Haichao Zhang

Warehouse logistics systems increasingly require rapid and efficient material transfer. Wheeled mobile robots (WMRs), as flexible automated handling equipment, are being widely adopted for warehouse transport. This paper addresses the trajectory-tracking problem of a WMR subject to external disturbances in warehouse handling scenarios and proposes an adaptive trajectory tracking control method with a convergence time that can be specified in advance. A Lyapunov-based sufficient condition is presented to support the explicit design of the convergence-time parameter. An intermediate variable is introduced into the kinematic error system of a differential-drive WMR, and lateral-error feedback is incorporated into the virtual velocity control law. The proposed design drives the longitudinal position error and heading error into a computable bounded set within a predefined time while maintaining a bounded lateral error. With the left and right wheel torques as servo inputs, an adaptive control law is designed from a first-order WMR velocity dynamics model. By adaptively compensating for the equivalent external disturbances caused by friction, ground irregularities, and related effects online, the actual linear and angular velocities rapidly track their virtual commands. It is further shown that the velocity-tracking error enters and remains in a bounded set within a predefined time, which establishes practical predefined-time stability of the complete closed-loop system. Finally, comparative numerical simulations and hardware experiments are conducted. The results show that the proposed method provides favorable trajectory-tracking accuracy and convergence speed, thereby supporting rapid motion and stable servo operation of warehouse-handling WMRs.

This study proposes a predefined-time sliding mode control framework for Lagrangian systems subject to input constraints, which is singularity-free and chattering-free. The favorable performance stems from the use of a state-dependent variable exponential approach and a smooth hyperbolic tangent function. A smooth sliding surface based on a state-dependent variable exponential predefined-time formulation is established. A smooth reaching law with state-dependent variable exponent is designed and incorporated into the controller synthesis, to suppress chattering and ensure robust stabilization of the closed loop dynamics within a predefined time. To address saturation nonlinearity, an auxiliary predefined-time subsystem is devised. Through rigorous theoretical analysis, the proposed scheme is devoid of singularities and guarantees predefined-time convergence of the controlled system. Finally, the developed control methodology is implemented in a spacecraft attitude regulation problem, and numerical simulations confirm its effectiveness.

This article addresses the optimal control problem for a class of continuous-time nonlinear systems with time-varying bounded disturbances. A novel approximate optimal control policy that incorporates a continuous robust control command is developed within the framework of adaptive dynamic programming. It makes the controlled system robust to the bounded time-varying disturbance rather than just the disturbance that vanishes as the system state converges. Moreover, an improved concurrent learning neural network (NN) weight updating algorithm that involves a switching mechanism is presented, and it can work without persistent/finite assumptions. Then, with the uniformly ultimately bounded stability guaranteed by Lyapunov theory, the closed-loop controlled system achieves approximate optimality and strong robustness. The superiority and effectiveness of the suggested control approach have been demonstrated via robotic trajectory tracking experiments.

This paper presents a unified Lyapunov-based predefined-time stability theorem that includes three sufficient conditions. The standard theoretical analysis method for achieving predefined-time stability of non-linear systems using this theorem is provided within the framework of Lyapunov theory. The developed Lyapunov-based theorem facilitates the establishment of equivalence between the existing Lyapunov theorems concerning predefined-time stability. Furthermore, when the presented sufficient conditions are relaxed, the predefined-time stability conclusion for non-linear systems degenerates into a finite-time one. Consequently, a standard non-singular sliding mode control framework based on the unified Lyapunov-based theorem is developed for a Lagrangian system to ensure its predefined-time stability. Exemplary numerical simulation results are subsequently given, in order to illustrate the convergence behavior of the system states and confirm that the controlled systems are predefined-time stable.

This brief gives a set of unified Lyapunov stability conditions to guarantee the predefined-time/finite-time stability of a dynamical systems. The derived Lyapunov theorem for autonomous systems establishes equivalence with existing theorems on predefined-time/finite-time stability. The findings proposed herein develop a nonsingular sliding mode control framework for an Euler-Lagrange system to analyze its stability, and its upper bound for the settling time can be arbitrarily determined a priori through predefined time constant.

During the operation of automated guided vehicles (AGVs) at automated terminals, the occurrence of conflicts and deadlocks will undoubtedly increase the ineffective waiting time of AGVs, so there is an urgent need for path planning and tracking control schemes for autonomous obstacle avoidance in AGVs. An innovative AGV autonomous obstacle avoidance path planning and trajectory tracking control scheme is proposed, effectively considering static and dynamic obstacles. This involves establishing three potential fields that reflect the influences of obstacles, lane lines, and velocities. These potential fields are incorporated into an optimized model predictive control (MPC) cost function, leveraging artificial potential fields to ensure effective obstacle avoidance. To enhance this system’s capability, a fuzzy logic system is designed to dynamically adjust the weight coefficients of the hybrid artificial potential field model predictive controller, strengthening the autonomous obstacle avoidance capabilities of the AGVs. The tracking control scheme includes a fuzzy linear quadratic regulator based on a fuzzy logic system, a dynamics model as a lateral controller, and a PI controller as a longitudinal tracker to track the pre-set trajectory and speed autonomously. Multi-scenario simulation tests demonstrate the effectiveness and rationality of our autonomous obstacle-avoidance control scheme.

The attitude tracking control problem of rigid spacecraft subjected to external disturbance and prescribed performance is studied. A fast fixed‐time stability theorem is firstly developed. A continuous disturbance observer is then designed with its estimation error can be driven into a tiny neighborhood containing zero within fixed time. Moreover, the spacecraft attitude tracking error constrained by prescribed performance function is transformed into an unconstrained dynamics. A disturbance observer‐based prescribed performance tracking controller is designed to stabilize the unconstrained dynamics within fixed time. It also means that the attitude tracking errors meet the prescribed performance as the stabilization of the unconstrained dynamics. Spacecraft simulation examples are finally given to validate the effectiveness of the proposed controller.