Control Of Robot Manipulators Research Articles

A Novel Disturbance Observer Based Fixed-Time Sliding Mode Control for Robotic Manipulators with Global Fast Convergence

A Novel Disturbance Observer Based Fixed-Time Sliding Mode Control for Robotic Manipulators with Global Fast Convergence

Prescribed Performance Adaptive Robust Control for Robotic Manipulators With Fuzzy Uncertainty

Prescribed Performance Adaptive Robust Control for Robotic Manipulators With Fuzzy Uncertainty

Adaptive Neural Network Tracking Control of Robotic Manipulators Based on Disturbance Observer

This article presents an adaptive neural network (ANN) control scheme based on a disturbance observer that can achieve trajectory tracking control of robotic manipulators under external disturbances and dynamic model uncertainties. Firstly, an ANN controller based on full-state feedback is derived using the backstepping technique to achieve an online approximation of uncertainty. The integral sliding mode surface with a position error is introduced into the controller, which reduces the steady-state error of the system and enhances robustness. Then, a novel disturbance observer is designed to estimate both the approximation errors of the ANN and external disturbances, and to provide compensation for the controller, effectively suppressing the trajectory tracking errors caused by approximation errors and disturbances. Subsequently, the Lyapunov stability theory is utilized to demonstrate the stability of the developed control strategy and the boundedness of all closed-loop signals. Finally, numerical simulations are used to confirm the efficacy of the proposed control method.

Robust sliding mode control for robot manipulators with analysis on trade‐off between reaching time and L∞ gain

This paper provides a new sliding mode control (SMC) approach, by which both the nominal and robust stability associated with a trajectory tracking problem for an uncertain robot manipulator are achieved. More precisely, the new control law consists of linear and nonlinear functions of tracking errors, in which the former is for the nominal stability and the latter is to ensure the robust stability of the resulting closed‐loop systems. The nonlinear function can be interpreted as an extended version of conventional SMC approach, and the reaching phase corresponding to a pregiven sliding surface is shown to be completed in a finite time; the tracking errors arrive at the sliding surface in a finite time and do not deviate from it after the arrival. In the sliding phase, the tracking errors are also ensured to converge to the origin. Finally, some simulation results are given to demonstrate both the theoretical validity and practical effectiveness of the proposed control approach.

Adaptive Dynamic Boundary Sliding Mode Control for Robotic Manipulators under Varying Disturbances

This paper introduces an Adaptive Dynamic Bounded Sliding Mode Control (ADBSMC) method that incorporates a disturbance observer to enhance the response characteristics of the robot manipulator while eliminating the reliance on a priori knowledge. The proposed method utilizes nonlinear sliding mode manifolds and fast-terminal-type convergence laws to address errors and parameter uncertainties inherent in the nonlinear system models. The adaptive law is designed to cover all boundary conditions based on the model’s state. It can dynamically determine upper and lower bounds without requiring prior knowledge. Consequently, the ADBSMC control method amalgamates the benefits of adaptive law and fast terminal sliding mode, leading to significant enhancements in control performance compared with traditional sliding mode control (SMC), exhibiting robustness against uncertain disturbances. To mitigate external disturbances, a system-adapted disturbance observer is devised, facilitating real-time monitoring and compensation for system disturbances. The stability of ADBSMC is demonstrated through the Lyapunov method. Simulation and experimental results validate the effectiveness and superiority of the ADBSMC control scheme, showcasing its potential for practical applications.

Adaptive Tracking Control of Robotic Manipulator Subjected to Actuator Saturation and Partial Loss of Effectiveness

Abstract This paper introduces an adaptive control design tailored for robotic systems described by Euler–Lagrange equations under actuator saturation and partial loss of effectiveness. The adaptive law put forth not only retains conventional control properties but also extends its scope to effectively address challenges posed by actuator saturation and partial loss of effectiveness. The framework’s primary focus is on bolstering system robustness, thereby ensuring the achievement of uniformly ultimate bounded tracking errors. The stability and convergence of the system’s behavior are rigorously established through the application of the Lyapunov analysis technique. Moreover, the effectiveness and superiority of the introduced framework are compellingly demonstrated through a series of practical simulations and experimental instances.

Tracking control for robotic manipulators with prescribed performance and adaptive sliding mode disturbance observer

This paper investigates the finite-time disturbance estimation and tracking control for robotic manipulators subjected to model perturbation and external disturbance. First, an adaptive sliding mode disturbance observer (ASMDO) is raised to estimate the lumped uncertainties, which does not need to consider the upper boundary prior knowledge of lumped uncertainties’ derivative, and excessive gain coefficients are avoided, meanwhile, which decrease the chattering phenomenon. Next, a finite-time prescribed performance control (FPPC) is established to guarantee the system converges within a finite time and promote transient performance. Then, together with the FPPC, a finite-time nonsingular fast terminal sliding mode variable is established, which handles singularity problem in terminal sliding mode. In the meantime, a finite-time adaptive nonsingular fast terminal sliding mode controller (FANFTSMC) is constructed based on ASMDO and FPPC. The Lyapunov theorem is given to clarify that the system can reach the stable state within a finite time. Theoretical analysis and experiments reveal that FANFTSMC has better tracking performance and stronger robustness.

Decentralized variable impedance control of modular robot manipulators with physical human–robot interaction using Gaussian process-based motion intention estimation

Decentralized variable impedance control of modular robot manipulators with physical human–robot interaction using Gaussian process-based motion intention estimation

Adaptive control of BLDC driven robot manipulators in task space

AbstractIn this study, task space tracking control of robot manipulators driven by brushless DC (BLDC) motors is considered. Dynamics of actuators are taken into account and the entire electromechanical system (i.e. kinematic, dynamic, and electrical models) is assumed to include parametric/structured uncertainties. A novel adaptive controller is designed and the stability of the closed loop system is ensured via novel Lyapunov type tools. To demonstrate performance and applicability of the proposed method, a simulation study is conducted using the model of a two degree of freedom, planar robotic manipulator driven by BLDC motors.

A novel generalized sliding mode controller for uncertain robot manipulators based on motion constraints

Abstract. To improve the trajectory tracking performance and robustness for uncertain robot manipulators, a generalized sliding mode controller (GSMC) including an ideal controller and a continuous sliding mode controller (SMC) is proposed from the standpoint of motion constraints. First, the trajectory tracking requirements are formulated as the motion constraints, based on which an ideal controller is proposed to satisfy the motion constraints for robot manipulators whose dynamics are precisely known. Second, an additional continuous SMC is presented to compensate for the effects of uncertainty, and the chattering phenomenon that commonly exists in the SMC can be avoided by the introduction of a smoothing function. Third, Lyapunov analysis is conducted to verify that the proposed GSMC enables the tracking error restricted to a small region around zero. Finally, the numerical simulation and experiment are performed to verify the effectiveness and superiority of the proposed GSMC.

Adaptive control of robot manipulators with closed architecture

This paper focuses on the adaptive control of robot manipulators with closed architecture and parametric uncertainty. We propose a new class of adaptive controllers via exploiting the potential as applying the forwardstepping approach and the input–output properties of an inverted form of the dynamics of robots with closed architecture. The condition that ensures the stability and convergence of the closed-loop system becomes rather moderate due to the new design and analysis, and the result presented further enhances the connection between nonlinear control theory and most industrial/commercial robotic applications.

Multilayer Neuroadaptive Output Feedback Control of Constrained Robotic Manipulators With Disturbance Compensation

Multilayer Neuroadaptive Output Feedback Control of Constrained Robotic Manipulators With Disturbance Compensation

A New Predefined Time Zeroing Neural Network With Drop Conservatism for Matrix Flows Inversion and Its Application.

Zeroing neural network (ZNN) can effectively solve the matrix flows inversion problem. Nevertheless, quite a few related research works focus on the improvement of the convergence and robustness performance of the ZNN models and ignore the conservatism of their predefined time. Therefore, this article adopts a polymorphous activation function (PAF) to construct a new predefined time ZNN (NPTZNN) model. The second method of Lyapunov is utilized to analyze the stability, convergence, and robustness of the NPTZNN model. The Beta function is dexterously employed in the process of calculating the predefined time of the NPTZNN model, reducing its conservatism. Furthermore, the correctness of the theoretical analyses is verified by numerous experiments. Finally, the NPTZNN model is applied to robot manipulator control and can improve the tracking speed, extending the applicability of the model.

Control of robot manipulators with uncertain closed architecture using neural networks

Control of robot manipulators with uncertain closed architecture using neural networks

Adaptive consensus tracking control for robotic manipulators with nonlinear time‐varying fault‐tolerant actuator and unknown control input directions

SummaryThe consensus tracking mechanism of a multi‐agent system consisting of third‐order nonlinear single joint manipulators was investigated in this study based on the pertinent industrial context. Unlike many previous studies on consensus control that presumed a known control direction, this investigation addressed the complex challenges posed by unknown control directions and time‐varying actuators. Specifically, we explored both time‐invariant and time‐varying actuator faults in scenarios wherein all parameters are unknown. The Nussbaum function was utilized to resolve time‐varying nonlinear fault‐tolerance and unknown control direction problems. In the design process of the control law, the backstepping method combining with Nussbaum function is adopted to address unmatched nonlinearity and unmatched uncertainty system, and the radial basis function neural network is used to approximate the unknown parameters The resulting control law allows all robotic manipulators to track the angle and angular velocity of the leader. Simulation results demonstrate the effectiveness of the proposed control scheme.

Analyzing stability and structural aspects of embedded fuzzy type 2 PID controller for robot manipulators

This study delves into the development and analysis of a novel Embedded Fuzzy Type 2 PID Controller for Robot Manipulators, motivated by the increasing need for enhanced control systems in robotic applications to improve precision and stability. In the background section, the limitations of conventional PID controllers in addressing uncertainties and disturbances, especially in complex tasks performed by robot manipulators, are presented. The concept of fuzzy logic and the Type 2 fuzzy system is introduced, highlighting their potential to manage imprecise and uncertain information. Through rigorous analysis and simulation, the superior performance of the Embedded Fuzzy Type 2 PID Controller is demonstrated when compared to traditional PID controllers and even Type 1 fuzzy controllers. The results showcase enhanced tracking accuracy, disturbance rejection, and adaptability, making it a promising solution for advanced robotic applications. In conclusion, this research provides a robust solution for improving the control of robot manipulators in uncertain and dynamic environments. The Embedded Fuzzy Type 2 PID Controller offers a new paradigm in control theory, ensuring stability and precision even in the face of unpredictable factors. This innovation holds great promise for advancing the capabilities of robotic systems and underlines the potential for further research in embedded fuzzy control systems.

Flatness-based control in successive loops for robotic manipulators and autonomous vehicles

ABSTRACT The control problem for the multivariable and nonlinear dynamics of robotic manipulators and autonomous vehicles is solved with the use of a flatness-based control approach which is implemented in successive loops. The state-space model of these robotic systems is separated into two subsystems, which are connected between them in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input–output linearised flat systems. The state variables of the second subsystem become virtual control inputs for the first subsystem. In turn, exogenous control inputs are applied to the first subsystem. The whole control method is implemented in two successive loops and its global stability properties are also proven through Lyapunov stability analysis. The validity of the control method is confirmed in two case studies: (a) control of a 3-DOF industrial rigid-link robotic manipulator and (b) control of a 3-DOF autonomous underwater vessel. The novel control method can simplify significantly the solution of the nonlinear control problem for robotic manipulators and vehicles. Unlike global linearisation-based control schemes, the proposed flatness-based method in successive loops does not need any changes in state variables of complicated state-space model transformations.

Adaptive Parallel Iterative Learning Control with A Time-Varying Sign Gain Approach Empowered by Expert System

This study explores the incorporation of time-varying sign gain into a parallel iterative learning control (ILC) architecture, augmented by an expert system, to enhance the performance and stability of a robotic arm system. The methodology involves iteratively tuning the learning control gains using time-varying sign gain guided by an expert system. Stability analysis, encompassing asymptotic and monotonic convergence, demonstrates promising results across multiple joints, affirming the effectiveness of the proposed control architecture. In comparison with traditional PID control, fixed gain ILC, and ILC with adaptive learning in the expert system, the analysis focuses on stability, precision, and adaptability, using root mean square error (RMSE) as a key metric. The results show that ILC with adaptive learning from the expert system consistently reduces RMSE, even in the presence of learning transients. This adaptability effectively controls the learning transients, ensuring improved performance in subsequent iterations. In conclusion, the integration of time-varying sign gain with expert system assistance in a parallel ILC architecture holds promise for advancing adaptive control in robotic systems. Positive outcomes in stability, precision, and adaptability suggest practical applications in real-world scenarios. This research provides valuable insights into the implementation of dynamic learning mechanisms for enhanced robotic system performance, laying the groundwork for future refinement in robotic manipulator control systems.

Optimal Fully Actuated System Approach-Based Trajectory Tracking Control for Robot Manipulators.

In this article, a trajectory tracking control strategy is proposed for robot manipulators via a fully actuated system (FAS) approach, which has shown its simplicity and flexibility for most of the nonlinear controller design. However, the motion control for robot manipulators is more complicated since unknown dynamical model, external disturbances, friction forces, and various physical constraints are required to be considered. Therefore, the FAS approach cannot be straightforwardly applied. To address these challenges, the dynamic model of robot manipulators is established via model identification methods. Furthermore, based on the identified model, an FAS composite control strategy with simple structure is designed, which is achieved by integrating a high-order disturbance observer (HODO) in the inner loop, with an FAS trajectory tracking controller in the outer loop. Specifically, the HODO is utilized for handling the uncertain dynamics and external disturbances. Moreover, the controller gains are optimized using a gradient-based optimal parameter tuning method (OPTM). By imposing joint angle constraints, joint angular velocity constraints, and input torque limits into the formulation, the OPTM also ensures the satisfaction of these physical constraints. Numerical simulations and experiments are provided to validate the performance of the proposed controller.

A Fuzzy-Enhanced Robust DZNN Model for Future Multiconstrained Nonlinear Optimization With Robotic Manipulator Control

A Fuzzy-Enhanced Robust DZNN Model for Future Multiconstrained Nonlinear Optimization With Robotic Manipulator Control