Control Of Flexible Manipulator Research Articles

Event-triggered consensus tracking control of flexible manipulators with nonlinear time-varying fault-tolerant actuators and input constraint

This paper introduces a novel event-triggered consensus control strategy for a multi-agent system composed of underactuated flexible manipulators. Most existing research on consensus tracking control assumes that the model targeted is fully actuated, the control signal is continuous, and that both time-varying actuator failures and input constraints are managed separately. These challenges were tackled in this study within the context of multi-agent systems, addressing input constraints and nonlinear time-varying actuator failures simultaneously. Moreover, an event-triggered mechanism is proposed to reduce signal communication. A double closed-loop consensus tracking method is designed based on the multi-agent topology, enabling all flexible manipulators to track the angle and angular velocity of the leader and an adaptive control law is designed to solve actuator faults. Finally, the simulation results demonstrate the effectiveness of the proposed control scheme.

Active disturbance rejection control of flexible industrial manipulator: A MIMO benchmark problem

Robotic manipulators are widely used in modern industry. In order for robot manipulators to achieve trajectory tracking control, the end-effectors must move precisely along the given trajectories. The nonlinearity, strong coupling, and uncertainty of the system make trajectory tracking very complicated and difficult. An approach for robust feedback control is presented in this paper as a solution to this problem. The design relies on active disturbance rejection control to estimate unknown disturbances affecting the plant. The controlled system is an uncertain two-link manipulator with elastic gear transmissions, described by nonlinear friction and elasticity. It is based on an industrial benchmark problem. The model has two significant uncertainties: parametric uncertainty as well as external disturbances that affect tools and motors. A simulation-based evaluation of the proposed control method and a comparison of its performance, considering specific robustness requirements and trajectory tracking, are presented. The comparison includes the baseline controller, the QFD (Quality Function Deployment) method, and PD-CNN (PD Control Compensation based on the Cascade Neural Network). Computer simulations of the proposed controller are carried out to validate the ABB (Asea Brown Boveri) industrial benchmark. The simulation results demonstrate superiority in meeting stability and target value requirements. The stability and ultimate boundedness of the tracking error of ADRC (Active Disturbance Rejection Control) are demonstrated.

Adaptive non-singular fixed-time sliding mode control of moving-base underwater flexible manipulators

Adaptive non-singular fixed-time sliding mode control of moving-base underwater flexible manipulators

Observer-Based Output Feedback Tracking Control for Flexible Joint Space Manipulator with Output Constraints

Observer-Based Output Feedback Tracking Control for Flexible Joint Space Manipulator with Output Constraints

Nonlinear Observer-Based Visual Servoing and Vibration Control of Flexible Robotic Manipulators With a Fixed Camera.

This article studies the visual servoing and vibration suppression control for flexible manipulators when the system states are unmeasurable and only the image feedback is available. The dynamic equations of flexible manipulators are decomposed into the slow and fast subsystems based on the singular perturbation theory. The nonlinear observers based on the state transformation using the Lie derivatives are proposed to estimate the unmeasurable system states and unknown camera intrinsic parameters at the same time. Then, the image-based controllers utilizing the estimated states are, respectively, designed in the slow and fast subsystems to regulate the image positions of feature points and suppress the vibration of flexible manipulators simultaneously. In the proposed approach, only the visual feedback is required to generate the control input for flexible manipulators, which simplifies the controller implementation. The stability of the proposed control scheme is proved based on the Lyapunov theory. Finally, experimental results on a flexible single-link manipulator are provided to demonstrate the effectiveness of the proposed control approach.

Adaptive Fuzzy-Based Fault-Tolerant Boundary Control of Flexible Timoshenko Manipulators Under Directed Graph

Adaptive Fuzzy-Based Fault-Tolerant Boundary Control of Flexible Timoshenko Manipulators Under Directed Graph

Sliding mode control of Two-link Flexible Manipulator for reduced vibration in the presence of unmatched uncertainty and time-varying external disturbance

Sliding mode control of Two-link Flexible Manipulator for reduced vibration in the presence of unmatched uncertainty and time-varying external disturbance

Dynamic modeling and rotation control for flexible single-link underwater manipulator considering flowing water environment based on modified morison equation

Flexible single-link underwater manipulator (FSLUM) can be installed in autonomous underwater vehicles, it can carry out underwater tasks such as offshore oil and gas exploration, underwater cable maintenance as well as fishery farming and fishing. The design and development of the FSLUM are of great significance for ocean exploration and development. The FSLUM is affected by the underwater interference torque during underwater tasks in the flowing water environment, which will greatly affect the operational accuracy. In addition, many nonlinear factors such as the flexibility of the transmission system and the two-dimensional deformation of flexible links will aggravate the control difficulty of the FSLUM. An improved sliding mode control (SMC) strategy based on neural network identification is proposed to enhance the FSLUM's control accuracy. Neural networks are proposed to identify uncertain components including underwater interference and flexible nonlinear terms, so as to advance the control law design accuracy. Firstly, the FSLUM dynamic equations coupled with underwater interference torque considering multiple nonlinear factors are established according to the assumed mode method (AMM). A modified Morison equation is proposed to abbreviate the underwater interference torque in flowing water environments. Next, the control law of improved neural network sliding mode control (NNSMC) strategies are proposed according to the Lyapunov stability theorem, so as to ensure closed-loop stability of the FSLUM. Finally, the results of simulation and prototype tracking control experiments show that the improved NNSMC strategy has high tracking accuracy.

Performance analysis of input shaped model reference adaptive control for a single-link flexible manipulator

Motion control for flexible link manipulators will have a great influence on their overall performance. Conventional control systems have the problem that they cannot compensate for the presence of uncertainty due to a variety of factors such as a change in the system itself-payload variations, degradation and modelling uncertainty. In addition, the underdamped structure of these systems entails motion-induced transient deflection and residual vibration. On the contrary, adaptive control, in particular Model Reference Adaptive Control (MRAC), has therefore been proposed to handle problems of this type dealing with other systems such as cranes, steering stability control for ground vehicles, airplanes and so on. This study presents a review and discussion on the MRAC and some individual issues of MRAC for these flexible link manipulators are addressed to specify: the state tracking error, the limitation of the adaptation values corresponding to the control gains and the control effort. Dealing with the last one, this work proves that shaping the command input to a Model Reference Adaptive Control (IS-MRAC) reduces the control effort necessary for the plant to follow the reference model and also mitigates the detrimental effects of flexibility. The IS-MRAC is implemented in a Single-Link Flexible Manipulator whose natural frequencies, experimentally and analytically estimated using the Transfer Matrix Method plus symbolic computation, vary significantly depending on payload mass and position. To this structured or parametric uncertainty, the proposed Lyapunov control law using the states associated with the low mode is designed for asymptotic stability and accommodates correctly. The state tracking, vibration suppression and control effort reduction performances of the proposed IS-MRAC combined controller are analysed via numerical simulations and experiments.

A new fuzzy decoupled sliding mode control of flexible joint robotic manipulators based on the finite‐time observer in the presence of chaos with experimental validation

AbstractThis paper presents a new fuzzy non‐singular fast terminal decoupled sliding mode control based on the finite‐time extended state observer for trajectory tracking control of flexible joint robot manipulators under chaos and uncertainty. First, a suitable chaotic reference is used in a synchronization mechanism to create chaos in the dynamics. Then, the essential modifications made to the conventional decoupled sliding mode control are introduced. A new coupling variable and a non‐singular fast terminal sliding surface are proposed in combination with an extended state observer. A complete convergence analysis is also performed for all the sliding surfaces. Furthermore, two fuzzy systems are designed in such a way that not only they can eliminate the chattering phenomenon but can also improve the convergence time without increasing the control input in a voltage‐based manner. The mathematical proof guarantees the finite‐time asymptotic stability of the closed‐loop control system under the proposed scheme for a class of under‐actuated systems in the presence of structured and un‐structured uncertainties. Finally, some simulations and experimental implementations as hardware‐in‐the‐loop are performed to evaluate the proposed method's performance. The proposed controller is an effective method, as confirmed by the numerical simulation results and experimental validation.

Adaptive Quantized Control of Flexible Manipulators Subject to Unknown Dead Zones

This article proposes an adaptive control for a flexible manipulator (FM) under the influence of distributed disturbances, unknown dead zones, and input quantization. First, the hybrid effect of the unknown dead zone and input quantization is formulated and represented based on some essential transformations. Then, an adaptive robust quantized control with online updating laws is developed to address the uncertainty of the dead zone, ensure robustness and angle position, and dampen the vibration in the FM system. Subsequently, the Lyapunov theoretical analysis is employed to ensure the bounded stability of the system. Finally, numerical simulations and experiments with a Quanser platform are given to further verify the feasibility and superiority of the designed scheme.

Consensus tracking and vibration control for multiple single-link flexible manipulators with input signal quantization

This study examines consensus tracking and vibration control of a multiagent system consisting of multiple single-link flexible manipulators with input signal quantization. The dynamics of each flexible manipulator are described by fourth-order partial differential equations (PDEs). A logarithmic quantizer with changeable parameters is adopted to quantize control inputs, and adaptive laws are designed to compensate for the effect caused by quantization. A distributed adaptive boundary control protocol is proposed based on the PDE model to ensure all manipulators track the desired angular position and eliminate vibrations without knowledge of quantizer parameters. The proposed control protocol is proven to ensure the uniformly bounded stability of the closed-loop system. Simulations are carried out under three cases to illustrate the effectiveness of the designed strategy.

Deep reinforcement learning with reward shaping for tracking control and vibration suppression of flexible link manipulator

This paper puts forward a novel deep reinforcement learning control using deep deterministic policy gradient (DRLC-DDPG) framework to address the reference tracking and vibration suppression problem of rotary flexible link (RFL) manipulator. Specifically, this study attempts to address the continuous action space DRLC problem through DDPG algorithm and presents a Lyapunov function based reward shaping approach for guaranteed deep reinforcement learning (DRL) convergence and enhanced speed of training. The proposed approach synthesizes the hard and soft constraints of the flexible manipulator as a constrained Markov decision problem (MDP) and evaluates the performance of DRLC-DDPG framework through hardware in loop (HIL) testing to realize precise servo tracking and suppressed vibration of the flexible manipulator. For identifying the dynamical model of the RFL, an empirical Auto-Regressive eXogenous (ARX) model using the closed loop identification technique is built. Moreover, to extract the true states (servo angle and deflection angle) from the actual measurements, which typically have the influence of sensor noise, an adaptive Kalman filter (AKF) is augmented with the DRLC scheme. The experimental results of DRLC-DDPG scheme compared with those of the model predictive control (MPC) for several test cases reveal that the proposed scheme is superior to MPC both in terms of trajectory tracking and robustness against the external disturbances and model uncertainty.

Dynamic modeling and RBF neural network compensation control for space flexible manipulator with an underactuated hand

In space operation, flexible manipulators and gripper mechanisms have been widely used because of light weight and flexibility. However, the vibration caused by slender structures in manipulators and the parameter perturbation caused by the uncertainty derived from grasping mass variation cannot be ignored. The existence of vibration and parameter perturbation makes the rotation control of flexible manipulators difficult, which seriously affects the operation accuracy of manipulators. What’s more, the complex dynamic coupling brings great challenges to the dynamics modeling and vibration analysis. To solve this problem, this paper takes the space flexible manipulator with an underactuated hand (SFMUH) as the research object. The dynamics model considering flexibility, multiple nonlinear elements and disturbance torque is established by the assumed modal method (AMM) and Hamilton's principle. A dynamic modeling simplification method is proposed by analyzing the nonlinear terms. What's more, a sliding mode control (SMC) method combined with the radial basis function (RBF) neural network compensation is proposed. Besides, the control law is designed using a saturation function in the control method to weaken the chatter phenomenon. With the help of neural networks to identify the uncertainty composition in the SFMUH, the tracking accuracy is improved. The results of ground control experiments verify the advantages of the control method for vibration suppression of the SFMUH.

Vibration Control of Flexible Manipulators by Active Cable Tension

The end point vibrations of the serial manipulator should be controlled during motion or working process. In this study the residual vibrations of the flexible manipulator were controlled with cable tensions. The finite element model was established in ANSYS Mechanical APDL. The open loop and closed loop control simulations were performed under the trapezoidal velocity motion profiles. Zero and three different initial strain values were assigned to the cables. As a result, the end point vibration amplitudes, axial forces of the cables and the bending strain values of the one element near the fixed end were observed in order to define the limitations of the sensors and actuators which will be selected for experimental setup.

Distributed neural-based fault-tolerant control of multiple flexible manipulators with input saturations

This paper focuses on a novel fault-tolerant control design for networked flexible manipulators with actuator failures, input saturations, and external disturbances. Unlike the existing methods, the new control strategy has three characteristics. First, the second-order auxiliary system is used to eliminate the influence of input saturation. Second, by designing some proportional–integral (PI) auxiliary variables, the boundary dynamics of manipulators are reconstructed as some new second-order systems, where the nonlinear uncertainties are estimated by the adaptive radial basis neural networks (RBFNNs) and the compound disturbances are compensated by the adaptive method. Third, a boundary local angle interaction protocol is given under a digraph. Then combining the virtual control and Lyapunov design, some novel distributed fault-tolerant control algorithms are obtained to ensure that the vibration states and the angle consensus error are uniformly ultimately bounded. Finally, numerical simulations are provided to show the effectiveness of these control algorithms.

Nussbaum function‐based adaptive boundary control for flexible manipulator with unknown control directions and nonlinear time‐varying actuator faults

AbstractThis study proposes a novel boundary control strategy for a vibrating single‐link flexible manipulator system modeled by partial differential equations. The system has uncertain control directions and actuator faults. Existing research studies model fault‐tolerant control systems with fixed fault coefficients; in this study, the actuator faults are nonlinear and time‐varying, and the control directions are unknown. To circumvent the obstacles introduced by the unknown control directions and nonlinear varying actuator failures, an adaptive boundary controller is designed by using Nussbaum functions with fast growth rates and the bound estimation method. In the developed control scheme, all closed‐loop signals are shown to be uniformly bounded. Additionally, the angle tracking error and vibration asymptotically converge to zero. Finally, numerical simulations for five cases demonstrate the effectiveness of the modeled boundary controller.

Adaptive Fuzzy Containment and Vibration Control for Multiple Flexible Manipulators With Model Uncertainties

This article investigates the cooperative vibration control problem for a flexible manipulator network with model uncertainties and boundary disturbances guided by multiple dynamic leaders. Different from the previous research on a single flexible manipulator, this article focuses on the containment control problem of multiple flexible manipulators. To this end, the boundary control algorithm is proposed for leader agents without disturbance and follower agents with disturbance and uncertainties. The fuzzy logic systems are applied to deal with uncertain and continuous functions in the system model. By constructing the appropriate Lyapunov function, not only the vibrations can be suppressed, but also the containment error between the convex hull spanned by the multiple leaders and all followers can be converged to zero by designing a distributed containment feedback control law. Furthermore, the control algorithm can ensure that all the signals in the closed-loop system are uniformity ultimately bounded; meanwhile, vibration amplitude and error converge to a small compact set. At last, the simulation results prove the effectiveness of the proposed control method.

PDE-based boundary event-triggered control of uncertain flexible manipulator with input nonlinearities

PDE-based boundary event-triggered control of uncertain flexible manipulator with input nonlinearities

A practical study of active disturbance rejection control for rotary flexible joint robot manipulator

A practical study of active disturbance rejection control for rotary flexible joint robot manipulator