Joint Trajectory Tracking Research Articles

A hybrid active control method for a 3-DOF wave-compensated gangway

ABSTRACT Wave-compensated gangways are affected by forces from internal joint coupling and vessel motion, making the control process complex and challenging. Previously, three-degree-of-freedom gangways adopted passive compensation methods, utilizing end-effector sensor feedback for control. However, these methods are constrained by slow response times and significant lag. This study proposes an active hybrid compensation approach to enhance response speed and reduce tracking errors. The kinematics and dynamics of the gangway under vessel motion were analyzed. Subsequently, an improved velocity feedforward second-order super-twisting sliding mode control scheme was designed, complemented by fuzzy control for small torque compensation. Two experiments were conducted to evaluate and compare the performance of three control methods. Experiments demonstrated that the proposed hybrid control method exhibited superior performance in joint trajectory tracking, significantly reducing both root mean square and maximum errors, validating its effectiveness in improving tracking speed and accuracy.

Joint buffetings suppression and trajectory tracking by fuzzy-based super-twist sliding mode control: Application to a fast parallel robot for PnP operations

This paper deals with the suppression of robotic joint buffetings for enhanced precision of trajectory tracking by means of fuzzy-based super-twist sliding mode control, which is illustrated with a fast pick-and-place parallel robot for material handling. Prior to control design, the joint motions and torques are measure to identify the dynamic parameters of the robot with recursive least square method, which is experimentally validated for the further model-based control design. In order to suppress the joint buffetings and to improve the tracking accuracy, the control law by integrating fuzzy algorithm and second-order sliding mode control is designed, where the control performance is evaluated by observing the joint dynamics, compared to the classical computed torque control and fuzzy sliding mode variable structure control. The experimental results and comparative study show the effectiveness of the developed control scheme, regarding the joint buffetings suppression and trajectory tracking precision. The main contribution lies in the integrated fuzzy algorithm and second-order sliding mode control for the model-based control design, with acceptable computational cost and trajectory tracking accuracy, from the perspective of actual engineering application.

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.

Sliding mode active disturbance rejection control for manipulator considering actuator saturation

<p style="text-align: justify;">Considering the issue of low control accuracy in joint trajectory tracking control for manipulator systems with actuator saturation due to external disturbances, modelling inaccuracies, and joint friction, a sliding mode active disturbance rejection control approach was proposed. An improved extended state observer is employed to observe and estimate the lumped disturbances affecting the system, providing feedback compensation. A variable gain reaching law is devised, coupled with a fast non-singular terminal sliding mode to design the system control law, which mitigates chattering inherent and ensuring precision control. Additionally, a novel output error compensation-based anti-windup scheme is introduced to compensate for the detrimental impacts caused by actuator saturation. Simulation results collectively demonstrate that the proposed tracking control algorithm exhibits better control performance and robustness against disturbances.</p>

Time-optimal coordinated detumbling and pose adjusting control of a non-cooperative target with model uncertainties

In this paper, a novel post-capture takeover control strategy of non-cooperative target with model uncertainties by the dual-arm space manipulator is proposed. Two mission objectives of target detumbling and pose adjusting are comprehensively considered. First, the motion trajectory of the target is parameterized with normalized time by a Bezier shape-based function. On this basis, by considering the resultant external force and moment constraint and the model uncertainties of the target, the mission duration is optimized to obtain the time optimal detumbling and pose adjusting the trajectory of the target. Furthermore, the desired trajectory in manipulator joint space is obtained by introducing the arm angle inverse kinematics method, and a joint trajectory tracking controller is designed based on the feedback linearization theory to track the desired joint trajectory. Simulation results show that detumbling and pose adjusting can be simultaneously achieved by one control action, which verifies the effectiveness of the proposed integrated control strategy.

JTEA: A Joint Trajectory Tracking and Estimation Approach for Low-Observable Micro-UAV Monitoring With 4-D Radar

JTEA: A Joint Trajectory Tracking and Estimation Approach for Low-Observable Micro-UAV Monitoring With 4-D Radar

Evaluation of a Fused Sonomyography and Electromyography-Based Control on a Cable-Driven Ankle Exoskeleton

This article presents an assist-as-needed (AAN) control framework for exoskeleton assistance based on human volitional effort prediction via a Hill-type neuromuscular model. A sequential processing algorithm-based multirate observer is applied to continuously estimate muscle activation levels by fusing surface electromyography (sEMG) and ultrasound (US) echogenicity signals from the ankle muscles. An adaptive impedance controller manipulates the exoskeleton's impedance for a more natural behavior by following a desired intrinsic impedance model. Two neural networks provide robustness to uncertainties in the overall ankle joint-exoskeleton model and the prediction error in the volitional ankle joint torque. A rigorous Lyapunov-based stability analysis proves that the AAN control framework achieves uniformly ultimately bounded tracking for the overall system. Experimental studies on five participants with no neurological disabilities walking on a treadmill validate the effectiveness of the designed ankle exoskeleton and the proposed AAN approach. Results illustrate that the AAN control approach with fused sEMG and US echogenicity signals maintained a higher human volitional effort prediction accuracy, less ankle joint trajectory tracking error, and less robotic assistance torque than the AAN approach with the sEMG-based volitional effort prediction alone. The findings support our hypotheses that the proposed controller increases human motion intent prediction accuracy, improves the exoskeleton's control performance, and boosts voluntary participation from human subjects. The new framework potentially paves a foundation for using multimodal biological signals to control rehabilitative or assistive robots.

Ultrasound Imaging-Based Closed-Loop Control of Functional Electrical Stimulation for Drop Foot Correction

Open-or closed-loop functional electrical stimulation (FES) has been widely investigated to treat drop foot syndrome, which is typically caused by weakness or paralysis of ankle dorsiflexors. However, conventional closed-loop FES control mainly uses kinematic feedback, which does not directly capture time-varying changes in muscle activation. In this study, we explored the use of ultrasound (US) echogenicity as an indicator of FES-evoked muscle activation and hypothesized that including US-derived muscle activation, in addition to kinematic feedback, would improve the closed-loop FES control performance compared to the closed-loop control that relies only on the kinematic feedback. A sampled-data observer (SDO) was derived to continuously estimate FES-evoked muscle activations from low-sampled US echogenicity signals. In addition, a dynamic surface controller (DSC) and a delay compensation (DC) term were incorporated with the SDO, denoted as the US-based DSC-DC, to drive the actual ankle dorsiflexion trajectory to the desired profile. The trajectory tracking error convergence of the closed-loop system was proven to be uniformly ultimately bounded based on the Lyapunov–Krasovskii stability analysis. The US-based DSC-DC controller was validated on five participants with no disabilities to control their ankle dorsiflexion during walking on a treadmill. The US-based DSC-DC controller significantly reduced the root-mean-square error of the ankle joint trajectory tracking by 46.52% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pm$</tex-math> </inline-formula> 7.99% ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p&lt;0.001$</tex-math> </inline-formula> ) compared to the traditional DSC-DC controller with only kinematic feedback but no US measurements. The results also verified the disturbance rejection performance of the US-based DSC-DC controller when a plantarflexion disturbance was added. Our control design, for the first time, provides a methodology to integrate US in an FES control framework, which will likely benefit persons with drop foot and those with other mobility disorders.

Optimally Initialized Model Reference Adaptive Controller of Wearable Lower Limb Rehabilitation Exoskeleton

A wearable lower-limb rehabilitation exoskeleton functions to fulfill the recovery process of limb functionality and assist physiotherapists. This paper presents an optimized adaptive control system for a wearable lower-limb rehabilitation exoskeleton. The tuning of the controller gains is defined as an optimization problem for a closed-loop control system of the wearable lower-limb rehabilitation robot by genetic algorithm and particle swarm optimization. We presented a novel initialized model reference adaptive controller (IMRAC) for real-time joint trajectory tracking, in which controller gains are adjusted by the gradient-based method. An experimental test of a 4-degree of freedom lower-limb rehabilitation exoskeleton was carried out to observe the closed-loop performance of IMRAC for bipedal human walking. The statistical comparison between IMRAC and MRAC shows an efficient performance and robustness of our proposed method for the joint trajectory tracking of the lower-limb rehabilitation exoskeleton in real time.

Glenohumeral joint trajectory tracking for improving the shoulder compliance of the upper limb rehabilitation robot.

Exoskeletons have become an important tool to help patients with upper extremity motor dysfunction in rehabilitation training and life assistance. In the study of the upper limb exoskeleton, the human glenohumeral joint will produce accompanying movement during the movement of the shoulder joint. This phenomenon causes a positional deviation between the shoulder joint and the exoskeleton, which affects the accuracy of exoskeleton-assisted human movement and the wearing comfort. Spend. Taking the coronal adduction and abduction of the shoulder joint as the research object, the shoulder joint angle and glenohumeral joint bony motion trajectory were fitted by bi-level X-rays, and then the Ultium Motion motion capture system was used to collect the characteristic motion of the shoulder joint surface and establish a model. A back-propagation neural network with shoulder joint motion and shoulder width as input and the coronal position of the glenohumeral joint as output, finally applied the model to the Nimbot exoskeleton upper limb rehabilitation training robot to verify the effectiveness of the algorithm. Real-time prediction of the glenohumeral joint motion trajectory was achieved, and the human-machine coupling compliance during the wearing of the upper limb exoskeleton was improved.

A dexterous motion control method of rope driven snake manipulator considering the rope-hole properties

The RDSM (rope driven snake manipulator) is a hyper redundant robot operating in narrow and confined space. However, the rope-hole properties of the mechanism are ignored mostly, resulting in the significant trajectory tracking error of the RDSM. To solve the problem, a RDSM prototype with 16 DOFs is designed firstly. Then the rope-hole nonlinear model is established considering the friction, clearance and rope flexibility. Based on the rope-hole nonlinear model, the rope length is corrected, and the kinematic model of the RDSM without considering the rope-hole properties is improved. The joint state observer and trajectory tracking controller of the RDSM are designed using the improved kinematic model. Finally, the proposed kinematic model and controller of the RDSM are verified by the virtual prototype and experimental prototype. The results show that the control precision of the RDSM can be improved significantly with the rope-hole compensation. The end position error is improved from the level of cm to mm, and the end attitude error is improved by about 60%.

Adaptive nonsingular terminal sliding mode control of robot manipulator based on contour error compensation

To achieve accurate contour tracking of robotic manipulators with system uncertainties, external disturbance and actuator faults, a cross-coupling contour adaptive nonsingular terminal sliding mode control (CCCANTSMC) is proposed. A nonsingular terminal sliding mode manifold is developed which eliminates the singularity completely. In order to avoid the demand of the prior knowledge of system uncertainties, external disturbance and actuator faults in practical applications, an adaptive tuning approach is proposed. The stability of the proposed control strategy is demonstrated by the finite-time stability theory. Then, the developed controller combines adaptive nonlinear terminal sliding mode control (ANTSMC) of joint trajectory tracking and proportion–differentiation control of end-effector contour tracking by introducing the coupling factor between multiple axes based on Jacobian. Moreover, a unified framework of cross-coupling contour compensation and reference position pre-compensation is built. Finally, numerical simulation and experimental results validate the effectiveness of the proposed control strategy.

A Multi-DOF Manipulator Joint Trajectory Tracking and Monitoring Method Based on Decision Tree

Understanding trajectory tracking control concerns is crucial for industrial-grade manipulators to provide precise and risk-free operations for the safe environment. Consequently, the robot arms need precise aim for tracking a given target trajectory by the trajectory control input driving torque which can use smart AI-based techniques for precision. Similarly, a decision tree is a soft computing-based method of feature space partitioning which can certainly allow the movement of robots in an accurate manner. The control of robot arms is an important aspect for automating the process of sustainable development. Aiming at the problem of poor tracking accuracy of traditional Multi-Degree-of-Freedom Manipulator Joint Trajectory Monitoring (Multi-DoF MJTM) and long monitoring delay, this article proposes a Multi-DOF manipulator joint trajectory tracking method based on decision tree. The Multi-DoF manipulator is developed for the adaptive control object of the working machine, and it is combined with the output response feature to construct the kinematics model of the Multi-DoF manipulator mechanism of the walking machine. The joint trajectory reconstruction of the running trajectory is used to obtain the joint trajectory deviation of the Multi-DoF manipulator’s running trajectory through the multi-measurement system. Based on this, the Multi-DoF manipulator’s running trajectory joint trajectory tracking control equation is obtained to realize the joint trajectory tracking and monitoring of the manipulator. The features for a safe environment are also integrated. The experimental results show that the proposed method has high accuracy in tracking the trajectory of Multi-DoF manipulator joints, and the time delay for tracking and monitoring the trajectory of manipulator’s joints is also optimized.

Adaptive Swarm Fuzzy Logic Controller of Multi-Joint Lower Limb Assistive Robot

The idea of developing a multi-joint rehabilitation robot is to satisfy the demands for recovery of lower limb functionality in hemiplegic impairments and assist the physiotherapists with their therapy plans. This work aims at to implement the Lyapunov Adaptive and Swarm-Fuzzy Logic Control (LASFC) strategy of 4-degree of freedom (4-DoF) Lower Limb Assistive Robot (LLAR) application, in which the control law is an integration of swarm-fuzzy logic control (SFLC) and Lyapunov adaptive control (LAC) with particle swarm optimization (PSO). The controller is established based on the sliding filtered steady-state error for SFLC. Its parameters are tuned by using PSO for the mathematical model of LLAR. The fuzzy defuzzification membership is set based on the tuned parameters for the real-time control system. LAC strategy is determined using stability analysis of the system to choose the controller’s parameters by observation of the system’s output and reference. The control law implemented in LLAR is the integration of SFLC and LAC to adjust the input voltage of joints. The parameters tuned by PSO are compared with the genetic algorithm (GA) statistically. In addition, the real-time trajectory tracking of the proposed controller for each joint is compared with LAC and SFLC separately. The experiment revealed that the LASFC has superior performance to the other two methods in trajectory tracking. For example, the average error for left hip by LASFC is 53.57% and 68% lower than SFLC and LAC, respectively. By the statistical analysis, it can be ascertained that the LASFC strategy performed efficiently for real-time control of the joint trajectory tracking.

Optimized trajectory tracking control of the clearance manipulator based on improved PSO algorithm

Aiming at the problems of low joint trajectory tracking accuracy and poor anti-interference ability caused by parameter uncertainty, nonlinearity, and external interference in the hydraulic servo system of the clearance manipulator, an active disturbance rejection servo control method optimized based on the improved Particle Swarm Optimization algorithm is proposed. First, through the kinematic and dynamic analysis of the clearance manipulator, the general solutions of its forward and inverse kinematics are derived, and the relationship expression between each joint angle and the extension of the piston rod of the driving cylinder is obtained according to the relationship between the joint space and the driving mechanism space; Second, the state space differential equations of the hydraulic system of the working device are derived using mechanistic modeling, and this is used to design an active disturbance rejection servo controller for each joint, and the parameters of the active disturbance rejection controller are optimized using an improved PSO algorithm to improve the control performance. Simulation and experimental results show that compared with classical PID control and conventional Active Disturbance Rejection Control, the improved ADRC in this paper has higher trajectory tracking control accuracy and the ability to suppress external disturbances.

Multi-Degree-of-Freedom Manipulator Joint Trajectory Tracking Control Method Based on Decision Tree

For industrial-grade manipulators, the study of trajectory tracking control issues provides an important guarantee for accurate and safe work. Therefore, the trajectory control input driving torque can meet the requirements of the robot arm to accurately track a given target trajectory, and the process of building a decision tree is a process of dividing the feature space. For a given training data set, a set of if-then is summarized the rule of. Based on this, this paper launches the research of multi-degree-of-freedom manipulator joint trajectory tracking control method based on decision tree. Based on the established kinematics and dynamics model of the manipulator, this paper uses a proportional-integral-derivative (PID) sliding mode controller based on the sliding mode surface of the manipulator to perform the trajectory tracking control of the end of the manipulator, and the simulation results of the improved sliding mode control are compared with the simulation results of the improved sliding mode control. The simulation results of the PID controller and the traditional sliding mode controller are compared. This paper finally verifies the effectiveness of the proposed new sliding mode controller based on the expanded state observer through the experimental platform. The speed and chattering problems of the trajectory tracking at the end of the manipulator are better than those of the controller on the experimental platform. Finally, this paper adopts the sliding mode variable structure control strategy combining the double-power reaching law and the improved terminal sliding mode surface to study the trajectory tracking control of the planar two-degree-of-freedom manipulator.

Robust fuzzy sliding mode control and vibration suppression of free-floating flexible-link and flexible-joints space manipulator with external interference and uncertain parameter

AbstractThe flexibility of the free-floating flexible space manipulator system’s link and joint may affect the control accuracy and cause vibrations. We studied the dynamics modeling, joint trajectory tracking control, and vibration suppressing problem of free-floating flexible-link and flexible-joints space manipulator system with external interference and uncertain parameter. The system’s dynamic equations are established using linear momentum conservation, angular momentum conservation, assumed mode method, and Lagrange equation. Then, the system’s singular perturbation model is established, and a hybrid control is presented. For the slow subsystem, a robust fuzzy sliding mode control is proposed to realize the joint desired trajectory tracking. For the fast subsystem, a speed difference feedback control and a linear-quadratic optimal control are designed to suppress the vibration caused by the flexible joints and the flexible link separately. The simulation comparison experiments under different conditions are taken. The simulate results demonstrate the proposed hybrid control’s validity.

Sensing and Control of a Multi-Joint Soft Wearable Robot for Upper-Limb Assistance and Rehabilitation

In the field of wearable robotics, there has been increased interest in the creation of soft wearable robots to provide assistance and rehabilitation for those with physical impairments. Compared to traditional robots, these devices have the potential to be fully portable and lightweight, a flexibility that may allow for increased utilization time as well as enable use outside of a clinical environment. In this letter, we present a textile-based multi-joint soft wearable robot to assist the upper limb, in particular shoulder elevation and elbow extension. Before developing a portable fluidic supply system, we leverage an off-board actuation system for power and control, with the worn components weighting less than half kilogram. We showed that this robot can be mechanically transparent when powered off, not restricting users from performing movements associated with activities of daily living. Three IMUs were placed on the torso, upper arm and forearm to measure the shoulder and elbow kinematics. We found an average RMSE of ~ 5 degrees when compared to an optical motion capture system. We implemented dynamic Gravity Compensation (GC) and Joint Trajectory Tracking (JTT) controllers that actively modulated actuator pressure in response to IMU readings. The controller performances were evaluated in a study with eight healthy individuals. Using the GC controller, subject shoulder muscle activity decreased with increasing magnitude of assistance and for the JTT controller, we obtained low tracking errors (mean ~ 6 degrees RMSE). Future work will evaluate the potential of the robot to assist with activities in post-stroke rehabilitation.

Tribo-dynamic analysis and motion control of a rotating manipulator based on the load and temperature dependent friction model

Joint friction has a significant influence on the dynamics and motion control of manipulators. However, the friction effect is often omitted or simplified in previous studies. In this paper, we establish the dynamics model of a single joint in a rotating industrial manipulator taking detailed friction effects into consideration and propose a new control algorithm for the friction compensation purpose. Firstly, the manipulator dynamics modeling is carried out employing a recently-proposed extended static friction model, which depicts load and temperature influence on Coulomb, Stribeck and viscous terms. Moreover, based on the established dynamics model, the paper presents a new adaptive fast nonsingular terminal sliding mode (AFNTSM) controller. The proposed approach has the advantages of continuous control inputs, fast convergence rate, no singularity and great robustness against disturbances. Furthermore, its adaptive property does not require any prior knowledge of the upper bound of the uncertainties. Finally, the proposed controller is applied to the manipulator joint trajectory tracking problem with varying friction subject to load and temperature changes. The numerical simulation verifies the effectiveness of our proposed method and its advantages over other controllers.

Singularity-Free Trajectory Planning of Free-Floating Multiarm Space Robots for Keeping the Base Inertially Stabilized

In a multiarm space robotic system, one or more manipulators can be used to stabilize the base through counteracting the disturbance caused by other manipulators performing on-orbital tasks. However, singularities are inevitably present in the traditional methods based on differential kinematics solutions. In this paper, we propose a singularity-free trajectory planning method to simultaneously keep the attitude and centroid position of the base stabilized in inertial space; the balance arms are also designed. First, we derive the coupling motion equations of a free-floating multiarm space robotic system. Then, the singularity problems are theoretically analyzed, and the theoretical basis for singularity-free trajectory planning is established. Second, we decompose the six degrees of freedom pose (attitude and position) stabilization problem into two 3DOF subproblems related to attitude and position balancing. We then design two robotic arms: 1) a position balance arm and 2) an attitude balance arm, to maintain the base centroid position and attitude, respectively. Third, we plan the coordinated trajectories of the two balance arms according to holonomic and nonholonomic constraints. As long as the desired motion is not beyond its balance ability, the reasonable joint variables can always be determined without encountering a singularity problem. Finally, the proposed methods are verified using simulations of typical on-orbital missions, including joint trajectory tracking and target capturing.