Trajectory Tracking Control Of Robot Research Articles

Emergency supplies transportation robot trajectory tracking control based on Koopman and improved event‐triggered model predictive control

AbstractIn the emergency rescue and disposal of social public emergencies, supply transportation effectively provides a strong supply foundation and realistic conditions. The trajectory tracking control of emergency supplies transportation robot is the key technology to ensure the timeliness of transportation. In this article, the emergency supplies transportation robot is taken as the research object, based on Koopman operator theory, combined with radial basis function (RBF) neural network disturbance observer and adaptive prediction horizon event‐triggered model predictive control (APET‐MPC) algorithm to investigate the purely data‐driven trajectory tracking control problem of emergency supplies transportation robot when the model parameters and models are unknown. Firstly, the Koopman operator is used to establish a high‐dimensional linear model of the robot. Secondly, the RBF neural network disturbance observer is designed to estimate the disturbance during the robot operation and compensate it to the controller. Thirdly, APET‐MPC is used to optimize the trajectory tracking control of the emergency supplies transportation robot to reduce computational complexity. Finally, the performance of the proposed trajectory tracking controller is verified by Carsim/ Simulink joint simulation. The simulation results show that the model established by Koopman operator theory can achieve the high accuracy approximation of the robot. Compared with the MPC trajectory tracking controller, the APET‐MPC trajectory tracking controller based on RBF neural network disturbance observer (RBF‐APET‐MPC) improves the tracking accuracy of the robot and reduces the total triggering times of the system by more than 50%.

Observer-Based Trajectory Tracking Control of Nonholonomic Wheeled Mobile Robots

Observer-Based Trajectory Tracking Control of Nonholonomic Wheeled Mobile Robots

Trajectory Tracking Control of Car-like Mobile Robots Based on Extended State Observer and Backstepping Control

Trajectory Tracking Control of Car-like Mobile Robots Based on Extended State Observer and Backstepping Control

A fast dynamic pose estimation method for vision-based trajectory tracking control of industrial robots

Vision-based robotic trajectory tracking control is considered a promising technology. However, the slow sampling rate and latency of the vision sensor enormously limit the tracking performance. To conquer the issues, this paper proposes a dual-space error-state Kalman filter (DS-ESKF). By combining the encoder measurement with the vision measurement, the end-effector’s pose between adjacent vision measurements is restored, and the pose estimation cycle is synchronized with the control cycle. The critical distinguishing of DS-ESKF is that the encoder-driven error-state kinematics for the industrial robot is reconstructed. The experimental results on the Staubli TX60 industrial robot show that compared with the existing dual-rate Kalman filters (DR-KF), DS-ESKF can reduce estimation errors by about 50 % and have robust estimation performance. By applying the trajectory tracking control scheme combining DS-ESKF with a simple PID controller to Staubli TX60, the tracking accuracy is significantly improved (±0.11 mm for position and ± 0.05°for orientation).

Trajectory tracking control of wheeled mobile robots with skidding and time-varying delay

This paper tackles the problem of trajectory tracking for wheeled mobile robots subject to time-varying input delay and bounded external disturbances. First, we establish a dynamic model for a wheeled mobile robot under sliding and skidding conditions, and determine the maximum allowable input delay that maintains system stability using Razumikhin-type stability analysis without prior knowledge of the variation in delay. The proposed adaptive robust controller combined with super-twisting sliding mode control is resilient to disturbances such as input delay, system uncertainty, and parameter variation. The proposed adaptive law enables real-time modification of switching gain based on tracking error without predefined knowledge of uncertainty bounds. Compared with traditional sliding mode control strategies, the super-twisting algorithm can eliminate chattering phenomenon while combined robust methods further reduce modeling uncertainties’ influence on system performance. Finally, we select an appropriate Lyapunov function to analyze and prove uniformly ultimately bounded of the closed-loop system. MATLAB simulation comparison results demonstrate that this approach achieves high tracking accuracy, faster response speed, and robustness.

Nonlinear Robust Adaptive Control of Universal Manipulators Based on Desired Trajectory

The introduction of a dynamic model in robot trajectory tracking control design can significantly improve its trajectory tracking accuracy, but there are many uncertainties in the robot dynamic model which can be dealt with through robust control and adaptive control. The prevailing robust control as well as adaptive control methods require real-time computation of robot dynamics, but the extreme complexity of the robot dynamics equations makes it difficult to apply these methods in real industrial systems. To this end, this article proposes a robust adaptive control method based on the desired trajectory, which uses the desired trajectory to compute most of the control terms offline, including the robot’s nominal dynamics and regression matrices, and substantially reduces the need for real-time computation of the feedback signals. The robust term modifies the perturbation of the inertial parameters of the links, the adaptive term learns the friction coefficients of the joints online, and an additional compensation term is designed to satisfy the Lyapunov stability condition of the system. Finally, taking a universal manipulator as the experimental platform, the control performances of different control methods are compared to show the feasibility of the controller and the effective reduction in real-time computational complexity.

Real-time Trajectory Planning and Tracking Control of Bionic Underwater Robot in Dynamic Environment.

In this article, we study the trajectory planning and tracking control of a bionic underwater robot under multiple dynamic obstacles. We first introduce the design of the bionic leopard cabinet underwater robot developed in our lab. Then, we model the trajectory planning problem of the bionic underwater robot by combining its dynamics and physical constraints. Furthermore, we conduct global trajectory planning for bionic underwater robots based on the temporal-spatial Bezier curves. In addition, based on the improved proximal policy optimization, local dynamic obstacle avoidance trajectory replanning is carried out. In addition, we design the fuzzy proportional-integral-derivative controller for tracking control of the planned trajectory. Finally, the effectiveness of the real-time trajectory planning and tracking control method is verified by comparative simulation in dynamic environment and semiphysical simulation of UWSim. Among them, the real-time trajectory planning method has advantages in trajectory length, trajectory smoothness, and planning time. The error of trajectory tracking control method is controlled around 0.2 m.

Tracking control of wheeled mobile robots via intermittent control.

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.

4WS Intelligent Fire-Fighting Robot Trajectory Tracking Control Based on Adaptive Cornering Stiffness

4WS Intelligent Fire-Fighting Robot Trajectory Tracking Control Based on Adaptive Cornering Stiffness

Trajectory Tracking Control of Fast Parallel SCARA Robots with Fuzzy Adaptive Iterative Learning Control for Repetitive Pick-and-Place Operations

Aiming at enhanced suppression of external disturbances and high-precision trajectory tracking of parallel SCARA robot dedicating to fast pick-and-place operations, this work presents the integrated control design of iterative learning algorithm, adaptive control and fuzzy rules, namely, fuzzy adaptive iterative learning control, for such type of robots. A step-design approach is adopted to ensure the adaptability of the designed control law, which is reflected in two aspects: ① the feedback gain of the controller is adjusted by the fuzzy rules; ② the adaptive unknown parameters are obtained by means of iterative learning estimation to suppress the uncertainties and external disturbances. The stability of the designed controller is analyzed and proved by the Lyapunov theory, and the effectiveness is verified by observing the tracking errors in joint space along with the testing pick path, in comparison with different iterative learning based algorithms. After the first-iteration learning, the motion errors of the four actuated joints can be reduced by 56.5%, 45.8%, 46.4% and 39.8%, respectively, and after 15 iterations of learning control, the final angular errors by the designed control law converge to 0.7×10−4 degree maximally. The varying maximum, root-mean-squared and mean angular displacement errors of the actuation joints can converge to zero values with the increasing iterations rapidly, which shows the robustness, effectiveness and advantages of the designed control law. The designed control law can be generalized to high-speed parallel pick-and-place robot to ensure high-precision trajectory tracking for high-quality material handling tasks.

Passivity-Based Trajectory Tracking and Formation Control of Nonholonomic Wheeled Robots Without Velocity Measurements

This note proposes a passivity-based control method for trajectory tracking and formation control of nonholonomic wheeled robots without velocity measurements. Coordinate transformations are used to incorporate the nonholonomic constraints, which are then avoided by controlling the front end of the robot rather than the center of the wheel axle into the differential equations. Starting from the passivity-based coordination design, the control goals are achieved via an internal controller for velocity tracking and heading control and an external controller for formation in the port-Hamiltonian framework. This approach endows the resulting controller with a physical interpretation. To avoid unavailable velocity measurements or unreliable velocity estimations, we derive the distributed control law with only position measurements by introducing a dynamic extension. In addition, we prove that our approach is suitable not only for acyclic graphs but also for a class of non-acyclic graphs, namely, ring graphs. Simulations are provided to illustrate the effectiveness of the approach.

Trajectory Tracking Control of Transformer Inspection Robot Using Distributed Model Predictive Control.

To overcome the difficulty in tracking the trajectory of an inspection robot inside a transformer, this paper proposes a distributed model predictive control method. First, the kinematics and dynamics models of a robot in transformer oil are established based on the Lagrange equation. Then, by using the nonlinear model predictive control method and following the distributed control theory, the motion of a robot in transformer oil is decoupled into five independent subsystems. Based on this, a distributed model predictive control (DMPC) method is then developed. Finally, the simulation results indicate that a robot motion control system based on DMPC achieves high tracking accuracy and robustness with reduced computing complexity, and it provides an effective solution for the motion control of robots in narrow environments.

Trajectory optimization and tracking control of free-flying space robots for capturing non-cooperative tumbling objects

This paper investigates the problem of capturing non-cooperative tumbling objects by free-flying space robots. To solve the two challenges of task constraints and base-manipulator coupling, a pseudospectral method based trajectory optimization and a reinforcement learning based tracking control are proposed for the free-flying space robots. Multiple constrains, including dynamics, field of view and obstacle avoidance, are considered in trajectory optimization. The adaptive segmented Radau pseudospectral method is used to discretize the energy-optimal trajectory problem into a nonlinear programming problem. By adaptively dividing the global time interval into multiple subintervals, higher-order interpolation polynomials are avoided. A reinforcement learning based parameter tuning method is proposed for the base controller to suppress the reaction torque caused by the manipulator. Numerical simulations and experiments on air-bearing testbed verify the effectiveness of our methods in terms of planning efficiency and tracking precision.

Trajectory tracking control of nonholonomic wheeled mobile robots using only measurements for position and velocity

This paper studies the trajectory tracking control of nonholonomic wheeled mobile robots, and only measurements for Cartesian position and velocity are used. A full-order observer is proposed to estimate the unmeasurable attitude and filter the position signal. Its effectiveness relies on the persistent excitation (PE) condition of non-zero linear velocity. Using the observer states, we design a local tracking controller that guarantees: (a) the linear velocity input is positive, and (b) the position tracking error is convergent to zero locally asymptotically. Experiments performed on a robot built in-house illustrate the control algorithm.

Position-based visual servoing of a 6-RSS parallel robot using adaptive sliding mode control

The trajectory tracking control of parallel robots is challenging due to their complicated dynamics and kinematics. This paper proposes a position-based visual servoing (PBVS) approach for a 6-Revolute-Spherical-Spherical (6-RSS) parallel robot using adaptive sliding mode control in Cartesian space. A photogrammetry sensor C-Track 780 in the eye-to-hand configuration is adopted to measure the real-time pose of the robot end-effector, which can avoid the calculation of robot forward kinematics and provide more flexibility for controller design. An adaptive Kalman filter is utilized to deal with uncertain noises in visual measurements to increase the pose estimation accuracy. A sliding mode controller with strong robustness is designed to cope with system uncertainties, and a radial basis function (RBF) neural network is incorporated to realize the auto-tuning of the control gains, which make the robot effectively track different trajectories with time-varying conditions in real applications. Based on Lyapunov theorem, the stability analysis of the controller has been done. Experiments have been conducted to validate the effectiveness of the proposed strategy and illustrate the superiority of the designed controller.

Dynamic Modeling and Robust Trajectory Tracking Control of a Hybrid Propulsion-Based Small Underwater Robot

This paper proposes a hybrid propulsion-based small underwater robot for robust trajectory tracking control in a harsh and complex underwater environment. The robot is equipped with a Coanda-effect jet thruster and a pair of propeller-based reconfigurable magnetic-coupling thrusters, allowing it to traverse safely in confined or cluttered spaces as well as cruise efficiently in the open water. To investigate the robot dynamic modeling, we first formulated its simplified mathematical model and estimated the hydrodynamic coefficients by performing the planar motion mechanism using CFD (computational fluid dynamics) simulation. Then, a double-loop trajectory tracking control architecture was designed considering the model uncertainties and environmental disturbances. Based on Lyapunov theory, the outer-loop kinematic control produces the virtual velocity command, while the inner-loop dynamic control adopts the full-state feedback L1-adaptive control to match the command. The asymptotic convergence of the tracking errors and the stability of the whole closed-loop system are guaranteed. Finally, comparative simulations in the presence of unknown disturbances and the variation of model parameters were carried out to verify the robustness of our proposed trajectory tracking control, which is also suitable for the separated son robots.

Cascaded ESO based multi-task priority tracking and null-space compliance control for redundant robots

This paper investigates multi-task priority trajectory tracking and null-space compliance control of redundant robots without joint velocity and joint torque sensors. A novel multi-task priority controller is proposed to improve the dynamic performance and robustness of all level tasks. First, a cascaded extended state observer (ESO) is put forward to estimate joint velocity, lumped disturbances and the interference from high-priority tasks to low-priority tasks simultaneously. Then, a tracking controller for higher priority tasks and a null space compliance controller for the lowest priority task are designed. The stability of the closed loop system under the proposed cascaded ESO and task trajectory tracking controller is analyzed, as well as the passivity of the lowest priority task. The parameter tunning procedure of the cascaded ESO is given. Comparative simulation and experiment results verify the effectiveness of the proposed method.

Trajectory tracking control of a three-wheeled omnidirectional mobile robot using disturbance estimation compensator by RBF neural network

Trajectory tracking control of a three-wheeled omnidirectional mobile robot using disturbance estimation compensator by RBF neural network

Lyapunov based global trajectory tracking control of wheeled mobile robot

This paper considers the global trajectory tracking control problem of wheeled mobile robot. A novel tracking controller for the kinematic model of wheeled mobile robot is presented based on the cascaded approach theory. Meanwhile, by using the Lyapunov stability theory, the stability of the controller is proved. Finally, the proposed scheme is implemented on the wheeled mobile robot. The simulation results show the effectiveness of the controller.

Dynamic Modelling and Continuous Trajectory Tracking Control of Space Robots Based on Lie Group SE(3)

For space robots, it is difficult to track continuous time-varying manifolds on SE(3) by using traditional closed-loop control strategies, which are designed to track the position and the attitude separately. Therefore, the dynamics model should be rebuilt, and the corresponding control strategy should be redesigned. Firstly, the dynamics equations for a space robot in the joint space and workspace are established separately in the framework of Lie group SE(3) and screw theory based on the Lagrange principle. Secondly, based on the proposed feedback form, a PD (proportional derivative) control law of output force on the end-effector is designed, and a closed-loop continuous tracking control strategy is proposed using the force Jacobian matrix and the kinematic model. The simulation results show that the control scheme has good performance when the system state changes gently. Furthermore, a robust sliding mode tracking control scheme is designed. The simulation results show that the proposed robust control law has better accuracy than the PD control law because the system state changes wildly. Finally, a robust fuzzy sliding mode tracking control scheme is designed to deal with the chattering phenomenon. The simulation results show that the proposed robust fuzzy control law can eliminate the chattering well and decrease the joint control torque significantly. The robustness of the proposed robust fuzzy control law is also verified by numerical simulation.