Robust tracking control for magnetic wheeled mobile robots using adaptive dynamic programming

Observer-based event-triggered recursive optimal tracking control for a class of strict-feedback nonlinear systems

In this paper, an innovative event-triggered optimal tracking control algorithm is proposed for input saturated strict-feedback nonlinear systems with unknown dynamics. In order to reduce the requirement of configuring a complete suit of sensors and enhance the reliability of the controlled system, a neural networks (NNs) based adaptive state observer is developed firstly to reconstruct the system states. Subsequently, based on the state estimation information, a hybrid-triggered feedforward controller is designed to transform the original tracking control problem into an equivalent regulation issue, which is then solved by developing an event-triggered optimal controller. Therefore, the final controller consists of a hybrid-triggered feedforward controller and an event-triggered optimal controller. In order to make the actual input signals of the two controllers be updated simultaneously, a synchronization-oriented triggering rule is established by using multiple triggering errors. By virtue of this unique framework, the proposed control scheme can not only minimize the predefined cost function, but also greatly reduce the data transmission. What is more, the convergence properties of the proposed control strategy are achieved by using Lyapunov theory. It is important to note that unlike the widely adopted observer-controller framework, where the separation principle holds for the design of the state observer, there is a considerable coupling relationship between the error dynamics of the state observer and the event-triggered optimal controller in this paper. The distinguishing feature of the proposed method is its ability to ensure a satisfactory level of precision in both state estimation and tracking control, even in the presence of control saturation issues. At last, the proposed control strategy is applied to the tracking control problem of a high-order robot system and marine surface vehicle to demonstrate its effectiveness.

Trajectory tracking nonlinear H∞ controller for wheeled mobile robots with disturbances observer

Wheeled Mobile Robots (WMRs) are systems with multiple industrial and civilian applications. Trajectory tracking is essential in many applications, such as surveillance, monitoring, and autonomous driving. However, in practical applications, a WMR is always affected by kinematic disturbances, state estimation error, and measurement noise, which may diminish the system's performance. Hence, this work proposes a novel observer-based H∞ controller that is robust against matched and unmatched disturbances. The proposed methodology compensates for disturbances through a disturbance observer, transforming the closed-loop system into a new one affected by uniformly bounded disturbances. Then, an H∞ controller is designed to make the WMR track a desired reference signal. A formal stability proof demonstrates the feasibility of the new proposal. Also, feedback and finite-time controllers are used to assess the novel controller. Numerical simulations and experimental results with a scaled autonomous car-like robot demonstrate the novel controller's efficiency and outstanding performance, despite disturbances when compared against finite-time and feedback controllers.

Self-learning sliding mode control based on adaptive dynamic programming for nonholonomic mobile robots

This paper proposes a self-learning sliding mode control (SlSMC) strategy with stability guarantee for the trajectory tracking of nonholonomic mobile robots (NMRs) under matched uncertainties, which improves the control performance of NMRs by optimizing the reaching law and the sliding mode surface of SMC as well as retaining the finite-time convergence and the robustness to uncertainties. In the presence of adverse factors such as skidding, slipping and environmental noise, the kinematic model of NMRs is reconstructed and an integral terminal sliding mode controller is designed for the trajectory tracking of NMRs. Then, based on the sliding mode controller, the proposed control strategy formulates the optimization of the SMC's reaching law and the sliding mode surface under stability constraints as two asynchronous optimal control problems with control constraints. Meanwhile, an online continuous-time receding-horizon optimization mechanism based on an actor-critic algorithm is proposed to solve the optimal problems asynchronously and improve online learning efficiency. The stability and the convergence of the proposed strategy are validated both in theory and simulations. Furthermore, extensive contrastive simulation results illustrate that the proposed receding horizon learning-based control strategy outperforms three recent methods in control performance. Finally, experiments of the proposed self-learning SMC strategy are carried out based on a real intelligent vehicle, and the experimental results also verify that the proposed method can meet the actual control needs of NMRs.

Neural Tracking Control of a Four-Wheeled Mobile Robot with Mecanum Wheels

This study designed an algorithm for the intelligent control of the motion of a mobile robot with mecanum wheels. After reviewing the model kinematics and dynamics of the robot, we conducted a synthesis of the neural control algorithm to determine network weight adaptation, according to Lyapunov stability theory. Using a MATLAB/Simulink computing environment, we developed a numerical simulation for the implementation of the robot’s motion path with parametric disturbances acting on the control object. To determine the quality of the implementation of the desired motion path, a numerical test of the robot’s motion, controlled with the use of a PD controller, was conducted. The proposed control algorithm was verified on a laboratory stand equipped with a dSpace DS1103 controller board and a Husarion Panther four-wheeled mobile robot with mecanum wheels. The conducted research confirmed the improved implementation of the desired motion path by a robot controlled with the use of an intelligent control system.