Adaptive neural network control for flexible telerobotic systems with communication constraints

This paper investigates the synchronous control problem for a class of flexible telerobotic systems subject to system uncertainties and communication constraints. In view of the asymmetric time-varying communication delays, an adaptive time-delay estimator is designed to reduce the impacts of delays on the system. Moreover, by combining the neural networks and parameter adaptive method, the uncertainties of system dynamics are estimated and compensated. Based on these efforts, a new adaptive compensation control protocol is proposed. Additionally, input quantization in network control induced chattering phenomenon and unknown parameters is also dealt with by the adaptive compensation method. A useful characteristic of this paper is that the “complexity explosion” problem caused by the backstepping technique is circumvented effectively. Finally, sufficient conditions are derived for the synchronous control of the master-slave flexible telerobotic system under Lyapunov stability theory. A numerical example of flexible-joint robotic system is provided to illustrate the effectiveness of the proposed control schemes.     

Predictive motion control for autonomous capture of a tumbling target with a space manipulator

In this study, an autonomous capture framework was proposed for a tumbling target satellite with a space manipulator. First, the motion model of a tumbling target was constructed and the motion properties were analyzed. Subsequently, a predictive motion control method was introduced to compensate for the time delay owing to image processing and to predict the tumbling motion of the target. Stability analysis was conducted using a Lyapunov function. To address the problem of large position and attitude deviations of the tumbling target, different autonomous capture strategies, that is, non-fixed-point position-based capture and area-based capture, were proposed to capture different parts of the target, such as the docking ring and side surfaces, which are important for practical applications. The capture conditions were created and compared, where the non-fixed-point position capture strategy and area-based capture strategy had a larger pose tolerance than the traditional fixed-point position methods. Finally, the framework was tested using Adams/Simulink co-simulations. The results validated the autonomous capture process and proposed alternatives for practical applications.     

Switching fault-tolerant control of a moving vehicle-mounted flexible manipulator system with state constraints

In this brief, a switching fault-tolerant control (FTC) scheme is presented for a moving vehicle-mounted flexible manipulator subject to state constraints. The dynamic characteristics of the system are represented by coupled ordinary differential equations and partial differential equations (ODEs–PDEs). When actuators are healthy, vibration control and position regulations can be realized without violation of the given constraints based on a Barrier Lyapunov Function (BLF). Moreover, a switching strategy is introduced to prevent the transgression of constraints even under actuator failure by detecting actuator faults as-assisted by the proposed monitoring functions. The closed-loop states are kept within the given bounds under FTC laws. By extending LaSalle's Invariance Principle to an infinite dimension, the asymptotic stability of the fault-free closed-loop system is strictly verified. Simulation results demonstrate the effectiveness of the proposed approach.     

Cooperative output feedback control of a mobile dual flexible manipulator

This paper addresses the cooperative output feedback control of a mobile dual flexible manipulator, which is mounted at a moving platform to grasp and move a rigid object. We derive the distributed parameter model with geometric constraints for the dual flexible manipulator system by utilizing the Lagrange multiplier method and the Hamilton’s principle, which avoids the problem of control spillover. This paper considers a case where the states of system are difficult to measure directly and exploits the high gain observer theory to design the state observers for estimating the unavailable states. Then the cooperative output feedback control scheme is developed by the Lyapunov’s method, which enables the cooperative control of the flexible manipulator system. Furthermore, under the cooperative output feedback control scheme, we prove that the states of the system are uniformly bounded. Finally, the feasibility of the designed cooperative output feedback controllers is verified by numerical simulation.     

Hybrid position/force control of a flexible parallel manipulator

In this paper, simultaneous position/force control of a closed-chain planar manipulator with the last link flexible is studied when the manipulator is in contact with an environment. The proposed manipulator consists of a flexible link connected to three rigid linkages whichare optimized for kinematic and force manipulability in the region of interest. The flexible link is modeled as a series of rigid links connected by virtual torsion springs. A hybrid position/force control algorithm is developed and implemented on the manipulator. Experimental results are presented to verify the performance of the controller.     

Optimal control for linear systems with time delay in control input

This paper presents the optimal regulator for a linear system with time delay in control input and a quadratic cost function. The optimal regulator equations are obtained using the duality principle, which is applied to the optimal filter for linear systems with time delay in observations, and then proved using the maximum principle. Performance of the obtained optimal regulator is verified in the illustrative example against the best linear regulator available for linear systems without delays. Simulation graphs and comparison tables demonstrating better performance of the obtained optimal regulator are included.     

Prescribed performance adaptive control for an uncertain robotic manipulator with input compensation updating law

This paper investigates an adaptive prescribed performance control strategy with specific time planning for trajectory tracking of robotic manipulator subject to input constraint and external disturbances. By constructing an accumulated error vector embedded with a performance enhancement function and introducing an input auxiliary function, a specified-time control framework with built-in prescribed performance is further designed to ensure that the trajectory tracking performance. More particularly, the proposed control law is compatible with the control input saturation suppression algorithm, which is capable of improving the robustness of closed loop system. Under the framework of the proposed control strategy, it is proved by theory that all the signals in the closed-loop system are bounded, and moreover the tracking error can reach the exact convergence domain in a given time. At last, a numerical example is presented to indicate the feasibility and effectiveness of the proposed method.     

Intelligent control for robotic manipulator with adaptive learning rate and variable prescribed performance boundaries

The purpose of this study is to enhance the transient performance and mitigate the possible boundary-crossing issue during the design of a neural network-based intelligent prescribed performance control for robotic manipulators that suffer from input saturation. Initially, an auxiliary system is created utilizing the saturation signal, which is then used to modify the prescribed performance boundaries when saturation takes place. This ensures that the tracking errors adhere to the performance constraints even if the available control effort is limited. To further enhance the transient performance of the closed-loop system, a composite learning-based online identification scheme employing a Gaussian function to adaptively adjust the learning rate is utilized instead of a fixed-learning-rate weight updating law to train the neural network. This approach facilitates the reduction of the undesired weight oscillations at the beginning of the control process when the neural network is not sufficiently trained. Lastly, the stability of the closed-loop system is demonstrated by applying the Lyapunov approach, and simulation results support the effectiveness of the identification and control schemes proposed in this study.     

A control algorithm for systems with dead time

A least squares control algorithm for single-input single-output (SISO) systems is developed. The algorithm allows for a delay with large dead time and uses proportional- integral-derivative actions in their parallel form to achieve steady-state without error. Optimization of the controller parameters is achieved and the parameters of the controller are determined from the solution of a set of linear simultaneous equations. The control strategy is to optimize the controller parameters such that a desired well-behaved trajectory is obtained. The controller is shown to be robust and the algorithm is shown to function as well without or with large dead time, to have low sensitivity to changes in the dead time, and to allow an adaptive estimation of changing system parameters. The application of the developed algorithm to control the glucoregulatory system, based on a 4th-order digital model, is presented in two cases: free time delay and with large dead time.     

Data-driven iterative feedforward control with rational parametrization: Achieving optimality for varying tasks

In precision motion systems, well-designed feedforward control can effectively compensate for the reference-induced error. This paper aims to develop a novel data-driven iterative feedforward control approach for precision motion systems that execute varying reference tasks. The feedforward controller is parameterized with the rational basis functions, and the optimal parameters are sought to be solved through minimizing the tracking error. The key difficulty associated with the rational parametrization lies in the non-convexity of the parameter optimization problem. Hence, a new iterative parameter optimization algorithm is proposed such that the controller parameters can be optimally solved based on measured data only in each task irrespective of reference variations. Two simulation cases are presented to illustrate the enhanced performance of the proposed approach for varying tasks compared to pre-existing results.     

Affine formation control for multi-agent systems with prescribed convergence time

In this paper, the affine formation control problem for multi-agent systems with prescribed convergence time is investigated. Firstly, on the basis of a time-varying scaling function, a distributed continuous control algorithm is designed, under which a stationary affine formation of the nominal configuration is able to be achieved within a prescribed time. Secondly, to track a time-varying formation within the prescribed time, a distributed control protocol is proposed by employing a leader–follower control strategy. Furthermore, the boundary layer technique is adopted to avoid chattering effect. Finally, simulation examples are provided to demonstrated the effectiveness of the proposed design.     

Optimal control of stochastic functional neutral differential equations with time lag in control

In this work, we consider an optimal control problem of a class of stochastic differential equations driven by additive noise with aftereffect appearing in control. We develop a semigroup theory of the driving deterministic neutral system and identify explicitly the adjoint operator of the corresponding infinitesimal generator. We formulate the time delay equation under consideration into an infinite dimensional stochastic control system without time lag by means of the adjoint theory established. Consequently, we can deal with the associated optimal control problem through the study of a Hamilton–Jacob–Bellman (HJB) equation. Last, we present an example whose optimal control can be explicitly determined to illustrate our theory.     

Passivity-based control for uncertain stochastic jumping systems with mode-dependent round-trip time delays

This paper considers the passivity-based control problem for stochastic jumping systems with mode-dependent round-trip time-varying delays and norm-bounded parametric uncertainties. By utilizing a novel Markovian switching Lyapunov functional, a delay-dependent passivity condition is obtained. Then, based on the derived passivity condition, a desired Markovian switching dynamic output feedback controller is designed, which ensures the resulting closed-loop system is passive. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed results.     

Robust adaptive sliding mode control for uncertain discrete-time systems with time delay

This paper focuses on robust adaptive sliding mode control for discrete-time state-delay systems with mismatched uncertainties and external disturbances. The uncertainties and disturbances are assumed to be norm-bounded but the bound is not necessarily known. Sufficient conditions for the existence of linear sliding surfaces are derived within the linear matrix inequalities (LMIs) framework by employing the free weighting matrices proposed in He et al. (2008) [3], by which the corresponding adaptive controller is also designed to guarantee the state variables to converge into a residual set of the origin by estimating the unknown upper bound of the uncertainties and disturbances. Also, simulation results are presented to illustrate the effectiveness of the control strategy.     

Robust tuning of uncertainty and disturbance estimator-based control for stable processes with time delay

To expand the potential of uncertainty and disturbance estimator (UDE)-based control in practical application to most industrial stable processes, this paper proposes a convenient yet robust tuning rule according to the widely used first-order plus time delay (FOPTD) plant. The Smith predictor is first introduced to anticipate the delay-free output, which guarantees signal synchronizations in three control modules and enables remarkable restorations of nominal stability and performance. Then a second-order filter is employed in UDE to decouple the trade-off between disturbance rejection and noise attenuation. Based on this improvement and fixing both tracking speed and feedback gain to suggested patterns, the exhaustive evaluations for robustness against model distortion are executed through scanning the dimensionless filter bandwidth. The boundary demarcation triggered by the plunge of the continuous range of tolerable mismatched delays subsequently facilitates the formulation of an intuitive tuning rule with prescribed robustness. Its inherent model-based scaling property largely enables this rule to be implemented readily in industrial processes just like the proportional-integral-derivative (PID) controller. Several representative simulations are performed to demonstrate the merits of the proposed method over the related control strategies. And the promising prospect of the UDE-based control in the practical application is further illustrated by conducting a water level control experiment.     

Global neuro-adaptive control of nonstrict-feedback systems with unknown control directions and multiple time delays

Issues of control of nonstrict-feedback systems with unknown control directions and multiple time delays are investigated. The proposed design consists of three major parts, a nominal minimal-learning-parameter (MLP) based adaptive neural controller, a supervisory robust controller for pulling back the escaped transients, and the dynamic surface control (DSC) for solving the explosion of complexity and algebraic-loop problems simultaneously. Meanwhile, the Nussbaum gain function (NGF) and the Lyapunov–Krasovskii functional (LKF) are included for handling the unknown control directions and the time delays, respectively. In particular, global instead of the semi-global tracking stability is achieved. Simulation results are provided to show the effectiveness of the proposed approach.     

Modeling and stabilization of a wireless network control system with packet loss and time delay

The problem of modeling and stabilization of a wireless network control system (NCS) is considered in this paper, where packet loss and time delay exist simultaneously in the wireless network. A discrete-time switched system with time-varying delay model is first proposed to describe the system closed by a static state feedback controller. A sufficient criteria for the discrete-time switched system with time-varying delay to be stable is proposed, based on which, the corresponding state feedback controller is obtained by solving a set of linear matrix inequalities (LMIs). Numerical examples show the effectiveness of the proposed method.     

Non-fragile finite-time extended dissipative control for a class of uncertain discrete time switched linear systems

In this paper, the issues of finite-time extended dissipative analysis and non-fragile control are investigated for a class of uncertain discrete time switched linear systems. Based on average dwell-time approach, sufficient conditions for the finite-time boundedness and finite-time extended dissipative performance of the considered systems are proposed by solving some linear matrix inequalities, where using the concept of extended dissipative, we can solve the H_∞, $L_2?L_{\infty },$ Passivity and (Q, S, R)-dissipativity performance in a unified framework. Furthermore, two form of non-fragile state feedback controllers are designed to guarantee that the closed-loop systems satisfy the finite-time extended dissipative performance. Finally, simulation example is given to show the efficiency of the proposed methods.