Trajectory Tracking Control Of Robot Research Articles

Adaptive neural network-based sliding mode control for trajectory tracking control of cable-driven continuum robots with uncertainties

Adaptive neural network-based sliding mode control for trajectory tracking control of cable-driven continuum robots with uncertainties

Chaos-based coverage path planning framework for mobile robots and its digital signal processing implementation

Abstract For special coverage path planning, chaos-based mobile robots present a promising solution. The robot can cover the area of interest rapidly with unpredictable trajectories. However, this unpredictability causes difficulties in trajectory control and the robot often makes sharp turns, affecting motion stability. To address these challenges, this paper proposes a universal chaos-based path planning framework for mobile robots. Firstly, a chaotic system that can generate multi-scroll and multi-wing attractors is designed to verify the effectiveness of the framework. Secondly, an integrated system consisting of mobile robots and chaos is proposed to generate chaotic trajectories. By the control parameters in the integrated system, the formation mechanism of continuous chaotic trajectories is shown, which makes the chaotic trajectory have the advantages of scaling, rotating, straightening, and bending, and the path planning no longer relies on the chaotic system only. Then, a regional path planning strategy is proposed to control the angular velocity and workspace of the robot, making its motion smooth and reducing trajectory redundancy. Experiments conducted under this framework have demonstrated its university, with various chaotic systems capable of generating efficient robot trajectories with high coverage rate. Compared with the state-of-the-art similar memoryless algorithms, the coverage task can be accomplished quickly and efficiently by our approach. Finally, the robot trajectory tracking control verifies the controllability of chaotic trajectories, and the implementation of chaotic trajectories with digital signal processing techniques further validates the feasibility of path planning frameworks.

Reproduction and generalization of robot trajectory tracking control method using reinforcement learning and neural network techniques

Abstract. This paper introduces a method for controlling the trajectory of a manipulator robot with uncertainty, using reinforcement learning. The control is designed to work even when there are limitations on the inputs to the system. Reinforcement learning and neural networks are employed alongside standard robust control techniques to enhance the fixed-time convergence of the system state. A novel algorithm is proposed to develop a reinforcement learning-based approach. This approach utilizes radial basis function neural networks and nonsingular fast terminal sliding mode control to ensure error convergence within a predetermined time. This paper presents the task of monitoring the intended path followed by robotic arms in the presence of uncertain and unfamiliar disruptions. The experimental results validate that the suggested approach substantially improves both the stability and accuracy of trajectory tracking, making it more feasible for real-world applications in robotic systems.

Hybrid trajectory tracking control of wheeled mobile robots using predictive kinematic control and dynamic robust control

AbstractTrajectory tracking control of wheeled mobile robots (WMRs) is still a remarkable problem for many applications. In the present paper, a hybrid control is presented based on dynamic and kinematic equations of motion for wheeled mobile robots in the presence of the sum of the external disturbances and parametric uncertainty. The designed control for the WMR utilizes control and guidance to reach the reference path. In many studies, a control strategy is normally employed for WMR. However, in this study, hybrid control was used for the mentioned purpose. Akin to other studies, the kinematic control scheme here was based on the predictive control, and the dynamic control scheme was designed based on the robust control. Therefore, in this article, having introduced the kinematic model, a nonlinear predictive control was proved and designed. In the next step, a finite‐time integral type terminal sliding mode control (FITSMC) was designed based on the nonlinear dynamic model in order to automatically adjust the control gain and eliminate online disturbances and destructive chattering phenomena completely. In particular, a finite‐time disturbance observer was designed to estimate the external disturbances. The proof of the new proposed control scheme was presented using Lyapunov stability theory and numerical results. The mentioned integrated scheme, including predictive control (outer loop) and nonlinear adaptive control (inner loop), ensures the convergence and optimal tracking performance of all signals, as a result of which the tracking errors can arbitrarily converge to the origin in a finite time. In the final step, the simulation results were presented to show the effectiveness of the proposed scheme using MATLAB software, and the introduced control design was compared with a similar controller quantitatively and qualitatively.

Extended state observer-based finite-time trajectory tracking control for wheeled mobile robots under FDI attacks

The trajectory tracking control of wheeled mobile robots (WMRs) under False Data Injection (FDI) attacks is investigated in this paper. First, to mitigate the impact of FDI attacks, a finite-time extended state observer (FTESO) is constructed. The error dynamic of the tracking control system is then split up into two subsystems via the cascaded control approach, and a one-to-one finite-time control law is designed for both subsystems. Second, a new stability condition is proposed to ensure that the tracking error system is finite-time globally uniform ultimate bounded (GUUB) and the tracking error can converge into a compact set. Finally, it is validated that the proposed control method is effective through simulation studies.

Trajectory tracking control of a mobile robot using fuzzy logic controller with optimal parameters

Abstract This work investigates the use of a fuzzy logic controller (FLC) for two-wheeled differential drive mobile robot trajectory tracking control. Due to the inherent complexity associated with tuning the membership functions of an FLC, this work employs a particle swarm optimization algorithm to optimize the parameters of these functions. In order to automate and reduce the number of rule bases, the genetic algorithm is also employed for this study. The effectiveness of the proposed approach is validated through MATLAB simulations involving diverse path tracking scenarios. The performance of the FLC is compared against established controllers, including minimum norm solution, closed-loop inverse kinematics, and Jacobian transpose-based controllers. The results demonstrate that the FLC offers accurate trajectory tracking with reduced root mean square error and controller effort. An experimental, hardware-based investigation is also performed for further verification of the proposed system. In addition, the simulation is conducted for various paths in the presence of noise in order to assess the proposed controller’s robustness. The proposed method is resilient against noise and disturbances, according to the simulation outcomes.

High-performance foot trajectory tracking control of hydraulic legged robots based on fixed-time disturbance observers

High-performance foot trajectory tracking control of hydraulic legged robots based on fixed-time disturbance observers

Extended state observer‐based trajectory tracking control of a wheeled mobile robot with one unpowered trailer

AbstractIn this paper, trajectory tracking control is investigated for a wheeled mobile robot with one unpowered trailer using an extended state observer (ESO). The unpowered trailer is added to improve load capacity, which results in a large impact on trajectory tracking as a slow‐varying and large disturbance. A backstepping controller is proposed to generate desired velocities in an outer loop of a double closed‐loop structure. The ESO is employed in an inner loop to estimate the slow‐varying and large disturbance from the unpowered trailer. An integral sliding mode controller is also designed in the inner loop to track the desired velocities from the outer loop. Stability analysis for the ESO, the backstepping controller and the integral sliding mode controller is conducted via Lyapunov methods. Simulation results are provided to show the effectiveness of the trajectory tracking control for a wheeled mobile robot with one unpowered trailer.

Research on trajectory tracking control of delta high-speed parallel robot based on PTNTSMC

Research on trajectory tracking control of delta high-speed parallel robot based on PTNTSMC

Research on ground mobile robot trajectory tracking control based on MPC and ANFIS

This study focuses on the control strategy for a ground mobile robot (GMR) with independent three-axis six-wheel drive and four-wheel independent steering, performing double lane change trajectory tracking in complex scenarios. Initially, a dynamic model of the six-wheel independent drive and steering GMR was constructed. Utilizing Model Predictive Control (MPC) technology, the challenge of trajectory tracking at low speeds was effectively addressed. For high-speed conditions, by thoroughly analyzing the impact of the predictive time-domain, this study innovatively introduced an Adaptive Neuro-Fuzzy Inference System (ANFIS) to dynamically adjust the prediction horizon of the MPC. A novel trajectory tracking algorithm integrating MPC and ANFIS was developed, with the network structure being trained using backpropagation (BP) method and the least squares method. Compared to traditional MPC, this hybrid strategy significantly improves trajectory tracking accuracy and stability at high speeds, with computational efficiency increased by 48.65%. Additionally, the algorithm demonstrated excellent adaptability and control effectiveness in various rigorous tests, including different speed levels, complex steering paths, load changes, sudden obstacles, and variable terrain. A 70 km/h trajectory tracking experiment on a physical vehicle yielded a root mean square (RMS) error of 0.1904 m, verifying its superior tracking performance and practical reliability. This provides a pioneering solution for high-performance trajectory control of ground mobile robots.

Motion/force coordinated trajectory tracking control of nonholonomic wheeled mobile robot via LMPC-AISMC strategy

Nonholonomic constrained wheeled mobile robot (WMR) trajectory tracking requires the enhancement of the ground adaptation capability of the WMR while ensuring its attitude tracking accuracy, a novel dual closed-loop control structure is developed to implement this motion/force coordinated control objective in this paper. Firstly, the outer-loop motion controller is presented using Laguerre functions modified model predictive control (LMPC). Optimised solution condition is introduced to reduce the number of LMPC solutions. Secondly, an inner-loop force controller based on adaptive integral sliding mode control (AISMC) is constructed to ensure the desired velocity tracking and output driving torques by combining second-order nonlinear extended state observer (ESO) with the estimation of dynamic uncertainties and external disturbances during WMR travelling process. Then, Lyapunov stability theory is utilised to guarantee the consistent final boundedness of the designed controller. Finally, the system is numerically simulated and practically verified. The results show that the double-closed-loop control strategy devised in this paper has better control performance in terms of complex trajectory tracking accuracy, system resistance to strong interference and computational timeliness, and is able to realise effective coordinated control of WMR motion/force.

Trajectory Tracking Control of Pneumatic Cylinder-Actuated Lower Limb Robot for a Gait Training System

Trajectory Tracking Control of Pneumatic Cylinder-Actuated Lower Limb Robot for a Gait Training System

Optimal enhanced backstepping method for trajectory tracking control of the wheeled mobile robot

AbstractThis paper proposes a novel control method applied to the trajectory tracking of the wheeled mobile robot. This method can solve the tracking difficulty caused by the non‐holonomic constraint and the under‐actuated properties. First, according to the kinematic and dynamic tracking error models, the desired velocities for trajectory tracking purposes are obtained. Second, the control method, consisting of an enhanced backstepping controller with fewer gains and an optimization algorithm, is designed. The actual trajectory of the mobile robot is exactly converged and kept at the predefined reference trajectory by the operation of this method. Next, this method with globally uniformly asymptotically stability is theoretically analyzed. Finally, simulation comparisons and physical experiments are conducted in different scenarios. The tracking performance is evaluated by three metrics, namely convergence speed, tracking accuracy and robustness, thus verifying the effectiveness of the novel control method.

Trajectory tracking control of wearable upper limb rehabilitation robot based on Laguerre model predictive control

Wearable rehabilitation robots have become an important auxiliary tool in rehabilitation therapy, providing effective rehabilitation training and helping to recover damaged muscles and joints. In response to the difficulty of traditional control methods in solving various constraints in the trajectory tracking process of the Upper Limb Rehabilitation Robot (ULRR), this study uses model predictive control to study the trajectory tracking problem of the upper limb rehabilitation robot. Firstly, based on the Lagrangian dynamic model of wearable rehabilitation robots, an extended state space model with pseudo linearization of the system was established. Given the performance indicators and various constraints of the system, a corresponding model predictive controller is designed based on the Laguerre model to ensure system performance while greatly reducing the computational complexity of predictive control. Secondly, the stability of the model predictive controller is demonstrated, and a disturbance observer is introduced into the controller to achieve compensation for slow-varying perturbations; a joint space sliding mode variable is also introduced to achieve simultaneous tracking of the joint’s desired position and desired velocity. Finally, taking a planar two bar robot as an example, comparative simulation verification was conducted on unconstrained joint trajectory tracking and constrained joint trajectory tracking. The simulation results show that the model predictive controller can achieve simultaneous tracking of joint expected trajectory and expected speed while meeting various constraints. It has good effects in improving patient motion control ability and reducing patient fatigue, providing new research ideas and methods for the field of rehabilitation therapy.

Design and motion control of a tendon-driven continuum robot for aerospace applications

Continuum robots are flexible and compliant. Compared to the case in conventional articulated manipulators, the driving unit can be placed outside the workspace of the robot, so that the motion orientation has a relatively complete linear configuration flow, which can be applied to a special environment with narrow and multiple obstacles such as aerospace. This study presents the development process of a tendon-driven continuum robot (TCR) with a high length-diameter ratio. The skeleton structure which imitates a snake is composed of continuous joints in series. The driving device is operated by using a tendon-driven method, which reduces the complexity of the driving box and control system significantly. The diameter of the robot is designed to be 5 mm, which enables it to work in a narrow and slender space with certain flexibility. Subsequently, a kinematic model of the robot is established. The mode function backbone method is applied to realize TCR trajectory planning. An idea of segmented solving is adopted to achieve trajectory tracking control of the continuum robot. Finally, a prototype of the continuum robot is produced, and the rationality of the robot design and the effectiveness of the motion control method are verified through trajectory simulations and experiments. The robot can perform inspection tasks within a narrow gap of 20 mm with good environmental adaptability.

A RISE-based asymptotic prescribed performance trajectory tracking control of two-wheeled self-balancing mobile robot

A RISE-based asymptotic prescribed performance trajectory tracking control of two-wheeled self-balancing mobile robot

Dynamics modeling and trajectory tracking control of a life support robot using sliding-mode control with extended state observer

This paper investigates the trajectory tracking control problem of a life support robot chassis under the slipping disturbances. First, a novel dynamics model for the robot chassis is proposed based on Lagrange equation. The constructed model incorporates the effects of rotational kinetic energy from the driving wheels, providing a more comprehensive representation of the chassis dynamics. Then, on the basis of the innovative framework, the dual-loop trajectory tracking control is derived for the chassis, via the kinematics-based backstepping controller and the dynamics-based sliding-mode controller. Moreover, an extended state observer is designed to reduce the influence caused by external disturbances. Finally, the effectiveness and superiority of the proposed tracking control approach are verified through simulation experiments.

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