Networked predictive control for linear systems with quantizers by an event-driven strategy

In this paper, networked predictive control is investigated for a networked control system with quantizers by an event-driven strategy. An event generator is designed according to a deviation of state estimation between the current time and last trigger time. A predictive strategy is proposed to compensate effect of network-induced delays and packet dropouts. The quantizers are used to deal with signals by converting real-valued signals into effective ones in both feedback and forward channels. Based on a “zoom” strategy, sufficient conditions are given to ensure stabilization of the networked control system by solving linear matrix inequalities. A simulation example is proposed to exhibit advantages and availability of the developed techniques.     

Dissipative filtering for singular Markov jump systems with generally uncertain transition rates via new integral inequality approach

This paper considers the problem of dissipative filtering problem for singular Markov jump systems with time-varying delay and generally uncertain transition rates. Firstly, by tuning the improved integral inequality and Wirtinger-based integral inequalities, a sufficient condition is derived to guarantee that the considered system is regular, impulse-free, stochastically stable with the dissipation performance. Then, based on the derived condition, and applying linear matrix inequalities (LMIs) techniques, the filter is synthesized. Finally, some numerical examples are given to illustrate the effectiveness of the obtained theoretic results.     

Observer-based decentralized control of large-scale stochastic high-order feedforward systems with multi time delays

This paper investigates the problem of observer-based decentralized control for a class of large-scale stochastic high-order feedforward systems with multi time delays. By using the homogeneous domination idea and constructing the implementable observer, the decentralized output-feedback controller design scheme is firstly proposed. Then, with the aid of stochastic time delay system stability theory, the globally asymptotically stable in probability of the closed-loop system is verified by selecting an appropriate Lyapunov–Krasoviskii functional. Finally, an example is provided to demonstrate the efficiency of the proposed design method.     

Model free adaptive iterative learning control for a class of nonlinear systems with randomly varying iteration lengths

This paper proposes a novel model free adaptive iterative learning control scheme for a class of unknown nonlinear systems with randomly varying iteration lengths. By applying the dynamic linearization technique along the iteration axis, such systems can be transformed into iteration-depended time varying linear systems. Then, an improved model free adaptive iterative learning control scheme can be constructed only using input and output data of the system. From the rigorous theoretical analysis, it is shown that the mathematical expectation of tracking errors converge to zero as iteration increases. This design does not require any dynamic information of the ILC systems and prior information of randomly varying iteration lengths. An illustrative example verifies the effectiveness of the proposed design.     

Asynchronous fault detection and robust control for switched systems with state reset strategy

This paper is concerned with the problem of simultaneous fault detection and control of switched systems under the asynchronous switching. A switching law and fault detection/control units called fault detector/controllers are designed to guarantee the fault sensitivity and robustness of the closed-loop systems. Different from the existing results, a state reset strategy is introduced in the process of fault detection/control, which reduces the conservatism caused by the jump of multiple Lyapunov functions at switching instants. Further, the proposed strategy is only dependent the state of fault detector/controllers, which is available when the system state is invalid. Finally, by using a performance gain transform technique, non-convex fault sensitivity conditions are converted into the convex error attenuation ones. This further improves the fault detection effect. A numerical example is given to demonstrate the effectiveness of the proposed results.     

Stabilization for multi-group coupled models with dispersal by feedback control based on discrete-time observations in diffusion part

In this paper, we study the almost surely stabilization for a class of unstable multi-group coupled models with dispersal (MGMs, in short) by feedback control design based on discrete-time observations in diffusion part. Specifically, we show that the corresponding stochastically controlled system is almost surely exponentially stable when the stochastic MGM is almost surely exponentially stable within a duration of time. Finally, an example is given to illustrate the obtained theoretical results.     

Finite-time control for periodic systems with Markov jump sensor nonlinearities and random input gains

Finite-time control for periodic systems with sensor nonlinearities and random input gains is addressed in this work. The variation of sensor nonlinearities is modeled by a Markov chain, and a stochastic variable is used to describe the influence of the actuator. A mode- and sensor nonlinearity-dependent non-fragile controller is designed to improve the performance and the non-fragility of the controller. The finite-time boundedness of the closed-loop system is ensured by a sufficient condition, the corresponding controller is then designed. Finally, the effectiveness of the developed results is illustrated by a numerical example.     

Nonlinear optimal control with disturbance rejection for asteroid landing

This paper presents an optimal solution to asteroid soft landing problem, on the base of $\theta ?D$ control technique and disturbance rejection mechanism. The control objective is to drive a probe to reach the surface of an asteroid with a desirable line-of-sight angle and zero velocity, eliminating the influence of external disturbance. Firstly, elementary $\theta ?D$ technique is applied in the absence of disturbance to tackle the nonlinear optimal control problem. Secondly, the disturbance is estimated in the fast-estimation framework with explicitly bounded estimation error. Afterwards, an integrated control protocol is presented in a feed-forward structure by the aid of an additional variable-structure term to ensure stability under time-varying disturbance. Simulation results of the proposed approach compared with the results of elementary $\theta ?D$ method and robust $\theta ?D$ method are presented at the end of this paper, demonstrating the effectiveness of the proposed control protocol.     

Security control for networked control systems with randomly occurring integrity check protection subject to randomly occurring zero-value attacks

This paper investigates the problem of secure control for networked control systems (NCSs) under randomly occurring zero-value attacks (ROZVAs). Specifically, ROZVAs only offset the true signal without injecting obfuscated information or noises, and possess the minimum energy of the added malicious information. To protect system stability against ROZVA, randomly occurring integrity check protection (ROICP) is introduced which prevents malicious data injection with less energy cost than persistently occurring protection. Besides the random phenomena of ROZVA and ROICP, which are characterized by two mutually independent random variables obeying the Bernoulli distribution, the randomly occurring time delays caused by ROICP are also considered in system modelling. According to the built stochastic linear system model, security analysis of the NCS with ROICP subject to ROZVA is carried out and sufficient condition for stochastic stability is derived via a linear matrix inequality (LMI) approach. Based on the proposed condition, a compensation feedback controller is designed to facilitate system stability. Finally, simulation results show the effectiveness of the proposed method.     

A new Lyapunov functional approach to sampled-data synchronization control for delayed neural networks

This paper discusses the problem of synchronization for delayed neural networks using sampled-data control. We introduce a new Lyapunov functional, called complete sampling-interval-dependent discontinuous Lyapunov functional, which can adequately capture sampling information on both intervals from $r(t?\overline{\tau })$ to $r(t_k?\overline{\tau })$ and from $r(t?\overline{\tau })$ to $r(t_{k+1}?\overline{\tau })$. Based on this Lyapunov functional and an improved integral inequality, less conservative conditions are derived to ensure the stability of the synchronization error system, leading to the fact that the drive neural network is synchronized with the response neural network. The desired sampled-data controller is designed in terms of solutions to linear matrix inequalities. A numerical example is provided to demonstrate that the proposed approaches are effective and superior to some existing ones in the literature.     

Consensus analysis for multi-agent systems via periodic event-triggered algorithms with quantized information

In this article, a novel distributed event-triggered control protocol for the consensus of second-order multi-agent systems with undirected topology is studied. Based on the proposed control protocol, the event-triggered condition is evaluated only at every sampling instant. The control input for each agent will be updated with local information if and only if its condition is violated. Both ideal and quantized relative state measurements are considered under this framework. Some sufficient conditions for achieving consensus are derived using spectral properties of edge Laplacian matrix and the discrete-time Lyapunov function method. Finally, numerical examples are given to demonstrate the effectiveness of our theoretical results.     

Intermittent control strategy for synchronization of fractional-order neural networks via piecewise Lyapunov function method

In this paper, the synchronization problem of fractional-order neural networks (FNNs) with chaotic dynamics is investigated via the intermittent control strategy. Two types of intermittent control methods, the aperiodic one and the periodic one, are applied to achieve the synchronization of the considered systems. Based on the dynamic characteristics of the intermittent control systems, the piecewise Lyapunov function method is employed to derive the synchronization criteria with less conservatism. The results under the aperiodically intermittent control show more generality than the ones via the periodically intermittent control. For each of the aperiodic and periodic cases, a simple controller design process is presented to show how to design the corresponding intermittent controller. Finally, two numerical examples are provided to demonstrate the effectiveness of the obtained theoretical results.     

Containment control of heterogeneous fractional-order multi-agent systems

Fractional-order calculus has been studied deeply because many networked systems can only be described with fractional-order dynamics in complex environments. When different agents of networked systems show diverse individual features, fractional-order dynamics with heterogeneous characters will be used to illustrate the multi-agent systems (MAS). Based on the distinguishing behaviors of agents, a compounded fractional-order multi-agent systems(FOMAS) is presented with diverse dynamical equations. Suppose multiple leader agents existing in FOMAS, containment consensus control of FOMAS with directed weighted topologies is studied. By applying frequency domain analysis theory of the fractional-order operator, an upper bound of delays is obtained to ensure containment controls of heterogenous FOMAS with communication delays. The consensus results of delayed fractional-order dynamics in this paper can be expanded to the integer-order models. Finally, the results are verified by simulation examples.     

H2/H∞ filtering for networked systems with data transmission time-varying delay,data packet dropout and sequence disorder

This paper investigates an H₂/H_∞ filter designing for networked systems perturbed by multiple noises. The measurement transmission from the sensor to the remote filter is completed via a communication network in simultaneously presenting of data transmission time-varying delays, data packet dropout and data sequence disorder. Since the filter will receive delayed and disordered information, a zero-order-hold (ZOH) or a logical-ZOH (LZOH) is firstly employed for resorting the chaos data sequence. Afterwards, a hybrid H₂/H_∞ filtering scheme is designed for accurately estimating the target output. By Itô formula and a novel free-weight method, the almost surely mean square exponentially stable (ASMSES) condition of the error system is conveniently obtained and the corresponding filter design method is finally presented. By the proposed method, not only the ASMSES with a pre-scheduled H₂/H_∞ performance can be achieved, but also the convergence rate of overall system is pre-regulable. In addition, it has been point out the dynamic filtering performance of LZOH scheme should be better than ZOH ones due to less time-varying delays are introduced and more latest measurement information are employed. Numerical examples are provided to demonstrate the effectiveness of the proposed methods.     

Non-fragile control of positive Markovian jump systems

This paper investigates the non-fragile control for positive Markovian jump systems both in continuous-time and discrete-time cases with actuator uncertainty. It is assumed that the coefficient matrices of the non-fragile controller is unknown and bounded. The state-feedback controller gain consists of nominal controller gain and gain perturbation. First, a set of state-feedback controllers for the considered system are designed by using a stochastic co-positive Lyapunov function integrated with linear programming approach. Under the designed controllers, the resulting closed-loop systems are positive and stochastically stable. Then, the proposed controller design approach is extended to discrete-time systems. Through comparisons, it is shown that existing results are special cases of the presented ones in the paper. Finally, two examples are given to illustrate the effectiveness of the proposed design.     

Data filtering based maximum likelihood extended gradient method for multivariable systems with autoregressive moving average noise

For multivariable systems with autoregressive moving average noises, we decompose the multivariable system into m subsystems (m denotes the number of outputs) and present a maximum likelihood generalized extended gradient algorithm and a data filtering based maximum likelihood extended gradient algorithm to estimate the parameter vectors of these subsystems. By combining the maximum likelihood principle and the data filtering technique, the proposed algorithms are effective and have computational advantages over existing estimation algorithms. Finally, a numerical simulation example is given to support the developed methods and to show their effectiveness.     

Guaranteed cost synchronization for second-order wireless sensor networks with given cost budgets

The current paper addresses leader-following guaranteed cost synchronization with the cost budget given previously for the second-order wireless sensor networks. The published researches on guaranteed cost synchronization design criteria usually are based on the linear matrix inequality (LMI) techniques and cannot take the cost budget given previously into consideration. Firstly, the current paper proposes a guaranteed cost synchronization protocol, which can realize the tradeoff design between the battery power consumption and the synchronization regulation performance. Secondly, for the case without the given cost budget, sufficient conditions for leader-following guaranteed cost synchronization are presented and an upper bound of the cost function is shown. Thirdly, for the case that the cost budget is given previously, the criterion for leader-following guaranteed cost synchronization is proposed. Especially, the value ranges of control gains in these criteria are determined, which means that the existence of control gains in synchronization criteria can be guaranteed, but the LMI techniques can only determine the gain matrix and cannot give the value ranges of control gains. Moreover, these criteria are only associated with the minimum nonzero eigenvalue and the maximum eigenvalue, which can ensure the scalability of the wireless sensor networks. Finally, numerical simulations are given to illustrate theoretical results.     

Viable immersion and invariance control for a class of nonlinear systems and its application to aero-engines

This paper presents a novel approach to stabilize a class of nonlinear systems with state constraints. The motivation behind this study is the need to develop a stabilizing state feedback controller that does not require the knowledge of Lyapunov function and can regulate the states to the equilibrium while meeting the constraints. By using an integration of two relatively new tools: immersion and invariance (I&I) theory and viability theory, a sufficient condition for stability and stabilizability of a general nonlinear affine system with state constraints is derived; Then, the related results are exploited to stabilize a class of nonlinear system in feedback form and with state constraints represented by inequalities and the viable I&I stabilizing state feedback controller is obtained constructively. Further, an application to a nonlinear aero-engine model with the temperature constraint is given to illustrate the applicability and the effectiveness of the proposed method. Finally, a comparative simulation is presented, highlighting the advantages of the viable I&I controller.     

Event-triggered decentralized output-feedback control for interconnected nonlinear systems with input quantization

In this paper, the event-triggered decentralized control problem for interconnected nonlinear systems with input quantization is investigated. A state observer is constructed to estimate the unmeasurable states, and the state-dependent interconnections are accommodated by presenting some smooth functions. Then by employing backstepping technique and neural networks (NNs) approximation capability, a novel decentralized output feedback control strategy and an event-triggered mechanism are designed simultaneously. It is proved through Lyapunov theory that the closed-loop system is stable and the tracking property of all subsystems is guaranteed. Finally, the effectiveness of the proposed scheme is illustrated by an example.