex indoor layout scenario, within a fully automated warehouse environment. The results showed that this hybrid method not only enhanced the various optimization metrics but also ensured more predictable and collision-free navigation paths, particularly in environments with complex obstacles. These improvements lead to increased operational efficiency and safety, highlighting the method’s potential in real-world applications.

In this study, we present a novel dual-loop robust trajectory tracking framework for autonomous underwater vehicles, with the objective of enhancing their performance in underwater searching tasks amidst oceanic disturbances. Initially, a real-world AUV experiment is conducted to validate the efficacy of a cross-rudder AUV configuration in maintaining sailing angle stability during the diving stage, which exhibits a strong capability for straight-line sailing. Building upon the experimental findings, we introduce a state-transform-model predictive guide law to compute the desired velocity for the dynamics loop. This guide law dynamically adjusts the controller across varying depths, thereby reducing model predictive control (MPC) computation while optimizing timing without compromising precision or convergence speed. Subsequently, we incorporate a sliding mode controller with a prescribed disturbance observer into the velocity control loop to concurrently enhance the robustness and convergence rate of the system. This innovative amalgamation of controllers significantly improves tracking precision and convergence rate, while also alleviating the computational burden—a pervasive challenge in AUV MPC control. Finally, various condition simulations are conducted to validate the robustness, effectiveness, and superiority of the proposed method. These simulations underscore the enhanced performance and reliability of our proposed trajectory tracking framework, highlighting its potential utility in real-world AUV applications.

To address the search-and-docking problem in multi-stage prescribed performance switching (MPPS) scenarios, this paper presents a novel compound control method for three-dimensional (3D) underwater trajectory tracking control of unmanned underwater vehicles (UUVs) subjected to unknown disturbances. The proposed control framework can be divided into two parts: kinematics control and dynamics control. In the kinematics control loop, a novel parallel model predictive control (PMPC) law is proposed, which is composed of a soft-constrained model predictive controller (SMPC) and hard-constrained model predictive controller (HMPC), and utilizes a weight allocator to enable switching between soft and hard constraints based on task goals, thus achieving global optimal control in MPPS scenarios. In the dynamics control loop, a finite-time terminal sliding mode control (FTTSMC) method combining a finite-time radial basis function neural network adaptive disturbance observer (RBFNN-FTTSMC) is proposed to achieve disturbance estimation and fast convergence of velocity tracking errors. The simulation results demonstrate that the proposed PMPC-FTTSMC approach achieved an average improvement of 33% and 80% in the number of iterations compared with MPC with sliding mode control (MPC-SMC) and traditional MPC methods, respectively. Furthermore, the approach improved the speed of response by 35% and 44%, respectively, while accurately achieving disturbance observation and enhancing the system robustness.