Adaptive Neural Network Control for Full-State Constrained Robotic Manipulator With Actuator Saturation and Time-Varying Delays

This article proposes an adaptive neural network (NN) control method for an n -link constrained robotic manipulator. Driven by actual demands, manipulator and actuator dynamics, state and input constraints, and unknown time-varying delays are taken into account simultaneously. NNs are employed to approximate unknown nonlinearities. Time-varying barrier Lyapunov functions are utilized to cope with full-state constraints. By resorting to saturation function and Lyapunov-Krasovskii functionals, the effects of actuator saturation and time delays are eliminated. It is proved that all the closed-loop signals are semiglobally uniformly ultimately bounded, full-state constraints and actuator saturation are not violated, and error signals remain within compact sets around zero. Simulation studies are given to demonstrate the validity and advantages of this control scheme.

Bio-Motivated Two-Level Event-Triggered Controller for Nonlinear Systems

In this article, a biologically motivated two-level event-triggered mechanism is proposed to design a neuroadaptive controller with exponential convergence property. Specifically, an exponential adaptive neural network controller is designed, and a two-level event-triggered mechanism is developed for a class of nonlinear systems. The two-level event-triggered mechanism, which incorporates both static and dynamic event-triggered features, is motivated by the biological response to low- and high-speed changes in the environment. We also introduce a method in which time-varying control gain is used to achieve exponential convergence of the plant state. The effectiveness of the proposed control scheme is validated by numerical simulations. The minimal interevent time internal is lower bounded by a positive number, so no Zeno behavior occurs.

Adaptive Fuzzy Backstepping Sliding Mode Control for a 3-DOF Hydraulic Manipulator with Nonlinear Disturbance Observer for Large Payload Variation

The paper proposes an adaptive fuzzy position control for a 3-DOF hydraulic manipulator with large payload variation. The hydraulic manipulator uses electrohydraulic actuators as primary torque generators to enhance carrying payload of the manipulator. The proposed control combines backstepping sliding mode control, fuzzy logic system (FLS), and a nonlinear disturbance observer. The backstepping sliding mode control includes a sliding mode control for manipulator dynamics and a PI control for actuator dynamics. The fuzzy logic system is utilized to adjust the control gain and robust gain of the sliding mode control (SMC) based on the output of the nonlinear disturbance observer to compensate the payload. The Lyapunov approach and backstepping technique are used to prove the stability and robustness of the whole system. Some simulations are implemented, and the results are compared to other controllers to exhibit the effectiveness of the proposed control.

Backstepping Design of Adaptive Neural Fault-Tolerant Control for MIMO Nonlinear Systems

In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in . In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in . In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.

Time-optimal path tracking for robots under dynamics constraints based on convex optimization

To fully utilize the dynamic performance of robotic manipulators and enforce minimum motion time in path tracking, the problem of minimum time path tracking for robotic manipulators under confined torque, change rate of the torque, and voltage of the DC motor is considered. The main contribution is the introduction of the concepts of virtual change rate of the torque and the virtual voltage, which are linear functions in the state and control variables and are shown to be very tight approximation to the real ones. As a result, the computationally challenging non-convex minimum time path tracking problem is reduced to a convex optimization problem which can be solved efficiently. It is also shown that introducing dynamics constraints can significantly improve the motion precision without costing much in motion time, especially in the case of high speed motion. Extensive simulations are presented to demonstrate the effectiveness of the proposed approach.

Adaptive control of rigid-link electrically driven robots with parametric uncertainties in kinematics and dynamics and without acceleration measurements

In this paper, the backstepping strategy is used to design an adaptive tracking controller for rigid-link electrically driven robots in the presence of parametric uncertainties in kinematics, manipulator dynamics, and actuator dynamics. To avoid acceleration measurements, two techniques are exploited. One technique adds compensation control terms to the control law signal. The other uses a linear in variable property of the Jacobian matrix. Global asymptotic convergence of the end-effector motion tracking errors is shown via Lyapunov analysis. Simulation results are presented to show the effectiveness of the proposed control scheme.

Exponential Tracking Control Using Backstepping Approach for Voltage-Based Control of a Flexible Joint Electrically Driven Robot

This paper addresses the design of exponential tracking control using backstepping approach for voltage-based control of a flexible joint electrically driven robot (EFJR), to cope with the difficulty introduced by the cascade structure in EFJR dynamic model, to deal with flexibility in joints, and to ensure fast tracking performance. Backstepping approach is used to ensure global asymptotic stability and its common algorithm is modified such that the link position and velocity errors converge to zero exponentially fast. In contrast with the other backstepping controller for electrically driven flexible joint robot manipulators control problem, the proposed controller is robust with respect to stiffness uncertainty and allows tracking fast motions. Simulation results are presented for both single link flexible joint electrically driven manipulator and 2-DOF flexible joint electrically driven robot manipulator. These simulations show very satisfactory tracking performances and the superiority of the proposed controller to those performed in the literature using simple backstepping methodology.

Fuzzy-neural-network inherited sliding-mode control for robot manipulator including actuator dynamics

This paper presents the design and analysis of an intelligent control system that inherits the robust properties of sliding-mode control (SMC) for an n-link robot manipulator, including actuator dynamics in order to achieve a high-precision position tracking with a firm robustness. First, the coupled higher order dynamic model of an n-link robot manipulator is briefy introduced. Then, a conventional SMC scheme is developed for the joint position tracking of robot manipulators. Moreover, a fuzzy-neural-network inherited SMC (FNNISMC) scheme is proposed to relax the requirement of detailed system information and deal with chattering control efforts in the SMC system. In the FNNISMC strategy, the FNN framework is designed to mimic the SMC law, and adaptive tuning algorithms for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. Numerical simulations and experimental results of a two-link robot manipulator actuated by DC servo motors are provided to justify the claims of the proposed FNNISMC system, and the superiority of the proposed FNNISMC scheme is also evaluated by quantitative comparison with previous intelligent control schemes.

A MODEL-FREE APPROACH FOR ACCURATE JOINT MOTION CONTROL IN HUMANOID LOCOMOTION

A new model-free approach to precisely control humanoid robot joints is presented in this article. An input–output online identification procedure will permit to compensate neglected or uncertain dynamics, such as, on the one hand, transmission and compliance nonlinear effects, and, on the other hand, network transmission delays. Robustness to parameter variations will be analyzed and compared to other advanced PID-based controllers. Simulations will show that not only good tracking quality can be obtained with this novel technique, but also that it provides a very robust behavior to the closed-loop system. Furthermore, a locomotion task will be tested in a complete humanoid simulator to highlight the suitability of this control approach for such complex systems.

Adaptive impedance controller design for flexible-joint electrically-driven robots without computation of the regressor matrix

To the best of our knowledge, this is the first paper focus on the adaptive impedance control of robot manipulators with consideration of joint flexibility and actuator dynamics. Controller design for this problem is difficult because each joint of the robot has to be described by a fifth-order cascade differential equation. In this paper, a backstepping-like procedure incorporating the model reference adaptive control strategy is employed to construct the impedance controller. The function approximation technique is applied to estimate time-varying uncertainties in the system dynamics. The proposed control law is free from the calculation of the tedious regressor matrix, which is a significant simplification in implementation. Closed-loop stability and boundedness of internal signals are proved by the Lyapunov-like analysis with consideration of the function approximation error. Computer simulation results are presented to demonstrate the usefulness of the proposed scheme.

PD control with feedforward compensation for rigid robots actuated by brushless DC motors

This work is concerned with trajectory tracking of robots when the electrical dynamics of the brushless DC motor actuators is considered. It is shown that proportional-derivative (PD) control with feedforward compensation, plus some additional terms to cope with the electrical dynamics, ensures state boundedness. Furthermore, tracking error converges to zero from arbitrarily large initial conditions if controller gains are correctly chosen. Under mild assumptions, this controller reduces to the well-known PD control with feedforward compensation when implemented according to torque control, a successful industrial practice. Thus, it is explained, for the first time, why this strategy works well in applications.

Integration of a plug-and-play desktop robotic system

This paper describes the development of a desktop robotic system that enables the plug-and-play function through the USB (universal serial bus) port of a personal computer (PC). Thus a new kind of desktop PC peripheral is invented that has programmable manipulability. The robotic system is realized on an internally distributed control structure that facilitates higher system reliability. A PID control algorithm is implemented on a prototype of the proposed system, to demonstrate the system's ability to implement feedback control. Experimental results show the performance and properties of the proposed system.

In situ measurement of motor electrical constants

Motor torque constant is an important parameter in modeling and controlling a robot axis. In practice this parameter can vary considerably from the manufacturer's specification, if available, and this makes it desirable to characterise individual motors. Traditional techniques require that the motor can be removed from the robot for testing, or that an elaborate technique involving weights and pulleys be employed. This paper describes a novel method for measuring the torque constant of robot servo motors in situ and is based on the equivalence of motor torque and back EMF constants. It requires a very simple experimental procedure, utilizes existing axis position sensors, and eliminates effects due to static friction and joint cross coupling. A straightforward extension to this approach can provide a measurement of motor armature impedance. Experimental results obtained for a Puma 560 are discussed and compared with other published results.

PID control of robot manipulators equipped with brushless DC motors

This paper is concerned with PID control of rigid robots equipped with brushless DC (BLDC) motors when the electric dynamics of these actuators is taken into account. We show that an adaptive PID controller yields global stability and global convergence to the desired link positions. Moreover, we also show that virtually the PID part of the controller suffices to achieve the reported global results. We present a theoretical justification for the torque control strategy, commonly used in practice to control BLDC motors. Our controller does not require the exact knowledge of neither robot nor actuator parameters.

Adaptive fuzzy approach for a class of uncertain nonlinear systems in strict-feedback form

Adaptive fuzzy control is proposed for a class of affine nonlinear systems in strict-feedback form with unknown nonlinearities. The unknown nonlinearities include two types of nonlinear functions: one satisfies the "triangularity condition" and can be directly approximated by fuzzy logic system, while the other is assumed to be partially known and consists of parametric uncertainties. Takagi-Sugeno type fuzzy approximators are used to approximate unknown system nonlinearities and the design procedure is a combination of adaptive backstepping and generalized small gain design techniques. It is proved that the proposed adaptive control scheme can guarantee the uniformly ultimately bounded (UBB) stability of the closed-loop systems. Simulation studies are shown to illustrate the effectiveness of the proposed approach.