Adaptive Neural Synchronization Control for Bilateral Teleoperation Systems With Time Delay and Backlash-Like Hysteresis

Design of Synchronization Tracking Adaptive Control for Bilateral Teleoperation System with Time-Varying Delays

The performances of position synchronization and force interaction of the teleoperation system provide a safe and efficient way for operators to perform tasks in remote, hazardous environments. In practice, however, communication delays and dynamic uncertainties can impair the performance of position synchronization controls. Under the above factors, it is necessary to study and design appropriate bilateral control methods to achieve stable and effective position synchronization control. In this paper, a new adaptive control architecture based on velocity feedback filter and radial basis function neural network is proposed. In the proposed control scheme, only the position signal is transmitted during the communication process, and the speed feedback filter and compensation method are designed and adopted to avoid the use of acceleration signals. In addition, a new auxiliary variable with a tracking error integral term is used to reduce the steady-state error of position tracking under nonzero external environmental forces. Using the Lyapunov-Krasovskii method, the stability of closed-loop remote operating systems is demonstrated. In the simulation and experiment sections, the algorithm was verified separately and compared with other algorithms. The results of a master-slave robot system verify the tracking performance of our proposed control scheme.

Adaptive Neural Network Fixed-Time Control Design for Bilateral Teleoperation With Time Delay

In this article, subject to time-varying delay and uncertainties in dynamics, we propose a novel adaptive fixed-time control strategy for a class of nonlinear bilateral teleoperation systems. First, an adaptive control scheme is applied to estimate the upper bound of delay, which can resolve the predicament that delay has significant impacts on the stability of bilateral teleoperation systems. Then, radial basis function neural networks (RBFNNs) are utilized for estimating uncertainties in bilateral teleoperation systems, including dynamics, operator, and environmental models. Novel adaptation laws are introduced to address systems' uncertainties in the fixed-time convergence settings. Next, a novel adaptive fixed-time neural network control scheme is proposed. Based on the Lyapunov stability theory, the bilateral teleoperation systems are proved to be stable in fixed time. Finally, simulations and experiments are presented to verify the validity of the control algorithm.

A New Sampled-Data Output-Feedback Controller Design of Nonlinear Systems via Fuzzy Affine Models

This article focuses on the sampled-data output-feedback control problem for nonlinear systems represented by Takagi-Sugeno fuzzy affine models. An input delay approach is adopted to describe the sample-and-hold behavior of the measurement output. Via augmenting the system states with the control input, the resulting closed-loop system is converted into a singular system first. Based on the piecewise quadratic Lyapunov-Krasovskii functionals, some novel results on the sampled-data piecewise affine output-feedback controller design are attained by employing some convexification techniques. The simulation studies are presented to illustrate the effectiveness of the proposed scheme.

Novel Adaptive Finite-Time Control of Teleoperation System With Time-Varying Delays and Input Saturation

In this paper, two novel adaptive finite-time control schemes are proposed for position tracking of nonlinear teleoperation system, which dynamic uncertainties, actuator saturation, and time-varying communication delays are considered. First, a novel auxiliary variable is designed to provide more stable performance. The radial basis function (RBF) neural network is introduced to estimate dynamic uncertainties. Second, two adaptive finite-time control schemes are investigated. In control scheme I, the RBF neural network and the gain switching strategy are applied to compensate the actuator saturation. In control scheme II, an auxiliary compensation filter and the compensation adaptive update laws, which contain the finite-time structure, are developed for dealing with saturation. Third, the finite-time adaptive controller is designed in each of these two control schemes. Based on the multiple Lyapunov function method, the closed-loop teleoperation system with these two control methods is proved to be bounded and finite-time stability. Finally, the simulation experiments are performed and the comparisons with other control methods are shown. The effectiveness of the proposed control schemes is demonstrated.

Bilateral Teleoperation of Single-Master Multislave Systems With Semi-Markovian Jump Stochastic Interval Time-Varying Delayed Communication Channels

Communication time delays in a bilateral teleoperation system often carries a stochastic nature, particularly when we have multiple masters or slaves. In this paper, we tackle the problem for a single-master multislave (SMMS) teleoperation system by assuming an asymmetric and semi-Markovian jump protocol for communication of the slaves with the master under time-varying transition rates. A nonlinear robust controller is designed for the system that guarantees its global robust H_∞ stochastic stability in the sense of the Lyapunov theory. Employing the nonlinear feedback linearization technique, the dynamics of the closed-loop teleoperator is decoupled into two interconnected subsystems: 1) master-slave tracking dynamics (coordination) and 2) multislave synchronization dynamics. Employing an improved reciprocally convex combination technique, the stability analysis of the closed-loop teleoperator is conducted using the Lyapunov-Krasovskii methodology, and the stability conditions are expressed in the form of linear matrix inequalities that can be solved efficiently using numerical algorithms. Numerical studies and simulation results validate the effectiveness of the proposed controller design algorithm in both tracking and synchronization performance of the SMMS system, and robustly handling the stochastic and nondifferentiable nature of communication delays.

Robust Adaptive Control Scheme for Teleoperation Systems With Delay and Uncertainties

This paper proposes a robust adaptive algorithm that effectively copes with time-varying delay and uncertainties in Internet-based teleoperation systems. Time-delay induced by the communication network, as a major problem in teleoperation systems, along with uncertainties in modeling of robotic manipulators and remote environment warn the stability and performance of the system. A robust adaptive control algorithm is developed to deal with the system uncertainties and to provide a smooth estimation of delayed reference signals. The proposed control algorithm generates chattering-free torques which is one of the practical considerations for robotic applications. In addition, the achieved input-to-state stability gains do not necessarily require high gain control torques to retain the system's stability. Experimental simulation studies validate the effectiveness of the proposed control strategy on a teleoperation system consisting of a Phantom Omni Haptic device and SimMechanics model of the industrial manipulator UR10. The validation of the proposed control methodology was executed through a real-time Internet-based communication established over 4G mobile networks between Australia and Scotland.

Event-Triggered Neural Control of Nonlinear Systems With Rate-Dependent Hysteresis Input Based on a New Filter

In controlling nonlinear uncertain systems, compensating for rate-dependent hysteresis nonlinearity is an important, yet challenging problem in adaptive control. In fact, it can be illustrated through simulation examples that instability is observed when existing control methods in canceling hysteresis nonlinearities are applied to the networked control systems (NCSs). One control difficulty that obstructs these methods is the design conflict between the quantized networked control signal and the rate-dependent hysteresis characteristics. So far, there is still no solution to this problem. In this paper, we consider the event-triggered control for NCSs subject to actuator rate-dependent hysteresis and failures. A new second-order filter is proposed to overcome the design conflict and used for control design. With the incorporation of the filter, a novel adaptive control strategy is developed from a neural network technique and a modified backstepping recursive design. It is proved that all the control signals are semiglobally uniformly ultimately bounded and the tracking error will converge to a tunable residual around zero.

Synchronization control for bilateral teleoperation system with position error constraints: A fixed-time approach

In this work, a novel exponential-type Barrier Lyapunov Function (EBLF) is proposed to address the synchronization control issue for a class of bilateral teleoperation systems with system uncertainties, external disturbances, and constraint requirement. The most prominent feature of the EBLF is that it can be used in a unified scheme, which deals with full state constrained and output constrained problems. Moreover, a novel control strategy is incorporated with the EBLF to achieve fixed-time convergence into a small set while the synchronization position tracking errors are guaranteed to never exceed the predefined constraints through the "adding a power integrator" technique, and the estimated settling time is shown to be independent of initial values. Simulation results demonstrate the effectiveness of the proposed control scheme.

Adaptive neural network based position tracking control for Dual-master/Single-slave teleoperation system under communication constant time delays

The novel trajectory tracking control strategies for trilateral teleoperation systems with Dual-master/Single-slave robot manipulators under communication constant time delays are proposed in this article. By incorporating this design technique into the neural network (NN) based adaptive control framework, two controllers are designed for the trilateral teleoperation systems in free motion. First, with acceleration measurements, an adaptive controller under the synchronization variables containing the position and velocity error is constructed to guarantee the position and velocity tracking errors between the trilateral teleoperation systems asymptotically converge to zero. Second, without acceleration measurements, an adaptive controller under the new synchronization variables is presented such that the trilateral teleoperation systems can obtain the same trajectory tracking performance as the first controller. Third, in term of establishing suitable Lyapunov-Krasovskii functionals, the asymptotic tracking performances of the trilateral teleoperation systems can be derived independent of the communication constant time delays. Moreover, these two controllers are obtained without the knowledge of upper bounds of the NN approximation errors, respectively. Finally, simulation results are presented to demonstrate the validity of the proposed methods.

Adaptive Formation Control of Cooperative Teleoperators With Intermittent Communications

Most research so far in teleoperation control has assumed that all information is transmitted continuously. Unfortunately, the damaged and electromagnetic interfered line cause communication link failure. In addition, the unreliable link further leads to port data congestion. The data packet will be discarded when the buffer overflows. Consequently, it is unknown whether stability of the teleoperator could be guaranteed in the presence of intermittent communications. In order to overcome these drawbacks, in this paper, we provide a solution to the formation control problem of a single-master-multislave teleoperator in the situation where each robot is allowed to communicate with its neighbors only at some irregular discrete time instants. The relationship among control gains, topology, and maximum-allowable connected interval is presented. Simulations are performed to show the validity of our proposed approach.

Adaptive Critic Design for Pure-Feedback Discrete-Time MIMO Systems Preceded by Unknown Backlashlike Hysteresis

This paper concentrates on the adaptive critic design (ACD) issue for a class of uncertain multi-input multioutput (MIMO) nonlinear discrete-time systems preceded by unknown backlashlike hysteresis. The considered systems are in a block-triangular pure-feedback form, in which there exist nonaffine functions and couplings between states and inputs. This makes that the ACD-based optimal control becomes very difficult and complicated. To this end, the mean value theorem is employed to transform the original systems into input-output models. Based on the reinforcement learning algorithm, the optimal control strategy is established with an actor-critic structure. Not only the stability of the systems is ensured but also the performance index is minimized. In contrast to the previous results, the main contributions are: 1) it is the first time to build an ACD framework for such MIMO systems with unknown hysteresis and 2) an adaptive auxiliary signal is developed to compensate the influence of hysteresis. In the end, a numerical study is provided to demonstrate the effectiveness of the present method.

Robust Fuzzy Adaptive Tracking Control for Nonaffine Stochastic Nonlinear Switching Systems

This paper is concerned with the trajectory tracking control problem of a class of nonaffine stochastic nonlinear switched systems with the nonlower triangular form under arbitrary switching. Fuzzy systems are employed to tackle the problem from packaged unknown nonlinearities, and the backstepping and robust adaptive control techniques are applied to design the controller by adopting the structural characteristics of fuzzy systems and the common Lyapunov function approach. By using Lyapunov stability theory, the semiglobally uniformly ultimate boundness in the fourth-moment of all closed-loop signals is guaranteed, and the system output is ensured to converge to a small neighborhood of the given trajectory. The main advantages of this paper lie in the fact that both the completely nonaffine form and nonlower triangular structure are taken into account for the controlled systems, and the increasing property of whole state functions is removed by using the structural characteristics of fuzzy systems. The developed control method is verified through a numerical example.

Different-Level Simultaneous Minimization Scheme for Fault Tolerance of Redundant Manipulator Aided with Discrete-Time Recurrent Neural Network

By incorporating the physical constraints in joint space, a different-level simultaneous minimization scheme, which takes both the robot kinematics and robot dynamics into account, is presented and investigated for fault-tolerant motion planning of redundant manipulator in this paper. The scheme is reformulated as a quadratic program (QP) with equality and bound constraints, which is then solved by a discrete-time recurrent neural network. Simulative verifications based on a six-link planar redundant robot manipulator substantiate the efficacy and accuracy of the presented acceleration fault-tolerant scheme, the resultant QP and the corresponding discrete-time recurrent neural network.

Observer-Based Fuzzy Adaptive Output-Feedback Control of Stochastic Nonlinear Multiple Time-Delay Systems

This paper is concerned with the observer-based fuzzy output-feedback control for stochastic nonlinear multiple time-delay systems. On the basis of the consistent form of virtual input signals and increasing characteristics of the system upper bound functions, a variable splitting technique is employed to surmount the difficulty occurred in the nonlower-triangular form. In the controller design procedure, a state observer is first designed, and then an adaptive fuzzy output-feedback control method is presented by combining backstepping design together with fuzzy systems' universal approximation capability. The proposed adaptive controller guarantees the semi-global boundedness of closed-loop system trajectories in terms of fourth-moment. Two simulation examples are displayed to demonstrate the feasibility of the suggested controller.