Effect of stepped‐impedance resonators on rectangular metamaterial unit cells

In this article, effect of the stepped‐impedance resonator (SIR) miniaturization technique on rectangular metamaterial unit cells is investigated and the influence of the method's structural parameters on the characteristic impedance and the distance between higher resonance modes is discussed. According to the results, the ideal unit cell for this method should be thick enough and have an impedance ratio greater than one. Furthermore, the SIR technique is applied on a conventional two‐turn spiral metamaterial unit cell and a new compact spiral unit cell is introduced. The effect of this method on the unit cell parameters is investigated and the new unit cell is compared to the conventional spiral. According to the results, a miniaturization factor of 0.75 can be achieved with the new unit cell. To validate the results, a two dimensional array of the unit cell is fabricated and its S‐parameters are measured using the free space method. © 2015 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:582–590, 2015.     

Stabilization of Fractional‐Order Linear Systems with State and Input Delay

This paper describes a variable structure control for fractional‐order systems with delay in both the input and state variables. The proposed method includes a fractional‐order state predictor to eliminate the input delay. The resulting state‐delay system is controlled through a sliding mode approach where the controller uses a sliding surface defined by fractional order integral. Then, the proposed control law ensures that the state trajectories reach the sliding surface in finite time. Based on recent results of Lyapunov stability theory for fractional‐order systems, the stability of the closed loop is studied. Finally, an illustrative example is given to show the interest of the proposed approach.     

A Maximum Principle via Malliavin calculus for combined stochastic control and impulse control of forward‐backward systems

We consider a combined stochastic control and impulse control problem of forward‐backward systems driven by Lévy processes, where both the system coefficients and the objective performance functional are allowed to be random, non‐Markovian; the information available to the controller is partial information. Applying a Malliavin calculus approach, we derive a maximum principle for this control problem, where the adjoint processes are explicitly represented by the parameters and the states of the system. Finally, we give two examples of applications. © 2015 Chinese Automatic Control Society and Wiley Publishing Asia Pty Ltd     

Networked Estimation of Multi‐Agent Systems Subject to Faults and Unreliable Information

In this paper, a novel framework for networked estimation of multi‐agent systems subject to presence of actuator faults is proposed. This framework is developed based on the notion of sub‐observers where within a group of sub‐observers each sub‐observer estimates certain states that are conditioned on a given input, output, and other state information. We model the overall estimation process by a weighted estimation (WE) digraph. By selecting an appropriate path in the WE digraph, an assigned supervisor can select and configure a set of sub‐observers to successfully estimate all the system states. In the presence of large intermittent disturbances, noise, and faults certain sub‐observers may become invalid, and consequently the supervisor reconfigures the set of sub‐observers by selecting a new path in the estimation digraph such that the impacts of these uncertainties are confined to only the local estimators. This will prevent the propagation of uncertainties on the estimation performance of the entire multi‐agent system. Simulation results provided for a five satellite formation flight system in deep space confirm the validity and applicability of our proposed analytical work.     

Robust H∞ Output Tracking Control with Partly Quantized Information

This paper is concerned with the stability and output tracking problems of networked control systems (NCSs) with partly quantized information. Both the remote and local systems are considered. The state variables transported from the remote system experiences time delays and quantization errors, while the local state variables do not. The purpose is to design a state feedback controller which guarantees that the output of the local system tracks the output of the remote system in the H∞ sense. This consideration is widely appeared in remote assistant systems. Based on the Lyapunov–Krasovskii (L‐K) functional approach, sufficient conditions on the existence of a quantized robust H∞ output tracking controller for NCSs are presented in terms of bilinear matrix inequalities (BMIs). Furthermore, a cone complementarity algorithm is used to convert these BMIs into a convex optimization problem. Finally, a simulation example is given to demonstrate the efficiency of the proposed method.     

Short‐Time Linear Quadratic Form Technique for Estimating Fast‐Varying Parameters in Feedback Loops

The precision of a closed‐loop controller system designed for an uncertain plant depends strongly upon the maximum extent to which it is possible to track the trend of time‐varying parameters of the plant. The aim of this study is to describe a new parameter estimation algorithm that is able to follow fast‐varying parameters in closed‐loop systems. The short‐time linear quadratic form (STLQF) estimation algorithm introduced in this paper is a technique for tracking time‐varying parameters based on short‐time analysis of the regressing variables in order to minimize locally a linear quadratic form cost function. The established cost function produces a linear combination of errors with several delays. To meet this objective, mathematical development of the STLQF estimation algorithm is described. To implement the STLQF algorithm, the algorithm is applied to a planar mobile robot with fast‐varying parameters of inertia and viscous and coulomb frictions. Next, performance of the proposed algorithm is assessed against noise effects and variation in the type of parameters.     

Global Robust Stabilization via Sampled‐Data Output Feedback for Nonlinear Systems with Uncertain Measurement and Control Gains

This paper studies the problem of using a sampled‐data output feedback controller to globally stabilize a class of nonlinear systems with uncertain measurement and control gains. A reduced‐order observer and a linear output control law, both in the sampled‐data form, are designed without the precise knowledge of the measurement and control gains except for their bounds. The observer gains are chosen recursively in a delicate manner by utilizing the output feedback domination approach. The allowable sampling period is determined by estimating and restraining the growth of the system states under a zero‐order‐hold input with the help of the Gronwall–Bellman Inequality. A DC–DC buck power converter as a real‐life example will be shown by numerical simulations to demonstrate the effectiveness of the proposed control method.     

Solving the Problem of Forbidden States in Discrete Event Systems: A Novel Systematic Method for Reducing the Number of Control Places

This paper deals with the problem of forbidden states in Discrete Event Systems modelled by non‐safe Petri Nets. To avoid these states, some Generalized Mutual Exclusion Constraints can be assigned to them. These constraints limit the weight sum of tokens in some places and can be enforced on the system using control places. When the number of these constraints is large, a large number of control places should be added to the system. In this paper, a method is presented to assign the small number of constraints to forbidden states using some states which cover the forbidden states. So, a small number of control places are added to the system leading to obtaining a maximally permissive controller.     

Game‐Based Valley‐Fill Charging Coordination for Large‐Population Plug‐in Electric Vehicles

Charging coordination of large‐population autonomous plug‐in electric vehicles (PEVs) in the power grid can be formulated as a class of constrained optimization problems. To overcome the computational complexity, a game‐based method is proposed for the charging problems of the PEV population, which is composed of homogeneous subpopulations, such that individuals update their best charging strategies simultaneously with respect to a common electricity price determined by the total demand. To mitigate the oscillation behavior caused by the greedy behavior for the cheap electricity by individuals, a deviation cost is introduced to penalize against the deviation of the individual strategy from the average value of the homogeneous subpopulation. By adopting a proper deviation cost and following a best strategy update mechanism, the game systems may converge to the socially optimal valley‐fill Nash equilibrium. Simulation examples are studied to illustrate the results.     

Sampled‐Data Approach to State Estimation Performance for Heterogeneous Distributed System with Fault

This paper considers the state estimation performance for heterogeneous distributed system with fault based on sampled‐data measurement. Firstly, a performance index for distributed state estimation is proposed. Based on the sampled‐data measurement of the heterogeneous distributed system, distributed state estimators are constructed. It is proved that by analyzing the distributed state estimation error systems, the required performance index for overall state estimation can be obtained using linear matrix inequality method. Finally, a simulation example is used to illustrate the effectiveness of the proposed theoretical scheme.