Papers by Keyword: Trajectory Tracking

Abstract: Lower limb exoskeletons assist individuals with mobility impairments by providing support and aiding rehabilitation. However, precise trajectory tracking remains a challenge due to variations in user movement and nonlinear gait dynamics. Traditional control methods, such as PID controllers, require continuous tuning and struggle with long-term adaptability. This study proposes a PID-based Iterative Learning Control (PID-ILC) approach for a 2-DOF lower limb exoskeleton, which refines control inputs over successive gait cycles to improve tracking accuracy. MATLAB simulations demonstrate that the PID-ILC strategy significantly reduces tracking errors over iterations, leading to smoother and more accurate joint movements. The results confirm that iterative learning enhances exoskeleton performance by improving motion precision and adaptability. This approach minimizes manual tuning efforts and provides a more effective solution for rehabilitation applications.

Abstract: This study presents controllers for trajectory tracking for the kinematic model of an Unmanned Ground Vehicle (UGV) subject to bounded inputs. The proposed controllers are based on smooth uniformly bounded functions that can easily be realized. Some results are demonstrated.

Abstract: In this paper, an unmanned aerial vehicle (UAV) with fixed-wing in normal condition flight, and fixed height, is considered and along with this process, kinematics model of UAV, assumed to have parametric uncertainty. In this situation the target of designing of proper controller family, based on switching logic, is to control the speed and roll angle of fixed-wing unmanned aerial vehicle in order to track desired path with minimum error. The desired path will be generated by trajectory maker block. The results of simulation on a fixed-wing UAV are presented to show the efficiency of the method.

Abstract: This paper presents a neural computed torque controller, which employs to a Caterpillar robot manipulator. A description to exert a control method application neural network for nonlinear PD computed torque controller to a two sub-mechanisms Caterpillar robot manipulator. A nonlinear PD computed torque controller is obtained via utilizing a popular computed torque controller and using neural networks. The proposed controller has some advantages such as low control effort, high trajectory tracking and learning ability. The joint angles of two sub-mechanisms have been obtained by using the numerical simulations. The discovered figures show that the performance of the neural computed torque controller is better than a conventional computed torque controller in trajectory tracking and reduction of setting time. Finally, snapshots of gain sequences are demonstrated.

Abstract: Trajectory tracking control of compliant parallel robot is presented. According to the characteristics of compliant joint, the system model is derived and the dynamic equation is obtained based on the Lagrange method. Radial Basis Function (RBF) neural network control is designed to globally approximate the model uncertainties. Further, an itemized approximate RBF control method is proposed for higher identify precision. The trajectory tracking abilities of two control strategies are compared through simulation.

Abstract: This paper presents a method of solving the problem of mobile robot motion control using a model predictive controller designed using Laguerre functions. A linear model of the two-wheeled nonholonomic robot is used. This linear model is obtained by converting the nonlinear model in the Cartesian system to a polar one. This change is preferred because it is easier to work with the linear model than its corresponding nonlinear one. Simulation results obtained from MATLAB showing that Laguerre-based MPC (LMPC) performs well are presented.

Abstract: In order to solve the mobile robot trajectory tracking problem better, an iterative learning control (ILC) was applied. And the efficiency of mobile robot trajectory tracking was improved. From the simulation result, ILC with forgetting factor has very good performance for solving mobile robot trajectory tracking problem, and the smooth of trajectory tracking process also improved well.

Abstract: This paper addresses a position and speed tracking problem for high-speed train automatic operation with actuator saturation and speed limit. A nonlinear model predictive control (NMPC) approach, which allows the explicit consideration of state and input constraints when formulating the problem and is shown to guarantee the stability of the closed-loop system by choosing a proper terminal cost and terminal constraints set, is proposed. In NMPC, a cost function penalizing both the train position and speed tracking error and the changes of tracking/braking forces will be minimized on-line. The effectiveness of the proposed approach is verified by numerical simulations.

Abstract: Trajectory tracking for autonomous vehicle is one of the field that researchers pay attention. The ultimate goal for trajectory tracking is to track the pre-defined path and follow the reference path with zero steady state error. The common modules for trajectory tracking field are reference generator, controller and plant. While most of the researchers are focusing on the controller development, less work has focused on the optimized reference generator. Optimized reference generator ensures the reference input to the controller is the optimized desired points in order to develop a good controller. Therefore, this work presents the reference generator algorithm that select the best point from the road coordinate profile before being send to the controller. The method is using the vehicle potential field and the modification from Dijkstra’s algorithm to generate the path. This algorithm is useful for trajectory tracking controller development. The algorithm is verified using simulation and experiment.

Abstract: This paper concerns trajectory tracking control of AGV. The model of forked AGV was simplified from a three-wheeled vehicle model to a “bicycle” model. The dynamic model of vehicle lateral motion was built depending on Newtown’s second law and the analysis of lateral tire forces. The optimal control linear quadratic regulator was applied to achieve trajectory tracking control. Use the MATLAB and CarSim to simulate. The satisfied results proved that the control algorithm was effective and could make the system stable.