An improve nonlinear robust control approach for robotic manipulators with PSO-based global optimization strategy

For accurate trajectory tracking of robotic manipulators with actuators, a novel nonsingular fast terminal sliding mode control (NFTSMC) strategy based on radial basis function neural network (RBFNN) is put forward and investigated in this paper. Because of the existence of nonsingular fast terminal sliding mode (NFTSM) manifold, the controller possesses high precision and fast convergence. Considering that it is difficult to obtain accurate model parameters owing to modeling errors or external disturbances, RBFNN is used to approximate the nonlinear uncertainties due to its simple structure and great generalization ability. A new adaptive law is designed to adjust RBFNN. In order to compensate the estimation errors and suppress other unstable factors, a robust term is introduced. A new adaptive law is developed to flexibly adjust the robust term. Then, Lyapunov theory is applied to prove the system stability and finite-time convergence. Finally, a small-sized industrial robotic manipulator Epson LS3-401S with its first two joints is taken as the simulation plant, and several simulations between the proposed controller and the other two controllers are performed. External disturbances and other two conditions are considered to simulate the real environment, and the corresponding results verify the effectiveness and superiority of the proposed controller.

Nonlinear active disturbance rejection control is much more effective than linear active disturbance rejection control in tolerance to uncertainties and disturbances. However, it brings a great challenge for theoretical analysis, especially the stability analysis. This article proposes a linear matrix inequality method to analyze the absolute stability of generalized nonlinear active disturbance rejection control form which contains multiple nonlinearities with different parameters in both extended state observer and control law for single-input single-output systems. The generalized nonlinear active disturbance rejection control algorithm and the single-input single-output system are transformed into a direct multiple-input multiple-output Lurie system. A sufficient condition to determine its absolute stability based on linear matrix inequality method is given. The Lyapunov function of the Lurie system exists when the group of linear matrix inequalities is feasible. The free parameters and coefficients in Lyapunov function are given by the solution of these linear matrix inequalities. The electromagnetic valve actuator system in camless engine is presented as an application to illustrate how to perform the proposed method for absolute stability analysis and the stable region of parameter perturbations is obtained via the method. Simulation results show that the linear matrix inequality–based method is convenient and effective to determine whether the closed-loop system is absolutely stable.

This paper considers the pathconstrained trajectory tracking for robot manipulators optimizing a limited energy budget. The proposed strategy is based on a Nonlinear Receding Horizon Predictive Control (NRHPC) using a path parameterization of dimension one. The dynamic of the parameterized trajectory is governed by a predefined linear system, then an energy and a cost functions are defined and a NRHPC based on a Newton method is used to minimize the cost function in real time. The method is presented in both joint and task space. The proposed solution is verified on a 4 DOF manipulator with successful simulation and experimental results.

This paper highlights, neural network (NN) based adaptive control using backstepping control technique is proposed for robot manipulator trajectory tracking. Firstly, the vector of current is considered as the control variable for robot manipulator mechanical subsystem by using the adaptive update algorithm of NN and an enclosed control input for the desired vector of current is constructed. So that, the goal of trajectory tracking of robot manipulator is achieved. Secondly, the voltage commands are constructed in order to control the joint currents to follow the anticipated value by using the NN controller in order to manipulate the dynamics of DC motor. Simplicity of control law is achieved by using proposed control technique along with low computational cost. In addition, robot manipulator and its actuator dynamics does not require the mathematical representation of model. The weight values of NN's and robotic manipulator parameters are adaptively updated. The efficiency and usefulness of proposed scheme on 2-DOF robot manipulator is analyzed by using the running mean error. The results depicts that the proposed model out perform than the conventional PD controller in terms of enhanced robotic manipulator trajectory tracking.

This article proposes an enhanced adaptive time delay controller (ATDC) for robot manipulators subjected to external disturbances. This approach is model-free and uses time delay estimation (TDE) to estimate complex and unknown robot dynamics. First, to remove the reaching phase and improve the robustness of the control, an integral sliding surface (ISS) is used. Second, to speed up the convergence and compensate for the TDE errors and external disturbances, the gains of time delay control and the sliding mode have been made adaptive. In this article, we clearly point out using the Lyapunov method that the position and velocity tracking errors are guaranteed to be uniformly ultimately bounded (UUB). The effectiveness and the robustness of the designed controller over existing methods are experimentally verified on a parallel Delta robot. The novel model-free proposed control is robust and highly accurate, which is appropriate and recommended for industrial robotic applications.

This paper develops a novel non-singular terminal sliding mode control scheme for trajectory tracking of robot manipulators in the presence of external disturbances and uncertainties. Firstly, with the introduction of two nonlinear terms, a newly terminal sliding surface is designed. Then utilizing this sliding surface, a novel non-singular terminal sliding mode controller is proposed to eliminate the reaching interval, singularity issue and subsequently with this controller, the finite time error convergence is also assured. In the proposed controller, radial basis function neural network is employed to approximate highly uncertain nonlinear dynamics of robot manipulators using update laws derived with Lyapunov approach. Meanwhile, the effects of approximation errors are attenuated with $$H_\infty $$ performance criterion by introducing a robust term into the controller. As a result of proposed approach, asymptotic convergence of tracking errors is achieved within finite time and the approximation errors are attenuated to desired levels. The numerical simulation result shows the effectiveness of proposed controller for the case of microbot type robot manipulator.

This study addresses the trajectory tracking control challenges of robot manipulators with uncertain dynamics. The aim is to achieve precise and smooth trajectory regulation through a novel composite position predictive control (PPC) scheme that integrates motion profile and disturbance preview techniques. First, we perform offline dynamics identification and feedforward compensation alongside a pre-defined motion profile. To handle the disturbances arising from uncertain dynamics, a super-twisting disturbance observer is designed, resulting in a dynamically compensated prediction model. Furthermore, the receding optimization operations for PPC are executed by solving an optimal solution associated with a joint angle tracking error. The combination of feedforward and feedback control improves the robot manipulator’s absolute positioning accuracy as opposed to the conventional model predictive control method, especially when dealing with uncertain dynamics. The effectiveness of the proposed control method is confirmed through trajectory tracking experiments conducted on a six-degree-of-freedom robot platform with varying end-effector loads. The experimental results demonstrate that the proposed PPC method enhances tracking accuracy by approximately 45% and 25% when compared to the traditional inverse dynamic control (IDC) and the robust IDC approaches, respectively.

This paper presents a sliding mode controller for trajectory tracking of robotic manipulators with actuator faults. To achieve finite-time convergence of tracking errors and desirable tracking precision, a fast nonsingular terminal sliding mode controller is designed. The uncertainty caused by actuator faults is transformed into system disturbance which is compensated by introducing the dynamic gain technique. As a result, the prior knowledge about upper bound of system disturbance is not required. Moreover, the sign function is replaced by the saturation function in the control law, so the chattering phenomenon is attenuated. Finally, the designed controller is applied to control a two-link robotic manipulator in simulation, and results validate the effectiveness of the control scheme.

Adaptive neural network control with optimal number of hidden nodes for trajectory tracking of robot manipulators

Neural adaptive controller design of robotic manipulators with an observer

This article presents a novel reinforcement learning (RL)-based control scheme for trajectory tracking of robot manipulators, incorporating an uncertainty estimator and an optimal tracking controller. A momentum observer (MO) is implemented in conjunction with an integral sliding mode control (ISMC) to facilitate uncertainty estimation for compensation. A proportional-derivative-like (PD-like) control plus an actor-critic neural network (NN)-based feedforward control is utilized for trajectory tracking. An NN parameter selection scheme is adopted to circumvent the time-consuming adjustments of its activation functions and initial weights, thereby ensuring the admissibility of the initial control policy and improving its efficiency. Lyapunov function analysis demonstrates stability of the closed-loop system, in which all error signals are shown to remain bounded and eventually settle into a small residual set. The proposed control scheme is compared with a conventional PD approach consisting of a feedforward controller and an NN controller with adaptive radial basis functions (RBFs). Comparison is also made with a recent approach featuring a feedforward super-twisting sliding mode control (FSTSMC). Simulation and experimental results show superior tracking performance of the presented control scheme against the comparative counterparts, validating the new approach and further supporting its effectiveness and feasibility.

The control stability and accuracy of quad tiltrotor UAVs is improved when encountering external disturbances during automatic flight by an active disturbance rejection control (ADRC) parameter self-tuning control strategy based on a radial basis function (RBF) neural network. Firstly, a nonlinear flight dynamics model of the quad tiltrotor UAV is established based on the approach of component-based mechanistic modeling. Secondly, the effects of internal uncertainties and external disturbances on the model are eliminated, whilst the online adaptive parameter tuning problem for the nonlinear active disturbance rejection controller is addressed. The superior nonlinear function approximation capability of the RBF neural network is then utilized by taking both the control inputs computed by the controller and the system outputs of the quad tiltrotor model as neural network inputs to implement adaptive parameter adjustments for the Extended State Observer (ESO) component responsible for disturbance estimation and the Nonlinear State Error Feedback (NLSEF) control law of the active disturbance rejection controller. Finally, an adaptive attitude control system for the quad tiltrotor UAV is constructed, centered on the ADRC-RBF controller. Subsequently, the efficacy of the attitude control system is validated through simulation, encompassing a range of flight conditions. The simulation results demonstrate that the Integral of Absolute Error (IAE) of the pitch angle response controlled by the ADRC-RBF controller is reduced to 37.4° in comparison to the ADRC controller in the absence of external disturbance in the full-states mode state of the quad tiltrotor UAV, and the oscillation amplitude of the pitch angle response controlled by the ADRC-RBF controller is generally reduced by approximately 50% in comparison to the ADRC controller in the presence of external disturbance. In comparison with the conventional ADRC controller, the proposed ADRC-RBF controller demonstrates superior performance with regard to anti-disturbance capability, adaptability, and tracking accuracy.

In this paper, an improved nonlinear active disturbance rejection control (INLADRC) approach is proposed for load frequency control of hybrid microgrid with communication delay. The INLADRC technique includes a nonlinear extended state observer (NESO) to estimate the system states and a nonlinear controller for the cancellation of the generalized disturbance acting on system. The effect of communication delay in the hybrid microgrid system is alleviated via the incorporation of an auxiliary structure that also restores the synchronization between the inputs of NESO. The efficacy of the proposed approach is validated via a comprehensive analysis, wherein step variations as well as random perturbations are considered in solar power, wind power and load demand. Further, the performance of proposed technique is also corroborated via consideration of real time solar and wind data. Moreover, the robustness of INLADRC technique is investigated in presence of parametric uncertainty, model-plant mismatch, delay time mismatch as well as varying communication delay. An extensive comparative analysis is a testimony to the effectiveness of INLADRC approach.

Active disturbance rejection control is proposed to study the problems on trajectory tracking and decoupling for a two-joint system driven by pneumatic artificial muscles. Because of coupling and nonlinearity, it is very hard to obtain good performance in trajectory tracking for the two-joint system. The active disturbance rejection nonlinear controller includes a tracking differentiator, an extended state observer and a nonlinear error feedback controller. The tracking differentiator is introduced to arrange a transitional process to track input signal and get differential signal. The extended state observer is designed to estimate the non-linearity of system. The nonlinear error feedback controller is designed to improve the performance of system. Simulation results are given to show the effectiveness of proposed controller in this paper.

Non-singular terminal sliding-mode control for a manipulator robot using a barrier Lyapunov function