一类网络控制系统的稳定性分析

在基于多包传输的网络控制系统中,传感器采用时间驱动,控制器和执行器采用事件驱动.假定传输时延可以忽略,则整个网络控制系统可以描述为一个具有N个事件的异步动态系统.该文针对受控对象状态均可测,控制律采用输出反馈的情况,利用双线性矩阵不等式方法,讨论了网络控制系统指数稳定的问题,并给出了稳定性的充分条件,最后通过仿真实验加以验证.     

H∞ non-fragile observer-based dynamic event-triggered sliding mode control for nonlinear networked systems with sensor saturation and dead-zone input

This paper considers the problem of robust non-fragile observer-based dynamic event-triggered sliding mode control (SMC) for a class of discrete-time Lipschitz nonlinear networked control systems subject to sensor saturation and dead-zone input nonlinearity. First, an improved dynamic event-triggered scheme (DETS) in consideration of sensor saturation is proposed to reduce the number of data transmission. Next, a non-fragile observer is designed to estimate the system state, which facilitates the construction of the discrete sliding surface. By using a reformulated Lipschitz property, the error dynamics and sliding mode dynamics are modeled as a unified linear parameter varying (LPV) networked system with time-varying delays. Then, based on this model, sufficient conditions are established to guarantee the resulting closed-loop system to be asymptotically stable with a given disturbance attenuation level. Furthermore, an observer-based event-triggered SMC law is designed to drive the trajectories of the observer system onto a region near equilibrium point in a finite time in the presence of dead-zone input nonlinearity. Finally, two practical examples are employed to demonstrate the effectiveness of the proposed method.     

A new approach to event-triggered static output feedback control of networked control systems

This paper is concerned with the event-triggered static output feedback control of networked control systems. The event-triggered mechanism is represented by a time-delay model and some latest techniques are employed to deal with the induced time-delay. Furthermore, a novel strategy is developed to eliminate the coupling among control gain, input matrix and output matrix. With these techniques, a new sufficient condition for system stability is established in the framework of linear matrix inequalities. The effectiveness of the proposed method is shown by two numerical examples.     

H∞ observer-based event-triggered sliding mode control for a class of discrete-time nonlinear networked systems with quantizations

This paper investigates the problem of H_∞ observer-based event-triggered sliding mode control (SMC) for a class of uncertain discrete-time Lipschitz nonlinear networked systems with quantizations occurring in both input and output channels. The event-triggered strategy is used to save the limited network bandwidth. Then, based on the zero-order-hold (ZOH) measurement, a state observer is designed to reconstruct the system state, which facilitates the design of the discrete-time sliding surface. Considering the effects of quantizations, networked-induced constraints and event-triggered scheme, the nonlinear state error dynamics and sliding mode dynamics are converted into a unified linear parameter varying (LPV) time-delay system with the aid of a reformulated Lipschitz property. By using the Lyapunov-Krasovskii functional and free weighting matrix, a new sufficient condition is derived to guarantee the robust asymptotic stability of the resulting closed-loop system with prescribed H_∞ performance. And then the observer gain, event-triggering parameter and sliding mode parameter are co-designed. Furthermore, a novel SMC law is synthesized to force the trajectories of the observer system onto a pre-specified sliding mode region in a finite time. Finally, a single-link flexible joint robot example is utilized to demonstrate the effectiveness of the proposed method.     

Generalized predictor based active disturbance rejection control for non-minimum phase systems

In this paper, a generalized predictor based control scheme is proposed to improve system performance of set-point tracking and disturbance rejection for non-minimum phase (NMP) systems. By using a generalized predictor to estimate the system output without time delay, a model-based extended state observer (MESO) is designed to simultaneously estimate the system state and disturbance. Accordingly, an active disturbance rejection control design is developed which consists of a state feedback control and a feedforward control for the disturbance rejection. The MESO and feedback controllers are analytically derived by specifying the desired characteristic roots of MESO and closed-loop system poles, respectively. To improve the output tracking performance, a pre-filter is designed based on a desired closed-loop transfer function for the set-point tracking. A sufficient condition guaranteeing robust stability of the closed-loop system against time-varying uncertainties is established in terms of linear matrix inequalities (LMIs). Three illustrative examples from the literature are used to demonstrate the effectiveness and merit of the proposed control scheme.     

Robust output feedback distributed model predictive control of networked systems with communication delays in the presence of disturbance

In this work, an output feedback cooperative distributed model predictive control is developed for a class of networked systems composed of interacting subsystems interconnected through their states, in which it handles bounded disturbances and time varying communication delays. A distributed buffer based prediction strategy is used to compensate bounded delays and predict those states, which are coupled between subsystems that their actual values may not available due to delays. In the design of robust distributed model predictive control, distributed moving horizon estimation is employed so that convergence and boundedness of the estimation error are ensured. Furthermore, robust exponential stability of the closed loop system is established. The effectiveness of the proposed method is illustrated using two interconnected continuous stirred tank reactors.     

Adaptive sampling rate control for networked systems based on statistical characteristics of packet disordering

This paper investigates an adaptive sampling rate control scheme for networked control systems (NCSs) subject to packet disordering. The main objectives of the proposed scheme are (a) to avoid heavy packet disordering existing in communication networks and (b) to stabilize NCSs with packet disordering, transmission delay and packet loss. First, a novel sampling rate control algorithm based on statistical characteristics of disordering entropy is proposed; secondly, an augmented closed-loop NCS that consists of a plant, a sampler and a state-feedback controller is transformed into an uncertain and stochastic system, which facilitates the controller design. Then, a sufficient condition for stochastic stability in terms of Linear Matrix Inequalities (LMIs) is given. Moreover, an adaptive tracking controller is designed such that the sampling period tracks a desired sampling period, which represents a significant contribution. Finally, experimental results are given to illustrate the effectiveness and advantages of the proposed scheme.     

Analysis and Design of a Haptic Control System: Virtual Reality Approach

In this paper, the analysis and design of telerobotics based on the haptic virtual reality (VR) approach for simulating the clay cutting system is proposed. The main components of the approach include a user interface, networking, simulation, and a robot control scheme. The telerobotics for the clay cutting system and the environment is simulated by a haptic virtual system that enables operators to feel the actual force feedback from the virtual environment just as they would from the real environment. The haptic virtual system integrates the dynamics of the cutting tool and the virtual environment whereas the handle actuator consists of the dynamics of the handle and the operator on the physical side. The control scheme employs a dynamical controller which is designed considering both the force and position that the operator imposes on the handle and feedforward to the cutting tool, and the environmental force imposed on the cutting tool and the feedback to the handle. The stability robustness of the closed-loop system is analysed based on the Nyquist stability criterion. It is shown that the proposed control scheme guarantees global stability of the system, with the output of the cutting tool approaching that of the handle when the ratios of the position and the force are selected correctly. Experiments in the virtual environment on cutting a virtual clay system are used to validate the theoretical developments.     

Multi-loop control of UPS inverter with a plug-in odd-harmonic repetitive controller

This paper proposes an improved multi-loop control scheme for the single-phase uninterruptible power supply (UPS) inverter by using a plug-in odd-harmonic repetitive controller to regulate the output voltage. In the suggested control method, the output voltage and the filter capacitor current are used as the outer and inner loop feedback signals, respectively and the instantaneous value of the reference voltage feedforwarded to the output of the controller. Instead of conventional linear (proportional-integral/-resonant) and conventional repetitive controllers, a plug-in odd-harmonic repetitive controller is employed in the outer loop to regulate the output voltage, which occupies less memory space and offers faster tracking performance compared to the conventional one. Also, a simple proportional controller is used in the inner loop for active damping of possible resonances and improving the transient performance. The feedforward of the converter reference voltage enhances the robust performance of the system and simplifies the system modelling and the controller design. A step-by-step design procedure is presented for the proposed controller, which guarantees stability of the system under worst-case scenarios. Simulation and experimental results validate the excellent steady-state and transient performance of the proposed control scheme and provide the exact comparison of the proposed method with the conventional multi-loop control method.     

A new approach to design of a dynamic output feedback stabilizing control law for LTI systems

We present a new state-space approach to construct a dynamic output feedback controller which stabilizes a class of linear time invariant systems All the states of the given system are not measurable and only the output is used to design the stabilizing control law In the design scheme, however, we first assume that the given system can be stabilized by a feedback law composed of the output and its derivatives of a certain order Beginning with this assumption, we systematically construct a dynamic system which removes the need of the derivatives The mam advantage of the proposed controller is regarding the controller order, which may be smaller than that of conventional output feedback controller Using a simple numerical example, it is shown that the order of the proposed controller is indeed smaller than that of reduced-order observer based output feedback controller     

Adaptive output feedback tracking of nonlinear systems with uncertain nonsymmetric dead-zone input

In this paper, the problem of adaptive practical tracking is investigated by output feedback for a class of uncertain nonlinear systems subject to nonsymmetric dead-zone input nonlinearity with parameters of dead-zone being unknown. Instead of constructing the inverse of dead-zone nonlinearity, an adaptive robust control scheme is developed by designing an output compensator including two dynamic gains based respectively on identification and non-identification mechanism. With the aid of dynamic high-gain scaling approach and Backstepping method, stability analysis of the closed-loop system is proceeded using non-separation principle, which shows that the proposed controller guarantees that all closed-loop signal is bounded while the output of system tracks a broad class of bounded reference trajectories by arbitrarily small error prescribed previously. Finally, two examples are given to illustrate our controller effective.     

Learning from adaptive neural network output feedback control of a unicycle-type mobile robot

This paper studies learning from adaptive neural network (NN) output feedback control of nonholonomic unicycle-type mobile robots. The major difficulties are caused by the unknown robot system dynamics and the unmeasurable states. To overcome these difficulties, a new adaptive control scheme is proposed including designing a new adaptive NN output feedback controller and two high-gain observers. It is shown that the stability of the closed-loop robot system and the convergence of tracking errors are guaranteed. The unknown robot system dynamics can be approximated by radial basis function NNs. When repeating same or similar control tasks, the learned knowledge can be recalled and reused to achieve guaranteed stability and better control performance, thereby avoiding the tremendous repeated training process of NNs.     

Predictive cloud control for multiagent systems with stochastic event-triggered schedule

This paper addresses a predictive cloud control problem for a linear multiagent system with random network delays and noises. To reduce communication cost, a stochastic event-triggered schedule is introduced to decide whether current measurements need to be transmitted. An optimal state estimation algorithm is designed to compensate random network delays in the feedback channel. Subsequently, a predictive cloud control scheme is proposed for the multiagent system to achieve both stability and consensus. Simultaneously, random network delays in the forward channel is compensated actively. Sufficient and necessary conditions of stability and consensus for the closed-loop multiagent system are derived. Finally, a numerical example is provided to verify correctness and effectiveness of the proposed methods.     

An LMI-Based Fuzzy State Feedback Control with Multi-Objectives

This paper proposes a systematic design methodology for the Takagi-Sugeno (TS) model based fuzzy state feedback control system with multi-objectives. In this investigation, the objectives are set to be guaranteed stability and pre-specified transient performance, and this scheme is applied to a nonlinear magnetic bearing system. More significantly, in the proposed methodology, the control design problems that consider both stability and desired transient performance are reduced to the standard LMI problems. Therefore, solving these LMI constraints directly (not trial and error) lead to a fuzzy state-feedback controller such that the resulting fuzzy control system meets the above two objectives. Simulation and experimentation results show that the proposed LMI-based design methodology yields not only maximized stability boundary but also the desired transient responses.     

Intelligent dimensional error pre-compensation in CNC grinding using iterative learning approach

This paper focuses on the problem of intelligent compensation of nonlinearly appeared dimensional error in the computer numerical control grinding process. An intelligent error pre-compensation scheme is proposed with intelligent controller, automatic dimensional error measurement, and feedback information. Iterative learning approach is applied to infer the error compensation of the next workpiece machining by using the historical data. The simulation results and the real system input and output responses show that the proposed intelligent error compensation scheme and the iterative learning approach have significant superiority.     

Robust control of uncertain feedback linearizable systems based on adaptive disturbance estimation

In this paper, an adaptive disturbance estimation-based control of a class of uncertain feedback linearizable systems with the presence of, both, external perturbations as well as non-modeled dynamics is considered. The aim of the control design was to solve the tracking trajectory problem for a class of output-based linearizable uncertain systems. An adaptive scheme is proposed for developing a state estimator of the uncertain dynamics. The estimation of both, the states and the uncertain dynamics is attained despite the limited knowledge of the plant and the information contained in the output signal. The uncertain section in the linearized system was approximated by a class of time-dependent combination of the system states. The observer implemented a parametric identifier to obtain the time varying parameters associated to the estimation of the uncertain section. This method ensured the adequate estimation process of the uncertainties/perturbations, measured in terms of the mean square error. Simultaneously, an adaptive gain associated to the observer adjusts its trajectories to provide the ultimate boundedness of the estimation error. Once the states of the uncertain system are obtained, a feedback controller rejects actively the perturbations that affect the system by a compensation scheme. Two numerical examples were developed to show the observer-based control performance.     

Controller design approach based on linear programming

This study explains and demonstrates the design method for a control system with a load disturbance observer. Observer gains are determined by linear programming (LP) in terms of the Routh–Hurwitz stability criterion and the final-value theorem. In addition, the control model has a feedback structure, and feedback gains are determined to be the linear quadratic regulator. The simulation results confirmed that compared with the conventional method, the output estimated by our proposed method converges to a reference input faster when a load disturbance is added to a control system. In addition, we also confirmed the effectiveness of the proposed method by performing an experiment with a DC motor.     

Development and kinematic calibration for measurement structure of a micro parallel mechanism platform

This paper presents a micro-positioning platform based on a unique parallel mechanism developed by the authors. The platform has a meso-scale rectangular shape whose size is 20 × 23 mm. The stroke is 5 mm for both the x-and y-axes and 100 degrees for the α-axis. The platform is actuated by three sets of dual stage linear actuators: a linear motor for rough positioning and a piezo actuator for fine positioning. The developed micro-positioning platform has a measurement system that consists of three linear sensors. The position and orientation values of the movable platform can be measured directly and used in a feedback control system. Selecting 18 kinematic error parameters of a measurement system (feedback control system), a two-stage kinematic calibration method is proposed. Constant error parameters are found in the first stage and variable error parameters are found in the second stage of kinematic calibration. After kinematic calibration the position error is reduced to within 0.5 μm and error reduction rate is over 90%.     

Networked control of industrial automation systems—a new predictive method

A new method for real-time prediction of uncertain network transmission time delays and a method for closed-loop control of manufacturing and industrial plants through networks are introduced. The proposed delay prediction method is based on the multilayer perceptron neural model. In order to minimize the number of neurons in the first layer of the network and hence reducing the computational burden in a real-time implementation, a method for determination of the Markov order of the time delay sequence is presented. Using the predicted delay, and a zero-order hold equivalent discrete-time model of the plant, a time varying state feedback control algorithm with a real-time gain updating strategy is proposed. A sufficient condition for closed-loop stability is also derived using the switching theorem for linear systems. The proposed method is shown, through two industrial networked case studies, namely, a DC motor driving a transportation roller for paper sheets and a milling machine. Simulation studies depict the efficacy of the proposed method in controlling such challenging problems.     

Load speed regulation in compliant mechanical transmission systems using feedback and feedforward control actions

The problem of controlling the load speed of a mechanical transmission system consisting of a belt-pulley and gear-pair is considered. The system is modeled as two inertia (motor and load) connected by a compliant transmission. If the transmission is assumed to be rigid, then using either the motor or load speed feedback provides the same result. However, with transmission compliance, due to belts or long shafts, the stability characteristics and performance of the closed-loop system are quite different when either motor or load speed feedback is employed. We investigate motor and load speed feedback schemes by utilizing the singular perturbation method. We propose and discuss a control scheme that utilizes both motor and load speed feedback, and design an adaptive feedforward action to reject load torque disturbances. The control algorithms are implemented on an experimental platform that is typically used in roll-to-roll manufacturing and results are shown and discussed.