Adaptive model predictive control of mobile robot with local path refitting

Presents the design of a differentiable, robust tracking controller for a mobile robot system. The controller provides robustness with regard to parametric uncertainty and additive bounded disturbances in the dynamic model. Through the use of a dynamic oscillator and a Lyapunov-based stability analysis, we demonstrate that the position/orientation tracking errors exponentially converge to a neighborhood about zero that can be made arbitrarily small, i.e. the controller ensures that the tracking error is globally uniformly ultimately bounded (GUUB). In addition, we illustrate how the robust tracking controller can be reconfigured as a variable structure controller that ensures global exponential regulation to an arbitrary desired setpoint.

A hierarchical control architecture, divided into upper and lower control levels, for the trajectory tracking control of mobile robots is proposed in this paper. The objective of the upper control level is to navigate the mobile robot on the desired trajectories by means of the kinematic model. A control law based on backstepping techniques is synthesized to make the posture error asymptotically stable. On the lower control level, the dynamic model of the robot is adopted from practical viewpoint. In order to guarantee the global asymptotic stability and demanded performance, a time-varying adaptive tracking control is designed such that the generalized velocities can converge to the upper control level's inputs. A hybrid controller is developed to incorporate the kinematic controller into the dynamic controller to deal with uncertain dynamic parameters of the mobile robot. Simulation results are utilized to illustrate the effectiveness of the proposed control algorithm.

In order to realize the trajectory tracking control of mobile robots, a global fast terminal sliding mode control method is proposed in the kinematic analysis. Based on the combination of the backstepping method and the thought of the fast terminal sliding mode control, the method is more superior than the backstepping design method on convergence time and convergence rate. The system state variables are designed based on based backstepping method, then, the controller is applied to mobile robot system by recursive structure of second order fast terminal sliding mode, the control law is designed by Lyapunov method. Finally, simulation results show the validity of the presented method.

Accurate trajectory tracking control of mobile robots is a complex task due to inherent system nonlinearities, modeling uncertainties, and external disturbances. To tackle these challenges, this paper proposes a neural network-based residual learning approach (NNRes) for high-fidelity and efficient system modeling. Unlike full model learning, the NNRes framework focuses on learning only the residual error between a nominal physical model and the actual system dynamics, thus preserving prior knowledge while enhancing adaptability. This hybrid model, combining the physical model and learned residual, is embedded into a Nonlinear Model Predictive Control (NMPC) scheme to improve control accuracy and robustness. The proposed method is systematically compared with state-of-the-art methods: full model learning using NNs (NNFull) and residual learning using Gaussian Processes (GPRes). Comparative simulation results demonstrate that NNRes achieves superior trajectory tracking control performance, enhanced robustness, and lower computational cost. These findings validate the effectiveness of NNRes within NMPC frameworks and offer valuable insights for the intelligent control of complex robotic systems.

In this paper a dynamic tracking control of mobile robot using neural network global fast sliding mode (NN-GFSM) is presented. The proposed strategy combines two control approaches, kinematic control and dynamic control. The laws of kinematic control are based on GFSM in order to determine the adequate velocities for the system stability in finite time. The dynamic controller combines two control techniques, the GFSM to stabilize the velocities errors, and a neural network controller in order to approximate a nonlinear function and to deal the disturbances. This dynamic controller allows the robots to follow the desired trajectory even in the presence of disturbances. The designed controller is dynamically simulated by using Matlab/ Simulink and the simulations results show the efficiency and robustness of the proposed control strategy.

The paper concerns the problem of trajectory tracking control of a four-wheeled PIAP SCOUT mobile robot with non-steered wheels. For this kind of wheeled robots, it is impossible to find kinematic relationship between robot’s body motion and motion of driven wheels, because of inherent sliding of wheels on the ground during turning. This is an important problem from the point of view of control of the robot. The approach followed in the present work relies on introducing a simple linear controller with feedback of actual yaw rate of robot’s body. The yaw velocity is measured by inexpensive MEMS gyroscope. Experiments were conducted on two kinds of floor typical for office buildings: PVC flooring and carpet flooring. Measurements of motion parameters were possible with INS technique. It was found that the proposed yaw rate controller significantly reduces the angular error of path tracking for 90 degrees turn maneuver.

In this paper, we propose an adaptive tracking control of nonholonomic mobile robots considering actuator dynamics. All parameters of robot kinematics, robot dynamics, and actuator dynamics are assumed to be uncertain. For the simple controller design, the dynamic surface control methodology is applied and extended to multi-input multi-output systems (i.e., mobile robots) that the number of inputs and outputs is different. From the Lyapunov stability theory, we derive adaptation laws and prove that all signals of a closed-loop system are semi-globally uniformly ultimately bounded. Finally, we perform compute simulations to demonstrate the performance of the proposed controller.

Robust Sliding Mode Trajectory Tracking Controller for a Nonholonomic Spherical Mobile Robot

The tracking control of wheeled mobile robot is a complex problem that is encountered in robotic science. In real applications, many serious difficulties affect the control of the robot. Nonlinear model, parameters uncertainties, and external disturbances limit the study of mobile robot tracking control. To reduce the mobile robot tracking error, we propose an adaptive law based on sliding mode control applied to a nonlinear model and taking into account uncertainties. Using the Lyapunov theory, the stability and the convergence of the tracking errors are proved. Simulations are used to illustrate the efficiency of the proposed controller.

This paper was concerned with the trajectory tracking control of wheeled mobile robots using aperiodic intermittent control. By establishing the corresponding motion model of the wheeled mobile robot, a tracking control strategy was proposed based on the intermittent control approach and backstepping method. Compared to the controllers using continuous state feedback, the proposed control strategy was activated only on separate time intervals, which combined the features of closed- and open-loop control. An example was given to illustrate the effectiveness of the obtained result.

The unfalsified control is one type of model-free adaptive control. The control strategy enables one to design a controller that is consist with a performance objective and measured past input-output online data without plant models or assumptions on the plant. The theory works by eliminating candidate controllers that do not satisfy performance specification by evolving experimental data. The controller which remained to the last is a controller which satisfies a performance specification. Based on unfalsified control theory, the trajectory tracking control of nonholonomic mobile robot is analyzed. By adopted adaptive PID control algorithm of unfalsified control strategy to feedback linearization system, the mobile robot can track the desired trajectory rapidly.

Backstepping based adaptive tracking control of non-holonomic mobile robots in the presence of both kinematic and dynamic parametric uncertainty is presented. The major challenge is the possible singularity phenomenon due to the approach of zero of the estimated input vector field entering the denominator of the control input, a common drawback of adaptive linearization-based schemes. A hybrid control approach, which switches between an adaptive and a robust control schemes, is developed for solving such a problem. It retains the advantage of an adaptive control approach to a greatest extent while avoiding the possible blowup of the torque inputs simultaneously. A case study on a specific Type (2; 0) mobile robot is provided in the final to verify the usefulness of the proposed design.

A feedback control law for orientation tracking problem of a mobile robot with N on-axle trailers is proposed in this paper. Based on a recursive designing process, the orientation tracking problem is converted into stabilizing the including angles on the designed reference value. The designing process of the control law is simple and can be easily extended to any given N trailers system. With input-to-state stability on cascade form system, the closed-loop system is proven to be asymptotically stable. Simulation experiment on a mobile robot with 4 trailers is implemented and the results demonstrate that the proposed control law works successfully.

This paper focuses on the trajectory tracking control algorithm for Differential Wheeled Mobile Robots (DWMRs) based on rhombic input constraints. The kinematics and dynamics model of DWMRs are established, and vector analysis method is used to design the controller when the linear velocity and angular velocity of DWMRs were not mutually independently. Through the simulation of tracking 8-shaped curve, a good control performance is obtained.

A nonlinear trajectory tracking controller for mobile robots with velocity limitation via fuzzy gains