Vibration Control of a Flexible Robotic Manipulator in the Presence of Input Deadzone
TL;DR: A neural network (NN) controller is designed to suppress the vibration of a flexible robotic manipulator system with input deadzone and is able to compensate for the estimated deadzone effect and track the desired trajectory.
Abstract: In this paper, a neural network (NN) controller is designed to suppress the vibration of a flexible robotic manipulator system with input deadzone. The NN aims to approximate the unknown robotic manipulator dynamics and eliminate the effects of input deadzone in the actuators. In order to describe the system more accurately, the model of the flexible manipulator is constructed based on the lumping spring-mass method. Full state feedback NN control is proposed first and output feedback NN control with a high-gain observer is then devised to make the proposed control scheme more practical. The effect of input deadzone is approximated by a radial basis function neural network (RBFNN) and the unknown dynamics of the manipulator is approximated by another RBFNN. The proposed NN control is able to compensate for the estimated deadzone effect and track the desired trajectory. For the stability analysis, the Lyapunov's direct method is used to ensure uniform ultimate boundedness (UUB) of the closed-loop system. Simulations are given to verify the control performance of the NN controllers comparing with the proportional derivative (PD) controller. At last, the experiments are conducted on the Quanser platform to further prove the feasibility and control performance of the NN controllers.
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TL;DR: With the proposed control, the stability of the closed-loop system is achieved via Lyapunov’s stability theory, and the tracking performance is guaranteed under the condition of state constraints and uncertainty.
Abstract: This paper investigates adaptive fuzzy neural network (NN) control using impedance learning for a constrained robot, subject to unknown system dynamics, the effect of state constraints, and the uncertain compliant environment with which the robot comes into contact. A fuzzy NN learning algorithm is developed to identify the uncertain plant model. The prominent feature of the fuzzy NN is that there is no need to get the prior knowledge about the uncertainty and a sufficient amount of observed data. Also, impedance learning is introduced to tackle the interaction between the robot and its environment, so that the robot follows a desired destination generated by impedance learning. A barrier Lyapunov function is used to address the effect of state constraints. With the proposed control, the stability of the closed-loop system is achieved via Lyapunov’s stability theory, and the tracking performance is guaranteed under the condition of state constraints and uncertainty. Some simulation studies are carried out to illustrate the effectiveness of the proposed scheme.
498 citations
413 citations
TL;DR: The trajectory tracking problem for a fully actuated autonomous underwater vehicle (AUV) that moves in the horizontal plane is investigated and two neural networks, including a critic and an action NN, are integrated into the adaptive control design.
Abstract: In this paper, we investigate the trajectory tracking problem for a fully actuated autonomous underwater vehicle (AUV) that moves in the horizontal plane. External disturbances, control input nonlinearities and model uncertainties are considered in our control design. Based on the dynamics model derived in the discrete-time domain, two neural networks (NNs), including a critic and an action NN, are integrated into our adaptive control design. The critic NN is introduced to evaluate the long-time performance of the designed control in the current time step, and the action NN is used to compensate for the unknown dynamics. To eliminate the AUV’s control input nonlinearities, a compensation item is also designed in the adaptive control. Rigorous theoretical analysis is performed to prove the stability and performance of the proposed control law. Moreover, the robustness and effectiveness of the proposed control method are tested and validated through extensive numerical simulation results.
370 citations
TL;DR: A decentralized adaptive formation controller is designed that ensures uniformly ultimate boundedness of the closed-loop system with prescribed performance and avoids collision between each vehicle and its leader.
Abstract: This paper addresses a decentralized leader–follower formation control problem for a group of fully actuated unmanned surface vehicles with prescribed performance and collision avoidance. The vehicles are subject to time-varying external disturbances, and the vehicle dynamics include both parametric uncertainties and uncertain nonlinear functions. The control objective is to make each vehicle follow its reference trajectory and avoid collision between each vehicle and its leader. We consider prescribed performance constraints, including transient and steady-state performance constraints, on formation tracking errors. In the kinematic design, we introduce the dynamic surface control technique to avoid the use of vehicle's acceleration. To compensate for the uncertainties and disturbances, we apply an adaptive control technique to estimate the uncertain parameters including the upper bounds of the disturbances and present neural network approximators to estimate uncertain nonlinear dynamics. Consequently, we design a decentralized adaptive formation controller that ensures uniformly ultimate boundedness of the closed-loop system with prescribed performance and avoids collision between each vehicle and its leader. Simulation results illustrate the effectiveness of the decentralized formation controller.
273 citations
Cites methods from "Vibration Control of a Flexible Rob..."
...Based on the dynamic surface control (DSC) technique [27]–[29], the PPC methodology [21], adaptive control techniques [30]–[32], neural network (NN) approximators [33]– [37], the backstepping procedure, and the Lyapunov synthesis, we propose decentralized leader–follower formation control that guarantees the satisfaction of the prescribed performance constraints on formation tracking errors and the nonviolation of the collision avoidance constraint between the follower and its leader despite the presence of modeling uncertainties and external disturbances....
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TL;DR: A novel distributed event-triggered communication protocol based on state estimates of neighboring agents is proposed to solve the consensus problem of the leader-following systems and can greatly reduce the communication load of multiagent networks.
Abstract: In this paper, the leader-following consensus problem of high-order multiagent systems via event-triggered control is discussed. A novel distributed event-triggered communication protocol based on state estimates of neighboring agents is proposed to solve the consensus problem of the leader-following systems. We first investigate the consensus problem in a fixed topology, and then extend to the switching topologies. State estimates in fixed topology are only updated when the trigger condition is satisfied. However, state estimates in switching topologies are renewed with two cases: 1) the communication topology is switched or 2) the trigger condition is satisfied. Clearly, compared to continuous-time interaction, this protocol can greatly reduce the communication load of multiagent networks. Besides, the event-triggering function is constructed based on the local information and a new event-triggered rule is given. Moreover, “Zeno behavior” can be excluded. Finally, we give two examples to validate the feasibility and efficiency of our approach.
269 citations
References
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01 Sep 1998
TL;DR: This graduate text provides an authoritative account of neural network (NN) controllers for robotics and nonlinear systems and gives the first textbook treatment of a general and streamlined design procedure for NN controllers.
Abstract: From the Publisher:
This graduate text provides an authoritative account of neural network (NN) controllers for robotics and nonlinear systems and gives the first textbook treatment of a general and streamlined design procedure for NN controllers. Stability proofs and performance guarantees are provided which illustrate the superior efficiency of the NN controllers over other design techniques when the system is unknown. New NN properties, such as robustness and passivity are introduced, and new weight tuning algorithms are presented. Neural Network Control of Robot Manipulators and Nonlinear Systems provides a welcome introduction to graduate students, and an invaluable reference to professional engineers and researchers in control systems.
1,337 citations
Book•
01 Jan 2008
TL;DR: In this paper, the authors present an introduction to backstepping, an elegant new approach to boundary control of partial differential equations (PDEs). Backstepping provides mathematical tools for converting complex and unstable PDE systems into elementary, stable, and physically intuitive target PDEs that are familiar to engineers and physicists.
Abstract: This concise and highly usable textbook presents an introduction to backstepping, an elegant new approach to boundary control of partial differential equations (PDEs). Backstepping provides mathematical tools for converting complex and unstable PDE systems into elementary, stable, and physically intuitive "target PDE systems" that are familiar to engineers and physicists. The text s broad coverage includes parabolic PDEs; hyperbolic PDEs of first and second order; fluid, thermal, and structural systems; delay systems; PDEs with third and fourth derivatives in space; real-valued as well as complex-valued PDEs; stabilization as well as motion planning and trajectory tracking for PDEs; and elements of adaptive control for PDEs and control of nonlinear PDEs. It is appropriate for courses in control theory and includes homework exercises and a solutions manual that is available from the authors upon request. Audience: This book is intended for both beginning and advanced graduate students in a one-quarter or one-semester course on backstepping techniques for boundary control of PDEs. It is also accessible to engineers with no prior training in PDEs. Contents: List of Figures; List of Tables; Preface; Introduction; Lyapunov Stability; Exact Solutions to PDEs; Parabolic PDEs: Reaction-Advection-Diffusion and Other Equations; Observer Design; Complex-Valued PDEs: Schrodinger and Ginzburg Landau Equations; Hyperbolic PDEs: Wave Equations; Beam Equations; First-Order Hyperbolic PDEs and Delay Equations; Kuramoto Sivashinsky, Korteweg de Vries, and Other Exotic Equations; Navier Stokes Equations; Motion Planning for PDEs; Adaptive Control for PDEs; Towards Nonlinear PDEs; Appendix: Bessel Functions; Bibliography; Index
1,059 citations
"Vibration Control of a Flexible Rob..." refers background in this paper
...In [4], a pioneering backstepping based boundary control strategy is proposed for various flexible structures....
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TL;DR: It is proved that the proposed design method is able to guarantee semi-global uniform ultimate boundedness of all signals in the closed-loop system, with arbitrary small tracking error by appropriately choosing design constants.
688 citations
01 Mar 2016
TL;DR: In this article, an adaptive impedance controller for a robotic manipulator with input saturation was developed by employing neural networks. But the adaptive impedance control was not considered in the tracking control design, and the input saturation is handled by designing an auxiliary system.
Abstract: In this paper, adaptive impedance control is developed for an ${n}$ -link robotic manipulator with input saturation by employing neural networks. Both uncertainties and input saturation are considered in the tracking control design. In order to approximate the system uncertainties, we introduce a radial basis function neural network controller, and the input saturation is handled by designing an auxiliary system. By using Lyapunov’s method, we design adaptive neural impedance controllers. Both state and output feedbacks are constructed. To verify the proposed control, extensive simulations are conducted.
685 citations
"Vibration Control of a Flexible Rob..." refers methods in this paper
...An adaptive NN control is proposed in [25] for...
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