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This study discusses the tracking trajectory issue of the exoskeleton under the bounded disturbance and designs an useful tracking trajectory control method to solve it. By using the proposed control method, the tracking error can be successfully convergence to the assigned boundary. Meanwhile, the chattering effect caused by the actuators is already reduced, and the tracking performance of the pneumatic artificial muscles (PAMs) elbow exoskeleton is improved effectively.

A prescribed performance sliding mode control method was developed in this study to fulfill the joint position tracking trajectory task on the elbow exoskeleton driven by two PAMs. In terms of the control structure, a dynamic model was built by conforming to the adaptive law to compensate for the time variety and uncertainty exhibited by the system. Subsequently, a super-twisting algorithm-based second-order sliding mode control method was subjected to the exoskeleton under the boundedness of external disturbance. Moreover, the prescribed performance control method exhibits a smooth prescribed function with an error transformation function to ensure the tracking error can be finally convergent to the pre-designed requirement.

From the theoretical perspective, the stability of the control method was verified through Lyapunov synthesis. On that basis, the tracking performance of the proposed control method was confirmed through the simulation and the manikin model experiment.

As revealed by the results of this study, the proposed control method sufficiently applies to the PAMs elbow exoskeleton for tracking trajectory, which means it has potential application in the actual robot-assisted passive rehabilitation tasks.

Target tracking systems are generally computationally intensive and require expensive and power‐hungry visual sensors. On the other hand, the existing target tracking control approaches fail to track the target swiftly and accurately when the mobile robot moves in the diversified manoeuvre modes. The purpose of this paper is to propose a novel target tracking control method with a low cost embedded vision system to achieve high accuracy and speediness of target tracking control, regardless of the type of manoeuvre modes.

The pan/tilt angle differences are transformed from the tracking error between the image centre and the coordinates of the target centroid returned by the CMUcam3; the corresponding pan/tilt angle variation rates are calculated based on the manoeuvre control. All of them are fed to the controller. Then the controller generates appropriate control signals to fit the changing speed of target centroid and compensate for the tracking error. The experiments are designed in a way that the CMUcam3 keeps the target centre coincident with the image centre when the mobile robot moves in the diversified manoeuvre modes.

In spite of the type of manoeuvre modes, the controller responds to the tracking error instantly and actuates the pan/tilt with suitable position and speed commands, and the target centroid remains in the bounding box during the entire movement.

The proposed target tracking control takes the correlation between the robot manoeuvre modes and the target tracking control into account, and particularly suits for the target tracking tasks in planetary exploration, surveillance and military applications.

With the increasing demands of industrial applications, it is imperative for robots to accomplish good contact-interaction with dynamic environments. Hence, the purpose of this research is to propose an adaptive fractional-order admittance control scheme to realize a robot–environment contact with high accuracy, small overshoot and fast response.

Fractional calculus is introduced to reconstruct the classical admittance model in this control scheme, which can more accurately describe the complex physical relationship between position and force in the interaction process of the robot–environment. In this control scheme, the pre-PID controller and fuzzy controller are adopted to improve the system force tracking performance in highly dynamic unknown environments, and the fuzzy controller is used to improve the trajectory, transient and steady-state response by adjusting the pre-PID integration gain online. Furthermore, the stability and robustness of this control algorithm are theoretically and experimentally demonstrated.

The excellent force tracking performance of the proposed control algorithm is verified by constructing highly dynamic unstructured environments through simulations and experiments. In simulations and experiments, the proposed control algorithm shows satisfactory force tracking performance with the advantages of fast response speed, little overshoot and strong robustness.

The control scheme is practical and simple in the actual industrial and medical scenarios, which requires accurate force control by the robot.

A new fractional-order admittance controller is proposed and verified by experiments in this research, which achieves excellent force tracking performance in dynamic unknown environments.

The purpose of this paper is to address a fractional order fuzzy PID (FOFPID) control approach for solving the problem of enhancing high precision tracking performance and robustness against to different reference trajectories of a 6-DOF Stewart Platform (SP) in joint space.

For the optimal design of the proposed control approach, tuning of the controller parameters including membership functions and input-output scaling factors along with the fractional order rate of error and fractional order integral of control signal is tuned with off-line by using particle swarm optimization (PSO) algorithm. For achieving this off-line optimization in the simulation environment, very accurate dynamic model of SP which has more complicated dynamical characteristics is required. Therefore, the coupling dynamic model of multi-rigid-body system is developed by Lagrange-Euler approach. For completeness, the mathematical model of the actuators is established and integrated with the dynamic model of SP mechanical system to state electromechanical coupling dynamic model. To study the validness of the proposed FOFPID controller, using this accurate dynamic model of the SP, other published control approaches such as the PID control, FOPID control and fuzzy PID control are also optimized with PSO in simulation environment. To compare trajectory tracking performance and effectiveness of the tuned controllers, the real time validation trajectory tracking experiments are conducted using the experimental setup of the SP by applying the optimum parameters of the controllers. The credibility of the results obtained with the controllers tuned in simulation environment is examined using statistical analysis.

The experimental results clearly demonstrate that the proposed optimal FOFPID controller can improve the control performance and reduce reference trajectory tracking errors of the SP. Also, the proposed PSO optimized FOFPID control strategy outperforms other control schemes in terms of the different difficulty levels of the given trajectories.

To the best of the authors’ knowledge, such a motion controller incorporating the fractional order approach to the fuzzy is first time applied in trajectory tracking control of SP.

Trajectory tracking error of robotic manipulator has limited its applications in trajectory tracking control systems. This paper aims to improve the trajectory tracking accuracy of robotic manipulator, so a linear-extended-state-observer (LESO)-based prescribed performance controller is proposed.

A prescribed performance function with the convergence rate, maximum overshoot and steady-state error is derived for the output error transformation, whose stability can guarantee trajectory tracking accuracy of the original robotic system. A LESO is designed to estimate and eliminate the total disturbance, which neither requires a detailed system model nor a heavy computation load. The stability of the system is proved via the Lyapunov theory.

Comparative experimental results show that the proposed controller can achieve better trajectory tracking accuracy than proportional-integral-differential control and linear active disturbance rejection control.

In the LESO-based prescribed performance control (PPC), the LESO was incorporated into the PPC design, it solved the problem of stabilizing the complex transformed system and avoided the costly offline identification of dynamic model and estimated and eliminated the total disturbance in real-time with light computational burden. LESO-based PPC further improved control accuracy on the basis of linear-active-disturbance-rejection-control. The new proposed method can reduce the trajectory tracking error of the robotic manipulators effectively on the basis of simplicity and stability.

This paper aims to present an adaptive robust sliding mode tracking controller for a 6 degree-of-freedom industrial assembly robot with parametric uncertainties and external disturbances. The controller is used to achieve both stringent trajectory tracking, accurate parameter estimations and robustness against external disturbances.

The controller is designed based on the combination of sliding mode control, adaptive and robust controls and hence has good adaptation and robustness abilities to parametric variations and uncertainties. The unknown parameter estimates are updated online based on a discontinuous projection adaptation law. The robotic dynamics is first formulated in both joint spaces and workspace of the robot’s end-effector. Then, the design procedure of the adaptive robust sliding mode tracking controller and the parameter update law is detailed.

Comparative tests are also conducted to verify the effectiveness of the proposed controller, which show that the proposed controller achieves significantly better dynamic trajectory tracking performances as compared with conventional proportional derivative controller and sliding mode controller under the same conditions.

This is a new innovation for industrial assembly robot to improve assembly automation.

The purpose of this paper is to take transient contact force response, overshoots and steady-state force tracking error problems into account to form an excellent force controller.

The basic impedance function with a pre-PID tuner is designed to improve the force response. A dynamic adaptive adjustment function that combines the advantages of hybrid impedance and adaptive hybrid impedance control is presented to achieve both force overshoots suppressing and tracking ability.

The introduced pre-PID tuner impedance function can achieve more than the pure impedance function in aspects of converging to the desired value and reducing the force overshoots. The performance of force overshoots suppression and force tracking error are maintained by introducing the dynamic adaptive sigma adjustment function. The simulation and experimental results both show the achieved control performance by comparing with the previous control methods.

The implementation of the controller is easy and convenient in practical manufacture scenes that require force control using industrial robots.

A superior robot controller adapting to a variety of complex tasks owing to the following characteristics: maintenance of high-accuracy position tracking capability in free-space (basic capabilities of modern industrial robots); maintenance of high speed, stability and smooth contact performance in collision stage; and presentation of high-precision force tracking capability in steady contact.

An active disturbance rejection controller (ADRC) based on model compensation is proposed in this paper. The method should first be taken a nominal model of the robot to compensate. Subsequently, the uncertain external disturbance is estimated and compensated is used an expansion state observer (ESO) in real time, which can reduce the estimating range of observation for ESO. The purpose of this paper is to suggest a novel method to improve the system tracking performance, as well as the dynamic and static performance index.

A welding robot is a complicated system with uncertainty, time-varying, strong coupling and a nonlinear system; it is more complex as if it is used in an underwater environment, and it is difficult to establish an accurate dynamic model for an underwater welding robot. Aiming at the tracking control of an underwater welding robot, it is difficult to achieve the control performance requirements by the conventional proportional integral derivative method to realize automatic tracking of the seam.

The simulation experiment is carried out by MATLAB/Simulink, and the application experiment is recorded. The experimental results show that the control method is correct and effective, and the system’s tracking performance is stable, and the robustness and tracking accuracy of the system are also improved.

The seam gets plumper and smoother, with better continuity and no undercut phenomenon.

The study aims to equip robots with the ability to precisely maintain interaction forces, which is crucial for tasks such as polishing in highly dynamic environments with unknown and varying stiffness and geometry, including those found in airplane wings or thin, soft materials. The purpose of this study is to develop a novel adaptive force-tracking admittance control scheme aimed at achieving a faster response rate with higher tracking accuracy for robot force control.

In the proposed method, the traditional admittance model is improved by introducing a pre-proportional-derivative controller to accelerate parameter convergence. Subsequently, the authors design an adaptive law based on fuzzy logic systems (FLS) to compensate for uncertainties in the unknown environment. Stability conditions are established for the proposed method through Lyapunov analysis, which ensures the force tracking accuracy and the stability of the coupled system consisting of the robot and the interaction environment. Furthermore, the effectiveness and robustness of the proposed control algorithm are demonstrated by simulation and experiment.

A variety of unstructured simulations and experimental scenarios are designed to validate the effectiveness of the proposed algorithm in force control. The outcomes demonstrate that this control strategy excels in providing fast response, precise tracking accuracy and robust performance.

In real-world applications spanning industrial, service and medical fields where accurate force control by robots is essential, the proposed method stands out as both practical and straightforward, delivering consistently satisfactory performance across various scenarios.

This research introduces a novel adaptive force-tracking admittance controller based on FLS and validated through both simulations and experiments. The proposed controller demonstrates exceptional performance in force control within environments characterized by unknown and varying.

This paper aims on the trajectory tracking of the developed six wheel-legged robot with heavy load conditions under uncertain physical interaction. The accuracy of trajectory tracking and stable operation with heavy load are the main challenges of parallel mechanism for wheel-legged robots, especially in complex road conditions. To guarantee the tracking performance in an uncertain environment, the disturbances, including the internal friction, external environment interaction, should be considered in the practical robot system.

In this paper, a fuzzy approximation-based model predictive tracking scheme (FMPC) for reliable tracking control is developed to the six wheel-legged robot, in which the fuzzy logic approximation is applied to estimate the uncertain physical interaction and external dynamics of the robot system. Meanwhile, the advanced parallel mechanism of the electric six wheel-legged robot (BIT-NAZA) is presented.

Co-simulation and comparative experimental results using the BIT-NAZA robot derived from the developed hybrid control scheme indicate that the methodology can achieve satisfactory tracking performance in terms of accuracy and stability.

This research can provide theoretical and engineering guidance for lateral stability of intelligent robots under unknown disturbances and uncertain nonlinearities and facilitate the control performance of the mobile robots in a practical system.