Control Of Robot Manipulators Research Articles

Proximate fixed-time fault-tolerant tracking control for robot manipulators with prescribed performance

This paper presents a proximate fixed-time fault-tolerant prescribed performance tracking control for robot manipulators with actuator faults, parametric uncertainties, and bounded disturbances. An exact convergent fixed-time prescribed performance function is first introduced. A proximate fixed-time fault-tolerant prescribed performance control (FTPPC) is proposed within the framework of terminal sliding mode control. Lyapunov stability is employed to prove the position tracking error can be ensured proximate fixed-time stability and prescribed performance with an exact convergence time. Advantages of the proposed FTPPC include proximate fixed-time prescribed performance guarantees featuring faster transient and higher steady-state precision and strong robustness to actuator faults, parametric uncertainties, and bounded disturbances. Simulations and experiments utilizing artificial faults demonstrate the improved performances of the proposed approach.

Robust H∞ Fuzzy Approach Design via Takagi‐Sugeno Descriptor Model. Application for 2‐DOF Serial Manipulator Tracking Control

This paper focuses on trajectory tracking control for robot manipulators. While much research has been done on this issue, many other aspects of this field have not been fully addressed. Here, we present a new solution using feedforward controller to eliminate parametric uncertainties and unknown disturbances. The Takagi‐Sugeno fuzzy descriptor system (TSFDS) is chosen to describe the dynamic characteristics of the robot. The combination of this fuzzy system and the robust H∞ performance makes the system almost isolated from external factors. The linear matrix inequalities based on the theory of Lyapunov stability is considered for control design. The proposed method has proven its effectiveness through simulation results.

A Comparative Study of Swarm Intelligence Metaheuristics in UKF-Based Neural Training Applied to the Identification and Control of Robotic Manipulator

This work presents a comprehensive comparative analysis of four prominent swarm intelligence (SI) optimization algorithms: Ant Lion Optimizer (ALO), Bat Algorithm (BA), Grey Wolf Optimizer (GWO), and Moth Flame Optimization (MFO). When compared under the same conditions with other SI algorithms, the Particle Swarm Optimization (PSO) stands out. First, the Unscented Kalman Filter (UKF) parameters to be optimized are selected, and then each SI optimization algorithm is executed within an off-line simulation. Once the UKF initialization parameters P0, Q0, and R0 are obtained, they are applied in real-time in the decentralized neural block control (DNBC) scheme for the trajectory tracking task of a 2-DOF robot manipulator. Finally, the results are compared according to the criteria performance evaluation using each algorithm, along with CPU cost.

Design of a Novel NNs Learning Tracking Controller for Robotic Manipulator with Joints Flexibility

The precise tracking control problem for the robotic manipulator with flexible joints, subjected to system uncertainties and external disturbances, is addressed. A novel control scheme is presented that does not use link velocity measurements and high-order derivatives of the link states. The control scheme employs neural networks-based observers to estimate both motor velocity and link velocity. By using the virtually applied torque, the link controller is designed based on rigid link dynamics, and the motor controller is designed using the dynamic surface control technique. The proposed control scheme can guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood around zero. The simulation results confirm our theoretical analysis, and a comparison study demonstrates the advantages of the proposed control scheme compared to the standard DSC method.

Design and implementation of an adaptive neural network observer–based backstepping sliding mode controller for robot manipulators

Robot manipulators as an indispensable part of automatic operation are becoming increasingly important in intelligent manufacturing systems, such as grinding and assembly. Although control methods of robot manipulators have been extensively studied, high-precision robots still face new challenges from uncertainties caused by changes in the environment and enhancement of interference. As a consequence, the neural network-based observer is utilized to reduce the influence of uncertainties and external disturbances. In this work, a new kind of nonlinear disturbance observer is designed which could well function with observed states. To improve the control efficiency and response characteristic, a kind of new integral sliding manifold is devised and the control input is generated via backstepping techniques. The stability is proved with Lyapunov theory, and the experimental results are given to demonstrate the effectiveness of the proposed controller.

Fractional order fast terminal sliding mode control scheme for tracking control of robot manipulators

In this study, a fractional order fast terminal sliding mode control strategy is developed to address the trajectory tracking problem that arises when robot manipulators are subjected to uncertainties and external disturbances. A novel fractional order fast terminal sliding surface is proposed to achieve rapid finite time convergence and the explicit expression for the settling time is also formulated. To manage uncertainties, chattering phenomenon, singularities, large control gains, etc., a new fractional order fast terminal sliding mode control scheme is developed based on the proposed sliding surface. The radial basis function neural network is used in the proposed control strategy to approximate the nonlinearities and modeling errors of the robot dynamics in real time. The reconstruction error of neural network and upper bound on disturbances are handled by the adaptive compensator. The Lyapunov technique is used to examine the stability of the proposed control strategy. The proposed control technique improves the efficiency of the controller and allows for the asymptotic error convergence to occur in a finite amount of time. To compare the effectiveness of the proposed scheme to various existing control approaches, numerical simulation studies are also conducted.

A Model-Free-Based Control Method for Robot Manipulators: Achieving Prescribed Performance and Ensuring Fixed Time Stability

This paper addresses three significant challenges in controlling robot manipulators: improving response time, minimizing steady-state errors and chattering, and enhancing controller robustness. It also focuses on eliminating the need for computing the robot’s dynamic model and unknown functions, as well as achieving global fixed-time convergence and the prescribed performance for the control system. To achieve these objectives, a fixed-time sliding mode function is designed, which uses transformation errors to achieve prescribed control performance, with adjustments made to the maximum overshoot, convergence time, and tracking errors to keep them within predefined bounds. Additionally, a radial basis function neural network (RBFNN) is used to eliminate the need for knowledge of the robot’s dynamical properties and uncertain terms, which also reduces negative chattering. Finally, a novel fixed-time terminal sliding mode control (TSMC) algorithm is developed for robot manipulators without using their dynamical model. The fixed-time stability of the control system is thoroughly demonstrated by applying Lyapunov criteria and conducting simulations on a robot manipulator to showcase its effectiveness.

Reinforcement Learning-Based Fixed-Time Trajectory Tracking Control for Uncertain Robotic Manipulators With Input Saturation.

A fixed-time trajectory tracking control method for uncertain robotic manipulators with input saturation based on reinforcement learning (RL) is studied. The designed RL control algorithm is implemented by a radial basis function (RBF) neural network (NN), in which the actor NN is used to generate the control strategy and the critic NN is used to evaluate the execution cost. A new nonsingular fast terminal sliding mode technique is used to ensure the convergence of tracking error in fixed time, and the upper bound of convergence time is estimated. To solve the saturation problem of an actuator, a nonlinear antiwindup compensator is designed to compensate for the saturation effect of the joint torque actuator in real time. Finally, the stability of the closed-loop system based on the Lyapunov candidate is analyzed, and the timing convergence of the closed-loop system is proven. Simulation and experimental results show the effectiveness and superiority of the proposed control law.

Adaptive Fuzzy Neural Network Command Filtered Impedance Control of Constrained Robotic Manipulators With Disturbance Observer.

This article proposes an adaptive fuzzy neural network (NN) command filtered impedance control for constrained robotic manipulators with disturbance observers. First, barrier Lyapunov functions are introduced to handle the full-state constraints. Second, the adaptive fuzzy NN is introduced to handle the unknown system dynamics and a disturbance observer is designed to eliminate the effect of unknown bound disturbance. Then, a modified auxiliary system is designed to suppress the input saturation effect. In addition, the command filtered technique and error compensation mechanism are used to directly obtain the derivative of the virtual control law and improve the control accuracy. The barrier Lyapunov theory is used to prove that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded. Finally, simulation studies are performed to illustrate the effectiveness of the proposed control method.

Force Control of a Grinding Robotic Manipulator With Floating Base Via Model Prediction Optimization Control

In this article, a grinding robot for large area and an impedance-based force control method applied to the robot are described. With the development of science and technology, the demand for various industrial robots increased, and among them, the demand and importance of grinding robots that require high risk and precision increased. In particular, research on grinding robots targeting large areas has not been conducted relatively, and accordingly, the design and control mechanism of robots specialized in large areas was needed. A robot consisting of a manipulator and a grinding module with a 2-DOF parallel structure is proposed as the design of a new grinding robot. The control method is based on impedance force control mainly used in existing grinding robots, but to overcome the limitations of using only impedance control, the impedance control via model-based prediction optimization (MPO) is proposed as a control technique for grinding robots. Experiments were conducted to verify the force tracking ability of the proposed control, resulting in a 28.1% improvement in settling time for the desired force. Even for disturbance, more improved recovery performance than conventional controllers has been verified. As a result, proposed impedance force control via MPO shows improved force tracking performance over conventional impedance control, and is presented as one of the appropriate control methods for grinding robots targeting large areas.

Command-Filtered Finite-Time Fuzzy Adaptive Fault-Tolerant Control of Output-Constrainted Robotic Manipulators With Unknown Dead-Zones

This article presents a barrier Lyapunov functions-based (BLFs) adaptive fuzzy finite-time fault-tolerant control scheme for the manipulator with actuator faults and unknown dead-zones. Firstly, by constructing time-varying BLFs, the system output will be constrained in time-varying regions. Secondly, an adaptive method is designed to estimate the degree of actuator faults and to compensate for the effect of dead-zones. Thirdly, the command filtered backstepping with error compensation mechanism is introduced to approximate the virtual control laws, which can not only solve the “explosion of complexity” problem, but also improve the control accuracy. The control method can ensure that all signals of the closed-loop system are finite-time bounded, the tracking errors of joint positions converge to a small neighborhood of the origin, and meets the requirements of time-varying constraints. Finally, a rigid manipulator is used to verify the effectiveness of the scheme.

FeedForward Super-Twisting Sliding Mode Control for Robotic Manipulators: Application to PKMs

This article deals with the development and implementation of a novel feedforward super-twisting sliding mode controller for robotic manipulators. A full stability analysis based on a Lyapunov candidate is established showing a local asymptotic finite-time convergence of the proposed controller in the presence of upper bounded disturbances. Its robustness toward parametric uncertainties and system disturbances, thanks to the super-twisting approach, is pointed out. In addition, the feedforward dynamic term of the proposed controller that can compensate for the model nonlinearities is not sensitive to measurement noise. Real-time experiments have been conducted on two parallel manipulators: a 5-DOF SPIDER4 PKM and a 3-DOF Delta PKM. The effectiveness of the proposed controller is validated in different scenarios, including the nominal case and robustness toward parametric variations (payload) and speed changes

Sliding mode-based adaptive tube model predictive control for robotic manipulators with model uncertainty and state constraints

Sliding mode-based adaptive tube model predictive control for robotic manipulators with model uncertainty and state constraints

Dynamic Neural Network for Bicriteria Weighted Control of Robot Manipulators.

In recent years, bicriteria optimization schemes for manipulator control have become preferred by researchers, given their satisfactory performance. In this article, a bicriteria weighted (BCW) scheme to remedy joint drift and minimize the infinity norm of joint velocity is proposed. The scheme adopts a novel repetitive motion index that can theoretically decouple the joint error and the position error, which many conventional cyclic motion generation schemes cannot achieve. Subsequently, through transformation, the BCW scheme is converted into a time-varying quadratic programming (QP) problem. Then, a dynamic neural network (DNN) system with a new Fisher-Burmeister function is proposed to address the resulting QP problem. It is proven that the proposed DNN system is free of residual errors, which means that the actual solution is able to converge to the theoretical solution. Another essential feature of the DNN system is that it has a suppression effect on noise. To demonstrate the convergence and robustness of the proposed DNN system, comparative simulations are carried out in nominal and noisy environments. Finally, experiments on Franka Emika Panda are conducted to elucidate the availability of the BCW scheme addressed by the DNN system.

A Novel Decentralized Fixed-time Tracking Control for Modular Robot Manipulators: Theoretical and Experimental Verification

A Novel Decentralized Fixed-time Tracking Control for Modular Robot Manipulators: Theoretical and Experimental Verification

Adaptive fractional tracking control of robotic manipulator using fixed-time method

This paper proposes an adaptive fractional-order sliding mode controller to control and stabilize a nonlinear uncertain disturbed robotic manipulator in fixed-time. Fractional calculus is used to construct a fractional-order sliding mode controller (FtNTSM) that suppresses chattering to help the robotic manipulator converge to equilibrium in a fixed-settling time based on fixed-time stability theory. Then, adaptive control is introduced and combined with FtNTSM to overcome the unknown system dynamics. The convergence time of the proposed fixed-time fractional-order sliding mode controller (AFtNTSM) is independent of beginning circumstances and can be precisely assessed, unlike the finite-time control approach. Finally, numerical simulations show that the adaptive fractional-order sliding mode controller outperforms finite-time sliding mode controller.

Adaptive practical predefined-time neural tracking control for multi-joint uncertain robotic manipulators with input saturation

Adaptive practical predefined-time neural tracking control for multi-joint uncertain robotic manipulators with input saturation

Adaptive fault control for robot manipulators based on nonsingular terminal sliding mode control technique

This work studies to improve the efficiency of the tracking control system based on the nonsingular terminal sliding mode technique for the robot manipulators in case of sudden faults. In the proposed control strategy, the adaptability is first enhanced by the self-updating algorithms for all control gains of the main sliding mode controller. Next, the unknown dynamics and abrupt faults are estimated to ensure the robustness and tracking performances of the control system. In addition, the proposed control method has more advantages in addressing the inevitable updating/estimating errors and guaranteeing the continuity and smoothness of control signals through an added robust controller. And based on the Lyapunov theorem, the designed adaptive updating/estimating control strategy has guaranteed for the stability, robustness, and finite time convergence of the control system. In the end, the improved features of the proposed control method are verified by the compared numerical simulation results.

A Gravity Compensation Algorithm of Robot Manipulator Control based on the Trigonometric Function

The conventional gravity compensation algorithm requires precise dynamic parameters and a complex matrix transformation operation, which is difficult in applications to real-time control. In this paper, a simple and practical gravity compensation algorithm is proposed based on the space geometry characteristics of a mechanical arm and the principle of torque balance. This algorithm does not require a complex calculation of space coordinate transformation and does not require obtaining all accurate dynamic models and parameters. It only requires estimating the maximum gravity moment of the mechanical arm and simply calculating the trigonometric function. Thus, this algorithm can be extended to a non-parallel shaft mechanical arm, which is suitable for N joints in space. To verify the control effect after gravity compensation, the most easily comprehensible proportional-derivative controller combined with gravity compensation is used to control two-joint and three-joint mechanical arms for simulation. With the gravity compensation and non-compensation of the mechanical arm and with a comparison with other compensation methods, such as the fixed gravity compensation algorithm, the results show that the gravity compensation algorithm can achieve better trajectory tracking control, higher steady-state precision, an effectively reduced work burden of the controller and improved system stability.

Trajectory tracking controller of a robotized arm with joint constraints, a direct adaptive gain with state limitations approach

Motion restrictions in robotic devices may introduce complex requirements for any closed-loop control design, mainly when the robot joints must track reference trajectories that force the end-effector to perform planned motions. This study summarizes the comprehensive technical design of an adaptive state feedback controller for multi-link robotic manipulators that consider the effect of position and velocity restrictions on the tracking trajectory control approach. The proposed design is less conservative than other methods because of the explicit inclusion of state restrictions in the control gain dynamics. A logarithm barrier Lyapunov function class supports the design of the adaptive gain for the manipulator. Sufficient conditions based on a Riccati equation simplify the implementation of the adaptive controller with gains depending on the distance between the current state and the restriction sets. Numerical simulations show the advantages of the proposed controller with adaptive gains concerning a similar adaptive controller that does not consider the restrictions and a proportional–integral–derivative form. An implementation for the motion control of a robotic arm is presented to demonstrate the development by implementing the proposed gain, which confirms the suggested improvements enforced by the proposed controller. The performed comparison shows the advantages of the suggested adaptive gain control form, inducing better tracking of reference trajectories and smaller control energy applications.