Filtering for networked stochastic time-delay systems with sector nonlinearity

Copyright [2009] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This paper is concerned with the filtering problem for a class of discrete-time stochastic nonlinear networked control systems with network-induced incomplete measurements. The incomplete measurements include both the multiple random communication delays and random packet losses, which are modeled by a unified stochastic expression in terms of a set of indicator functions that is dependent on certain stochastic variable. The nonlinear functions are assumed to satisfy the sector nonlinearities. The purpose of the addressed filtering problem is to design a linear filter such that the filtering-error dynamics is exponentially mean-square stable. By using the linear-matrix-inequality (LMI) method and delay-dependent technique, sufficient conditions are derived which are dependent on the occurrence probability of both the random communication delays and missing measurement. The filter gain is then characterized by the solution to a set of LMIs. A simulation example is exploited to demonstrate the effectiveness of the proposed design procedures

H∞ controller design for networked predictive control systems based on the average dwell-time approach

This brief focuses on the problem of H∞ control for a class of networked control systems with time-varying delay in both forward and backward channels. Based on the average dwell-time method, a novel delay-compensation strategy is proposed by appropriately assigning the subsystem or designing the switching signals. Combined with this strategy, an improved predictive controller design approach in which the controller gain varies with the delay is presented to guarantee that the closed-loop system is exponentially stable with an H∞-norm bound for a class of switching signal in terms of nonlinear matrix inequalities. Furthermore, an iterative algorithm is presented to solve these nonlinear matrix inequalities to obtain a suboptimal minimum disturbance attenuation level. A numerical example illustrates the effectiveness of the proposed method

Stabilizing Gain Selection of Networked Variable Gain Controller to Maximize Robustness Using Particle Swarm Optimization

Networked Control Systems (NCSs) are often associated with problems like random data losses which might lead to system instability. This paper proposes a method based on the use of variable controller gains to achieve maximum parametric robustness of the plant controlled over a network. Stability using variable controller gains under data loss conditions is analyzed using a suitable Linear Matrix Inequality (LMI) formulation. Also, a Particle Swarm Optimization (PSO) based technique is used to maximize parametric robustness of the plant.Comment: 6 pages, 6 figure

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

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Scheduling policy based on model prediction error is presented to reduce energy consumption and network conflicts at the actuator node, where the characters of networked control systems are considered, such as limited network bandwidth, limited node energy, and high collision probability. The object model is introduced to predict the state of system at the sensor node. And scheduling threshold is set at the controller node. Control signal is transmitted only if the absolute value of prediction error is larger than the threshold value. Furthermore, the model of networked control systems under scheduling policy based on predicted error is established by taking uncertain parameters and long time delay into consideration. The design method of H∞ guaranteed cost controller is presented by using the theory of Lyapunov and linear matrix inequality (LMI). Finally, simulations are included to demonstrate the theoretical results

Data analytics for stochastic control and prognostics in cyber-physical systems

In this dissertation, several novel cyber fault diagnosis and prognosis and defense methodologies for cyber-physical systems have been proposed. First, a novel routing scheme for wireless mesh network is proposed. An effective capacity estimation for P2P and E2E path is designed to guarantee the vital transmission safety. This scheme can ensure a high quality of service (QoS) under imperfect network condition, even cyber attacks. Then, the imperfection, uncertainties, and dynamics in the cyberspace are considered both in system model and controller design. A PDF identifier is proposed to capture the time-varying delays and its distribution. With the modification of traditional stochastic optimal control using PDF of delays, the assumption of full knowledge of network imperfection in priori is relaxed. This proposed controller is considered a novel resilience control strategy for cyber fault diagnosis and prognosis. After that, we turn to the development of a general framework for cyber fault diagnosis and prognosis schemes for CPSs wherein the cyberspace performance affect the physical system and vice versa. A novel cyber fault diagnosis scheme is proposed. It is capable of detecting cyber fault by monitoring the probability of delays. Also, the isolation of cyber and physical system fault is achieved with cooperating with the traditional observer based physical system fault detection. Next, a novel cyber fault prognosis scheme, which can detect and estimate cyber fault and its negative effects on system performance ahead of time, is proposed. Moreover, soft and hard cyber faults are isolated depending on whether potential threats on system stability is predicted. Finally, one-class SVM is employed to classify healthy and erroneous delays. Then, another cyber fault prognosis based on OCSVM is proposed --Abstract, page iv

Design and implementation of predictive control for networked multi-process systems

This thesis is concerned with the design and application of the prediction method in the NMAS (networked multi-agent system) external consensus problem. The prediction method has been popular in networked single agent systems due to its capability of actively compensating for network-related constraints. This characteristic has motivated researchers to apply the prediction method to closed-loop multi-process controls over network systems. This thesis conducts an in-depth analysis of the suitability of the prediction method for the control of NMAS. In the external consensus problem, NMAS agents must achieve a common output (e.g. water level) that corresponds to the designed consensus protocol. The output is determined by the external reference input, which is provided to only one agent in the NMAS. This agreement is achieved through data exchanges between agents over network communications. In the presence of a network, the existence of network delay and data loss is inevitable. The main challenge in this thesis is thus to design an external consensus protocol with an efficient capability for network constraints compensation. The main contribution of this thesis is the enhancement of the prediction algorithm’s capability in NMAS applications. The external consensus protocol is presented for heterogeneous NMAS with four types of network constraints by utilising the developed prediction algorithm. The considered network constraints are constant network delay, asymmetric constant network delay, bounded random network delay, and large consecutive data losses. In the first case, this thesis presents the designed algorithm, which is able to compensate for uniform constant network delay in linear heterogeneous NMAS. The result is accompanied by stability criteria of the whole NMAS, an optimal coupling gains selection analysis, and empirical data from the experimental results. ‘Uniform network delay’ in this context refers to a situation in which the agent experiences a delay in accessing its own information, which is identical to the delay in data transfer from its neighbouring agent(s) in the network In the second case, this thesis presents an extension of the designed algorithm in the previous chapter, with the enhanced capability of compensating for asymmetric constant network delay in the NMAS. In contrast with the first case—which required the same prediction length as each neighbouring agent, subject to the same values of constant network delay—this case imposed varied constant network delays between agents, which required multi-prediction lengths for each agent. Thus, to simplify the computation, we selected a single prediction length for all agents and determined the possible maximum value of the constant network delay that existed in the NMAS. We tested the designed control algorithm on three heterogeneous pilotscale test rig setups. In the third case, we present a further enhancement of the designed control algorithm, which includes the capability of compensating for bounded random network delay in the NMAS. We achieve this by adding delay measurement signal generator within each agent control system. In this work, the network delay is considered to be half of the measured total delay in the network loop, which can be measured using a ramp signal. This method assumes that the duration for each agent to receive data from its neighbouring agent is equal to the time for the agent’s own transmitted data to be received by its neighbouring agent(s). In the final case, we propose a novel strategy for combining the predictive control with a new gain error ratio (GER) formula. This strategy is not only capable of compensating for a large number of consecutive data losses (CDLs) in the external consensus problem; it can also compensate for network constraints without affecting the consensus convergence time of the whole system. Thus, this strategy is not only able to solve the external consensus problem but is also robust to the number of CDL occurrences in NMAS. In each case, the designed control algorithm is compared with a Proportional-Integral (PI) controller. The evaluation of the NMAS output performance is conducted for each by simulations, analytical calculations, and practical experiments. In this thesis, the research work is accomplished through the integration of basic blocks and a bespoke Networked Control toolbox in MATLAB Simulink, together with NetController hardware

Networked Control System Design and Parameter Estimation

Networked control systems (NCSs) are a kind of distributed control systems in which the data between control components are exchanged via communication networks. Because of the attractive advantages of NCSs such as reduced system wiring, low weight, and ease of system diagnosis and maintenance, the research on NCSs has received much attention in recent years. The first part (Chapter 2 - Chapter 4) of the thesis is devoted to designing new controllers for NCSs by incorporating the network-induced delays. The thesis also conducts research on filtering of multirate systems and identification of Hammerstein systems in the second part (Chapter 5 - Chapter 6). Network-induced delays exist in both sensor-to-controller (S-C) and controller-to-actuator (C-A) links. A novel two-mode-dependent control scheme is proposed, in which the to-be-designed controller depends on both S-C and C-A delays. The resulting closed-loop system is a special jump linear system. Then, the conditions for stochastic stability are obtained in terms of a set of linear matrix inequalities (LMIs) with nonconvex constraints, which can be efficiently solved by a sequential LMI optimization algorithm. Further, the control synthesis problem for the NCSs is considered. The definitions of H₂ and H∞ norms for the special system are first proposed. Also, the plant uncertainties are considered in the design. Finally, the robust mixed H₂/H∞ control problem is solved under the framework of LMIs. To compensate for both S-C and C-A delays modeled by Markov chains, the generalized predictive control method is modified to choose certain predicted future control signal as the current control effort on the actuator node, whenever the control signal is delayed. Further, stability criteria in terms of LMIs are provided to check the system stability. The proposed method is also tested on an experimental hydraulic position control system. Multirate systems exist in many practical applications where different sampling rates co-exist in the same system. The l₂-l∞ filtering problem for multirate systems is considered in the thesis. By using the lifting technique, the system is first transformed to a linear time-invariant one, and then the filter design is formulated as an optimization problem which can be solved by using LMI techniques. Hammerstein model consists of a static nonlinear block followed in series by a linear dynamic system, which can find many applications in different areas. New switching sequences to handle the two-segment nonlinearities are proposed in this thesis. This leads to less parameters to be estimated and thus reduces the computational cost. Further, a stochastic gradient algorithm based on the idea of replacing the unmeasurable terms with their estimates is developed to identify the Hammerstein model with two-segment nonlinearities. Finally, several open problems are listed as the future research directions