A Neural Net Predictive Control for Telerobots with Time Delay

This paper extends the Smith Predictor feedback control structure to unknown robotic systems in a rigorous fashion. A new recurrent neural net predictive control (RNNPC) strategy is proposed to deal with input and feedback time delays in telerobotic systems. The proposed control structure consists of a local linearized subsystem and a remote predictive controller. In the local linearized subsystem, a recurrent neural network (RNN) with on-line weight tuning algorithm is employed to approximate the dynamics of the time-delay-free nonlinear plant. The remote controller is a modified Smith predictor for the local linearized subsystem which provides prediction and maintains the desirable tracking performance. Stability analysis is given in the sense of Lyapunov. The result is an adaptive compensation scheme for unknown telerobotic systems with time delays, uncertainties, and external disturbances. A simulation of a two-link robotic manipulator is provided to illustrate the effectiveness of the proposed control strategy.     

Stabilization of remote control systems with unknown time varying delays by LMI techniques

In this paper, the stabilization of one class of remote control systems with unknown time varying delays is analysed and discussed using LMI techniques. A discrete time state space model under a static control law for remote control systems is first introduced based on some assumptions on the uncertain term. The time delay is unknown, time varying, and can be decomposed into two parts: one fixed part which is unknown and is an integer multiple of the sampling time; the other part which is randomly varying but bounded by one sampling time. Static controller designs based on delay dependent stability conditions are presented. This system is then extended to a more general case when the randomly varying part of the time delay is not limited to one sampling time. The derivative of the time delay is not limited to be bounded. Hence, the contributions are as follow: (i) for a given controller, we can use these stability criteria to test stability of the resulted system; (ii) we can design a remote controller to stabilize an unstable system. Finally, simulation examples are presented to show the effectiveness of the proposed method and to demonstrate remote stabilization of open loop unstable systems.     

Teleoperation in the presence of varying time delays and sandwich linearity in actuators

In this paper, a novel control scheme is proposed to guarantee global asymptotic stability of bilateral teleoperation systems that are subjected to time-varying time delays in their communication channel and sandwich linearity in their actuators. This extends prior art concerning control of nonlinear bilateral teleoperation systems under time-varying time delays to the case where the local and the remote robots’ control signals pass through saturation or similar nonlinearities that belong to a class of systems we name sandwich linear systems. Our proposed controller is similar to the proportional plus damping (P+D) controller with the difference that it takes into account the actuator saturation at the outset of control design and alters the proportional term by passing it through a nonlinear function; thus, we call the proposed method as nonlinear proportional plus damping (nP+D). The asymptotic stability of the closed-loop system is established using a Lyapunov–Krasovskii functional under conditions on the controller parameters, the actuator saturation characteristics, and the maximum values of the time-varying time delays. To show the effectiveness of the proposed method, it is simulated on a variable-delay teleoperation system comprising a pair of planar 2-DOF robots subjected to actuator saturation. Furthermore, the controller is experimentally validated on a pair of 3-DOF PHANToM Premium 1.5A robots, which have limited actuation capacity, that form a teleoperation system with a varying-delay communication channel.     

不确定时滞关联大系统的全局稳定模糊容错控制

研究了一类带有时变时滞的不确定非线性关联大系统的自适应模糊容错控制问题.用有界的参考信号代换模糊逼近器输入中的未知时滞信号,使得控制器的设计与应用不再依赖于时滞假设条件,使得控制器的设计和控制方法的应用更为方便.容错反推控制技术和自适应技术相结合来处理代换误差和逼近误差.所提出的方案能有效补偿所有4种类型的执行器故障,同时还可保证闭环系统的全局稳定性.仿真结果进一步验证了本文方法的有效性.     

Control of bilateral teleoperators with time delays using only position measurements

A major drawback in the control of bilateral teleoperators is time delays. The nature of the communication channel that interconnects the local and the remote manipulators imposes these delays, which can be time‐varying. Several commercially available robots do not incorporate velocity sensors, and velocities are usually estimated using dirty derivatives. In this paper, we are interested in the control of bilateral teleoperators with variable time delays and without requiring velocity measurements. The proposal makes use of a second‐order dynamical controller that backpropagates damping to the local and the remote manipulators. If sufficient damping is injected in the controller and under the common passivity assumption of the human operator and the environment, it is proved that position errors and velocities are bounded. Invoking Barbalat's lemma, when the human and the environment do not inject forces in the system, it is shown that position error and velocities globally asymptotically converge to zero. A simulation comparison with other two control techniques shows the performance of the novel proposal. Experimental results evidence the robustness of the proposed scheme to interconnecting time delays.     

ADAPTIVE ROBUST CONTROL OF UNCERTAIN SYSTEMS WITH TIME‐VARYING STATE DELAY

In this paper, a new adaptive robust control scheme is developed for a class of uncertain dynamical systems with time‐varying state delay, unknown parameters and disturbances. By incorporating adaptive techniques into the robust control method, we propose a continuous adaptive robust controller which guarantees the uniform boundedness of the system and at the same time, the regulating error enters an arbitrarily designated zone in a finite time. The proposed controller is independent of the time‐delay, hence it is applicable to a class of dynamical systems with uncertain time delays. The paper includes simulation studies demonstrating the performance of the proposed control scheme.     

Neural-network predictive control for nonlinear dynamic systems with time-delay

A new recurrent neural-network predictive feedback control structure for a class of uncertain nonlinear dynamic time-delay systems in canonical form is developed and analyzed. The dynamic system has constant input and feedback time delays due to a communications channel. The proposed control structure consists of a linearized subsystem local to the controlled plant and a remote predictive controller located at the master command station. In the local linearized subsystem, a recurrent neural network with on-line weight tuning algorithm is employed to approximate the dynamics of the time-delay-free nonlinear plant. No linearity in the unknown parameters is required. No preliminary off-line weight learning is needed. The remote controller is a modified Smith predictor that provides prediction and maintains the desired tracking performance; an extra robustifying term is needed to guarantee stability. Rigorous stability proofs are given using Lyapunov analysis. The result is an adaptive neural net compensation scheme for unknown nonlinear systems with time delays. A simulation example is provided to demonstrate the effectiveness of the proposed control strategy.     

基于终端滑模机器人模糊自适应路径跟踪控制

对质心位置未知的移动机器人系统设计了基于快速终端滑模的模糊自适应路径跟踪控制方法。该方法采用模糊逻辑系统逼近控制器中的未知函数,基于李亚普诺夫稳定性分析方法对未知参数设计自适应律,并设计鲁棒控制器来补偿逼近误差。该方法不但可以保证闭环系统中的所有信号有界,而且可使跟踪误差在有限时间内收敛到原点的小邻域内。仿真结果验证了方法的有效性。     

一种自适应鲁棒控制方法及其闭环稳定性分析

研究了含有未建模动态的慢时变系统的自适应镇定问题.考虑的对象具有非最小相 位、含未建模动态和大范围时变参数等不良特性,且存在未知但有界外部扰动.这类对象很难 用时不变鲁棒控制器或传统自适应控制器进行镇定.利用l1优化设计结合参数估计的投影算 法,提出了一种自适应鲁棒控制策略.基于l1优化设计的连续性和投影算法的收敛性,证明了 这种控制策略能够持续适应慢时变对象并且保持闭环系统一致稳定性.鲁棒性分析表明这种 控制策略具有良好的鲁棒镇定性.     

Global output feedback regulation of nonlinear time delay systems subject to sensor measurement faults

This article studied the global output feedback regulation problem for a class of uncertain nonlinear time delay systems subject to unknown measurement faults on sensors. Different from the existing works, we consider the unknown time‐varying delays on the system states and relax their conservative condition on nonlinear functions. By introducing two novel time‐varying gains, a new global output feedback regulation algorithm is proposed, which ensures control parameters can be chosen flexibly. The proposed linear‐like controller is independent of the unknown time‐varying delays. Moreover, it has a simple structure, which is convenient for the implementation in practice. Based on the Lyapunov stability theory, it is strictly proved that all signals of the resulting closed‐loop system are globally bounded with the designed controller. Finally, a simulation example is presented to illustrate the effectiveness of the proposed output feedback regulation algorithm.     

Prediction‐based Adaptive Robust Tracking Control of an Uncertain First‐Order Time‐Delay System

This paper considers the tracking problem of a delayed uncertain first‐order system which is simultaneously subject to (possibly large) known input delay, unknown but bounded time‐varying disturbance, and unknown plant parameter. The proposed predictor adaptive robust controller (PARC) involves prediction‐based projection type adaptation laws with model compensation and prediction‐based continuous robust feedback such that the closed loop system has global exponential convergence with an ultimate bound proportional to delay, disturbance bound, and switching gain. Further, if there are only delay and parameter uncertainties after some finite time, then semi‐global asymptotic tracking is guaranteed. The proposed design is shown to have significant closed loop performance improvement over the baseline controller.     

Robust Adaptive Synchronization Control for a Class of Perturbed and Delayed Neural Networks

This paper is concerned with the asymptotic synchronization problem of a general neural network using the robust adaptive control technique. It is considered a class of modified Cohen–Grossberg neural networks which is supposed to undergo unknown perturbations caused by state-independent nonlinearities and bounded mixed time-varying delays on neuron amplification and activation functions. An adaptive compensation control strategy is proposed to ensure the elimination of the perturbed and delayed effects by means of adaptive estimations of unknown controller parameters. Through Lyapunov stability theory, it is shown that the proposed adaptive compensation controllers can guarantee the asymptotic synchronization of neural networks without knowing the knowledge of bounds of nonlinearities and delays. A numerical example is provided to illustrate the effectiveness of the developed techniques.     

Adaptive Robust Output Tracking Control of Uncertain Nonlinear Cascade Systems with Disturbance and Multiple Unknown Time‐Varying Delays

In this paper, an adaptive robust controller is designed for a class of uncertain nonlinear cascade systems with multiple time‐varying delays under external disturbance. It is assumed that multiple time‐varying delays are not exactly known and, therefore, the delayed terms must not appear in the adaptation and control laws. Accordingly, by using a Lyapunov‐Krasovskii function, delays are deleted from the adaptation and control laws. A controller based on an adaptive backstepping approach is designed to assure the global asymptotic tracking of the desired output and boundedness of the other states. The proposed controller is proved to be robust against unknown time‐varying delays and external disturbances applying to the system. Simulation results are provided to show the effectiveness of the proposed approach.     

Universal adaptive control of feedforward nonlinear systems with unknown input and state delays

This paper considers the problem of global asymptotic regulation via output feedback for a class of uncertain feedforward nonlinear systems with input and state delays, where the bounds of time delays are unknown. With the help of the high-gain scaling approach and the idea of universal adaptive control, we explicitly construct an adaptive output compensator with a novel positive dynamic gain which compensates simultaneously the unknown delays and the output growth rate with unknown constant. Based on such output compensator, we reduce the conservatism of the restrictive conditions imposed on nonlinearities to generalise the existing results. By the Lyapunov–Krasovskii theorem, a delay-independent controller design scheme is proposed to guarantee that all the closed-loop signals are globally bounded while rendering the states of original system and the estimate states to globally asymptotically converge to zero. Finally, two illustrative examples are given to show the usefulness of the proposed design method.     

Robust stability conditions for remote SISO DMC controller in networked control systems

A two level hierarchy is employed in the design of networked control systems (NCSs) with bounded random transmission delay. At the lower level a local controller is designed to stabilize the plant. At the higher level a remote controller with the dynamic matrix control (DMC) algorithm is implemented to regulate the desirable set-point for the local controller. The conventional DMC algorithm is not applicable due to the unknown transmission delay in NCSs. To meet the requirements of a networked environment, a new remote DMC controller is proposed in this study. Two methods, maximum delayed output feedback and multi-rate sampling, are used to cope with the delayed feedback sensory data. Under the assumption that the closed-loop local system is described by one FIR model of an FIR model family, the robust stability problem of the remote DMC controller is investigated. Applying Jury’s dominant coefficient lemma and some stability results of switching discrete-time systems with multiple delays; several stability criteria are obtained in the form of simple inequalities. Finally, some numerical simulations are given to demonstrate the theoretical results.     

State and Impedance Reflection Based Control Interface for Bilateral Telerobotic System with Asymmetric Delay

In this paper, we investigate state and imped-ance reflection based robust control strategy for bilateral shared telerobotic system under unsymmetrical time varying delay. Shared input for both master and slave robot is designed by combining delayed position and position-velocity signals with impedance reflection properties of the interaction between slave and environment and between human and master robot manipulator. Adaptive control algorithm is proposed to estimate the interaction properties between human and master manipulator and between slave and remote environment. Then, the delayed estimated interaction properties are reflected back to the master and slave robot manipulator to match with the estimated impedance properties of the interaction between human and remote environment. We combine robust term with adaptive control term to deal with the uncertainty associated with gravity loading vector, unmodeled dynamic and external disturbance. The stability conditions with time varying delays are derived by using Lyapunov-Krasovskii functional. Experimental results are given to demonstrate the validity of the proposed design for real-time applications.     

Adaptive robust control of uncertain systems with state and input delay

In this paper, adaptive robust control of uncertain systems with multiple time delays in states and input is considered. It is assumed that the parameter uncertainties are time varying norm-bounded whose bounds are unknown but their functional properties are known. To overcome the effect of input delay on the closed loop system stability, new Lyapunov Krasovskii functional will be introduced. It is shown that the proposed adaptive robust controller guarantees globally uniformly exponentially convergence of all system solutions to a ball with any certain convergence rate. Moreover, if there is no disturbance in the system, asymptotic stability of the closed loop system will be established. The proposed design condition is formulated in terms of linear matrix inequality (LMI) which can be easily solved by LMI Toolbox in Matlab. Finally, an illustrative example is included to show the effectiveness of results developed in this paper.     

Adaptive leader-follower formation control of mobile robots with unknown skidding and slipping effects

This paper investigates an adaptive leader-follower formation control problem of multiple mobile robots in the presence of unknown skidding and slipping. First, we employ the concept of virtual robots to achieve the desired formation and derive the kinematics of the virtual leader and follower robots considering skidding and slipping effects. Then, we design an adaptive formation controller based on a two-dimensional error surface where the adaptive technique is used for compensating the unknown skidding and slipping effects that influence the follower robots. From Lyapunov stability theorem, we show that all errors of the closed-loop system are uniformly ultimately bounded, and thus the desired formation is successfully achieved regardless of the presence of unknown skidding and slipping effects. Simulation results are provided to demonstrate the effectiveness of the proposed formation control scheme.     

Adaptive tracking for MIMO nonlinear systems with Unknown fast time‐varying parameters

In this paper, we consider a class of MIMO nonlinear systems with fast time‐varying parametric uncertainties. First, the tracking problem of general nonlinearly time‐varyingly parameterized systems is solved. Then, a Lyapunov‐based singularity free adaptive controller is proposed for the considered system. Specifically, an estimation approach with a proportional plus integral adaptation scheme is utilized to update the estimations of the unknown parameters under a mild assumption that the signs of the leading minors of the input gain matrix are known. The asymptotic stability is achieved with full state feedback. Furthermore, we design an output feedback controller by utilizing a standard high‐gain observer and achieve uniformly ultimately bounded convergence. Simulation examples illustrate the effectiveness of the proposed methods.