Model-based Networked Control Systems Research Articles

Mean Square Stability of Model Based Networked Control Systems with Multiplicative Noise

This paper presents a necessary and sufficient condition of mean square stability of a model based networked control systems (MB-NCS) with multiplicative noise, where a linear discrete time plant is controlled by using a model state feedback. The lifting approach is employed, and the original time varying system is recast as an equivalent time invariant system whose mean square stability is investigated. The necessary and sufficient condition derived here is given as a set of a linear matrix inequality and linear equations, which enjoys an advantage on numerical computation.

Adaptive model‐based event‐triggered control of networked control system with external disturbance

This study is concerned with adaptive model-based event-triggered control of an uncertain continuous system with external disturbance. The proposed framework incorporates two important control techniques for reducing communication burden and regulating the states of the system online in control network, that is, adaptive model-based networked control system and event-triggered control (ETC). An adaptive model of the plant is capable of generalising the zero-order hold implementation in traditional ETC schemes, while also providing stability thresholds that are robust to model uncertainties. In the adaptive model-based controller, the authors present an update law to estimate the parameters of the adaptive model at triggered instant. After revisiting the adaptive model property in the context of event-triggered communication, an event-triggered condition is proposed using the Lyapunov technique. The stability condition of the proposed approach does not need explicit knowledge of the plant parameters, but are given only in terms of the parameters of the adaptive model and some bounds in the model uncertainties. In addition, lower bound on transmission periods are provided. Meanwhile, L 2 stability with respect to external disturbance is examined. A real-time simulation example is presented to demonstrate the effectiveness of the theoretical results.

Model-Based Event-Triggered Predictive Control for Networked Systems with Data Dropout

This paper proposes a novel structure of networked control systems (NCSs) with communication logic, which incorporates model-based networked control systems (MB-NCSs), predictive control, and an event-triggered communication scheme into a unified framework to consider the bandwidth reduction of the network communications. Within this framework, first an event-triggered communication scheme at the sensor side is introduced to determine whether or not the sensor measurement should be transmitted to the controller through the imperfect forward paths. Second, at the controller, a model of the plant is used to predict future state behavior of the plant, and based on the predicted state between successful transmission instants, a novel predictive event-triggering scheme is proposed to compress the size of the packetized control signals transmitted from the controller side to the actuator side through feedback paths. Finally, a unified model of NCSs is established. Based on this model, a codesign condition of th...

The Butterfly-Shaped Feedback Loop in Networked Control Systems for the Unknown Delay Compensation

Available model-based networked control system (NCS) design approaches are basically formulated from the known system model and the information of the networked-induced delay time. Because the delay in remote control systems is significant and time-varied as commercial networks involved, available model- based NCS design approaches may thus become impractical. The increase of time delay due to the limited communication bandwidth usually induces data dropout to make NCS design even more difficult.Inthispaper,theschemeoftheperfectdelaycompensation (PDC) is proposed by introducing the butterfly-shaped inner feed- back loop in NCS to deal with the unknown network-induced time delay. Furthermore, analytical results indicate that the controller obtained in general control systems can be directly implemented on NCS with the proposed PDC scheme. Both simulation and experi- mental results were successfully carried out on an ac servo motor at a distance of 15 km through Ethernet to maintain a stable remote control system.

Packet‐Loss‐Dependent Stabilization of Model‐Based Networked Control Systems

AbstractIn this paper, the stabilization problem and controller design of model‐based networked control systems (MB‐NCSs) with both arbitrary and Markovian packet dropouts are discussed via the switched system approach. Different from the common way of using the last successfully transmitted information, the approximate state produced by the explicit plant model is applied to deal with the packet loss problem in our method. Based on the Lyapunov functional methodology and inequality techniques, some sufficient stabilization conditions are derived and stabilizing state feedback controllers are constructed. Moreover, by using the cone complementary linearation (CCL) method, a non‐linear minimization problem subject to some linear matrix inequalities (LMIs) is provided here to help find a sub‐optimal solution. Numerical examples and accompanying simulations illustrate the effectiveness and validity of our techniques, and also evidence of improvements over the existing literature.

Stability analysis of model-based networked distributed control systems

Networked distributed control systems (NDCSs) face serious challenges such as delays and packet dropouts induced by the communication network employed to connect local controllers of interacting subsystems. These two network-induced shortcomings may degrade the performance or even destabilize NDCSs. This paper is concerned with the problem of stability analysis and stabilization of the NDCSs, featuring both random delay and random packet loss in their communication networks. A model-based networked distributed control framework is proposed to stabilize the NDCS consisting of discrete-time subsystems interconnected through their states. In this control framework, to compensate for the adverse effects of these two network-induced shortcomings, an interaction estimator is provided in each local controller; in addition to a main control unit. This estimator uses the explicit model of the subsystems to estimate the evolution of the states of interacting subsystems, when information about their actual values is not available. A model for the NDCS subject to both random packet loss and random delay is developed. By providing a 3-step interaction estimating algorithm, the closed-loop model-based networked distributed control system (MB-NDCS) is formulated as a time-dependent impulsive system. Then, a quadratic Lyapunov function is constructed to derive a linear matrix inequality (LMI) based sufficient condition for stability analysis of the overall impulsive system. Finally, an illustrative example of a network of interconnected chemical reactors with recycle is presented to show the effectiveness of the proposed approach.

Model-Based Event-Triggered Control for Systems With Quantization and Time-Varying Network Delays

This paper combines two important control techniques for reducing communication traffic in control networks, namely, model-based networked control systems (MB-NCS) and event-triggered control. The resulting framework is used for stabilization of uncertain dynamical systems and is extended to systems subject to quantization and time-varying network delays. The use of a model of the plant in the controller node not only generalizes the zero-order-hold (ZOH) implementation in traditional event-triggered control schemes but it also provides stability thresholds that are robust to model uncertainties. The effects of quantized measurements are especially important in the selection of stabilizing thresholds. We are able to design error events based on the quantized variables that yield asymptotic stability compared to similar results in event-triggered control that consider nonquantized measurements which, in general, are not possible to use in digital computations. With respect to MB-NCS, the stability conditions presented here do not need explicit knowledge of the plant parameters as in previous work but are given only in terms of the parameters of the nominal model and some bounds in the model uncertainties. We consider the joint adverse effects of quantization and time delays and emphasize the expected tradeoff between the selection of quantization parameters and the admissible network induced delays.

Parameter estimation in time-triggered and event-triggered model-based control of uncertain systems

In this article on-line parameter estimation of dynamical systems is addressed in the context of model-based networked control systems (MB-NCSs). Stability conditions that are robust to parameter uncertainties and lack of feedback for extended intervals of time are presented. The updated model is used to control the real system the next time feedback information is unavailable. Additionally, new estimation models are proposed that offer better convergence properties than typical state-space parameter estimation methods; common assumptions such as availability of persistently exciting inputs and estimation of only a canonical form of the system are relaxed. The implementation of upgraded models in MB-NCSs results in better usage of the network by allowing longer intervals without the need for measurement updates.

Robust stability of nonlinear model-based networked control systems with time-varying transmission times

Robust stability of a class of model-based networked control systems (MB-NCSs, for short) with nonlinear perturbation is analyzed. Based on the model-based networked control algorithm, a linear model of the plant is used to estimate the plant state behavior between transmission times. The case that the nonlinear plant and the linear plant model are connected via a network channel with transmission times that are varying within a time interval is of particular interest. Sufficient condition on stability of MB-NCSs with nonlinear perturbation is given. One advantage of the proposed method is that the maximum transmission interval and the robustness bound on nonlinear perturbation can be computed. Finally, numerical simulation is worked out to show our main result.

On the model-based networked control for singularly perturbed systems with nonlinear uncertainties

In this paper, the model-based networked control is addressed for a class of singularly perturbed control systems with nonlinear uncertainties. An approximate linear slow and fast control system of the plant, which can be obtained by omitting the nonlinear uncertainties, are used as a model to estimate the state behavior of the plant between transmission times. The stability of model-based networked control systems is investigated under the assumption that the controller/actuator is updated with the sensor information at constant time intervals. It is shown that there exists the allowable upper bound of the singular perturbation parameter such that the model-based networked control system is globally exponentially stable.

Model-based control with intermittent feedback: bridging the gap between continuous and instantaneous feedback

The aim of this article is to provide a set of results in stability of model-based networked control systems (MB-NCS) with intermittent feedback, which we intend will serve as a nexus between the study of systems with instantaneous feedback and with continuous feedback. We apply the concept of intermittent feedback to a class of networked control systems known as MB-NCS. MB-NCS use an explicit model of the plant in order to reduce the network traffic while attempting to prevent excessive performance degradation, while intermittent feedback consists of the loop remaining closed for some fixed interval, then open for another interval. We begin by introducing the basic architecture for model-based control, then discuss the concept of intermittent feedback, its applications in various fields and its role as a link between instantaneous and continuous feedback. We then provide our results for the model-based architecture with intermittent feedback. We also address the case with output feedback (through the use of a state observer), providing a full description of the state response of the system, as well as a necessary and sufficient condition for stability in each case. Extensions of our results to cases with nonlinear plants are also presented. Next, we investigate the situation where the update times τ and h are time-varying, first addressing the case where they have upper and lower bounds, then moving on to the case where their distributions are independent identically distributed or driven by a Markov chain. Finally, we study the case of model-based control with intermittent feedback for discrete-time plants, again providing stability conditions for the basic architecture, the state observer case and the case with time-varying parameters.

On model-based networked control system with multi-rate input sampling

To study networked control system with multi-rate input sampling, transforms the system into a single-rate sampling system by introducing lifting technique. Adopting model-based control scheme, state feedback control and stability of the system under different transmission policies are studied. Necessary and sufficient conditions for system to be globally exponentially stable are derived. Then, the model errors influence on the maximum communication interval is investigated. Simulation examples are used throughout to illustrate the main results. It is to be noted that the smaller the model error, the larger the maximum transmitting time.

Feedback scheduling of model-based networked control systems with flexible workload

In this paper, a novel control structure called feedback scheduling of model-based networked control systems is proposed to cope with a flexible network load and resource constraints. The state update time is adjusted according to the real-time network congestion situation. State observer is used under the situation where the state of the controlled plant could not be acquired. The stability criterion of the proposed structure is proved with time-varying state update time. On the basis of the stability of the novel system structure, the compromise between the control performance and the network utilization is realized by using feedback scheduler. Examples are provided to show the advantage of the proposed control structure.

On the model-based approach to nonlinear networked control systems

The problem of model-based stabilization of a nonlinear system based on its approximate discrete-time model is addressed, under the assumption that both the feedforward and the feedback paths are subject to network-induced constraints. These constraints include irregularity of the transfer intervals, time-varying communication delays, and the possibility of packet losses. A communication protocol that copes with these constraints is proposed. A “Stability+performance recovery” result for the nonlinear model-based networked control system (NCS) is formulated and proven.Simulation results presented confirm that the proposed method improves the maximum allowable transfer interval.

Stability of Model-Based Networked Control Systems with Intermittent Feedback

In this paper, we apply the concept of Intermittent Feedback to a class of networked control systems known as Model-Based Networked Control Systems (MB-NCS). We begin by introducing the basic architecture for model-based control with intermittent feedback, then address the case with output feedback (through the use of a state observer), providing a full description of the state response of the system, as well as a necessary and sufficient condition for stability in each case. Examples are provided to complement the theoretical results. Extensions of our results to cases with nonlinear plants are also presented. Finally, we investigate the situation where the update times τ and h are time-varying, first addressing the case where they have upper and lower bounds, then moving on to the case where their distributions are i.i.d or driven by a Markov chain.

Static and dynamic quantization in model-based networked control systems

In this paper the effects of quantization in an important class of networked control systems called model-based networked control systems (MB-NCS) are considered. The MB-NCS architecture uses an explicit model of the plant in the controller in order to reduce the network traffic, while attempting to prevent excessive performance degradation. Sufficient stability conditions for two types of static and a dynamic quantization schemes for MB-NCS are derived. An important feature is that the stability conditions are explicitly expressed in terms of the plant and controller dynamics, the error between the model and the plant parameters, the transmission or update times, the quantization parameters, and a robustness measure of the system to parameter uncertainty. This is important because it allows the design of the controller and network parameters to achieve the desired goals. Examples are used throughout to illustrate the main results.

Stability of Model-Based Networked Control Systems With Time-Varying Transmission Times

In model-based networked control systems (MB-NCSs), an explicit model of the plant is used to produce an estimate of the plant state behavior between transmission times. In this paper, the stability of MB-NCSs is studied when the controller/actuator is updated with the sensor information at nonconstant time intervals. Networked control systems with transmission times that are varying either within a time interval or are driven by a stochastic process with identically independently distributed and Markov-chain driven transmission times are studied. Sufficient conditions for Lyapunov stability are derived. For stochastically modeled transmission times almost sure stability and mean-square sufficient conditions for stability are introduced.