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This paper investigates the distributed optimization problem of networked Euler–Lagrange (EL) systems with actuator faults and unknown model parameters. In order to solve this issue, some event‐triggered fault‐tolerant (ETFT) optimal coordination algorithms with the mechanism of intermittent communication and intermittent controller update are designed for the first time to compensate for the impact of physical faults on the system, and reduce communication costs and control resource consumption. For practical applications, it is difficult for the system to have a secure environment if there exist malicious attacks blocking network channels among agents. Therefore, the resilient ETFT optimal coordination with intermittent communication subject to asynchronous denial‐of‐service (DoS) attacks is considered. Especially, auxiliary systems with adaptive gains are introduced in the algorithms due to the unavailability of the EL model parameters, which also enable virtual data exchange between agents and protect actual state information. Note that the algorithms are fully distributed as the global topology information is not required. Finally, the feasibility of the proposed algorithms is verified through numerical simulation of examples. [ABSTRACT FROM AUTHOR]

Existing methods for nonlinear robust control often use scenario‐based approaches to formulate the control problem as large nonlinear optimization problems. The optimization problems are challenging to solve due to their size, especially if the control problems include time‐varying uncertainty. This paper draws from local reduction methods used in semi‐infinite optimization to solve robust optimal control problems with parametric and time‐varying uncertainty. By iteratively adding interim worst‐case scenarios to the problem, methods based on local reduction provide a way to manage the total number of scenarios. We show that the local reduction method for optimal control problems consists of solving a series of simplified optimal control problems to find worst‐case constraint violations. In particular, we present examples where local reduction methods find worst‐case scenarios that are not on the boundary of the uncertainty set. We also provide bounds on the error if local solvers are used. The proposed approach is illustrated with two case studies with parametric and additive time‐varying uncertainty. In the first case study, the number of scenarios obtained from local reduction is 101, smaller than in the case when all 214+3×192$$ {2}^{14+3\times 192} $$ extreme scenarios are considered. In the second case study, the number of scenarios obtained from the local reduction is two compared to 512 extreme scenarios. Our approach was able to satisfy the constraints both for parametric uncertainty and time‐varying disturbances, whereas approaches from literature either violated the constraints or became computationally expensive. [ABSTRACT FROM AUTHOR]

In this paper, the delay margin problem of a linear system with multiple delays is investigated. Estimations for allowable delay bounds of the system are provided in the form of matrix measure. By means of the weighted matrix measure and optimization technologies, the conservatism of estimated delay bounds is reduced. Numerical examples show that the weighted matrix measure has better performance in estimating the delay margin of the system than usual matrix measures. [ABSTRACT FROM AUTHOR]

Summary: In this paper, an L1 adaptive output‐feedback controller is developed for multivariable nonlinear systems subject to constraints using online optimization. In the L1 adaptive architecture, an adaptive law will update the adaptive parameters that represent the nonlinear uncertainties such that the estimation error between the predicted state and the real state is driven to zero at every integration time step. Of course, neglection of the unknowns for solving the error dynamic equations will introduce an estimation error in the adaptive parameters. The magnitude of this error can be lessened by choosing a proper sampling time step. A control law is designed to compensate the nonlinear uncertainties and deliver a good tracking performance with guaranteed robustness. Model predictive control is introduced to solve a receding horizon optimization problem with various constraints maintained. Numerical examples are given to illustrate the design procedures, and the simulation results demonstrate the availability and feasibility of the developed framework. [ABSTRACT FROM AUTHOR]

Summary: This paper presents an algorithm for the computation of full‐complexity polytopic robust control invariant (RCI) sets, and the corresponding linear state‐feedback control law. The proposed scheme can be applied for linear discrete‐time systems subject to additive disturbances and structured norm‐bounded or polytopic uncertainties. Output, initial condition, and performance constraints are considered. Arbitrary complexity of the invariant polytope is allowed to enable less conservative inner/outer approximations to the RCI sets whereas the RCI set is assumed to be symmetric around the origin. The nonlinearities associated with the computation of such an RCI set structure are overcome through the application of Farkas' theorem and a corollary of the elimination lemma to obtain an initial polytopic RCI set, which is guaranteed to exist under certain conditions. A Newton‐like update, which is recursively feasible, is then proposed to yield desirable large/small volume RCI sets. [ABSTRACT FROM AUTHOR]

The paper introduces a new computationally efficient algorithm to determine a lower bound on the real structured singular value μ. The algorithm is based on a pole migration approach where an optimization solver is used to compute a lower bound on real μ independent of a frequency sweep. A distinguishing feature of this algorithm from other frequency independent one-shot tests is that multiple localized optima (if they exist) are identified and returned from the search. This is achieved by using a number of alternative methods to generate different initial conditions from which the optimization solver can initiate its search from. The pole migration algorithm presented has also been extended to determine lower bounds for complex parametric uncertainties as well as full complex blocks. However, the results presented are for strictly real and repeated parametric uncertainty problems as this class of problem is the focus of this paper and are in general the most difficult to solve. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of estimating the state for a class of uncertain discrete-time linear systems with constraints by using an optimization-based approach. The proposed scheme uses the moving horizon estimation philosophy together with the game theoretical approach to the [ABSTRACT FROM AUTHOR]

SUMMARY This paper considers the problem of placing additional phasor measurement units (PMUs) and communication links into a PMU network in a power system. The objective is for the synchrophasors (PMU outputs) across the power system to meet a prescribed availability profile with fewest new PMUs and links among them. Achieving a required synchrophasor availability profile is critical in supporting wide area monitoring, protection, and control in a power system. In our recently reported work, synchrophasor availability is solved from a controlled Markov chain where binary variables indicating placement decisions are embedded as stationary control variables. In this paper, the assumption that communication links are always available wherever needed is removed. Random interruptions in PMU communications are modeled in such a way that the reformulated placement problem remains Boolean-convex, and the computational complexity with respect to the number of buses remains O( n2). The solution to placement of PMUs and communication links into the Institute of Electrical and Electronics Engineers (IEEE) 118-bus test system is presented. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY Based on recent papers that have demonstrated that robust iterative learning control can be based on parameter optimization using either the inverse plant or gradient concepts, this paper presents a unification of these ideas for discrete-time systems that not only retains the convergence properties and the robustness properties derived in previous papers but also permits the inclusion of filters in the input update formula and a detailed analysis of the effect of non-minimum-phase dynamics on algorithm performance in terms of a 'plateauing' or 'flat-lining' effect in the error norm evolution. Although the analysis is in the time domain, the robustness conditions are expressed as frequency domain inequalities. The special case of a version of the inverse algorithm that can be used to construct a robust stable anti-causal inverse non-minimum-phase plant is presented and analysed in detail. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper proposes a consensus protocol for a class of high-order multiagent systems under directed networks. It is supposed that each agent is exposed to an external disturbance additive to its control input. Based on the [ABSTRACT FROM AUTHOR]

This paper presents a robust model predictive control algorithm with a time-varying terminal constraint set for systems with model uncertainty and input constraints. In this algorithm, the nonlinear system is approximated by a linear model where the approximation error is considered as an unstructured uncertainty that can be represented by a Lipschitz nonlinear function. A continuum of terminal constraint sets is constructed offline, and robust stability is achieved on-line by using a variable control horizon. This approach significantly reduces the computational complexity. The proposed robust model predictive controller with a terminal constraint set is used in tracking set-points for nonlinear systems. The effectiveness of the proposed method is illustrated with a numerical example. [ABSTRACT FROM AUTHOR]

This paper considers event-triggering controller design for directly observable discrete-time linear systems subject to bounded disturbances. The main control objective is diminishing the influence aroused by the disturbances despite a reduction of the communication. Criteria are given to design feedback controllers in order to guarantee that systems are uniformly ultimately bounded in an ellipsoidal-positive invariant set, which is used as an estimate of control performance for disturbance rejection. An optimization for minimizing the ellipsoidal-positive invariant set is achieved by synthesizing the feedback control gain and the given event-triggering conditions in LMIs. The effectiveness and applicability of the controller are illustrated by simulations and experimental implementations. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper we propose a fractional-order proportional-integral-derivative controller design based on the solution of an [ABSTRACT FROM AUTHOR]

SUMMARY This paper deals with convex relaxations for quadratic distance problems, a class of optimization problems relevant to several important topics in the analysis and synthesis of robust control systems. Some classes of convex relaxations are investigated using the sum of squares paradigm for the representation of positive polynomials. The main contribution is to show that two different relaxations, based respectively on the Positivstellensatz and on properties of homogeneous polynomial forms, are equivalent. Relationships among the considered relaxations are discussed and numerical comparisons are presented, highlighting their degree of conservatism. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY This paper presents novel results related to an innovative airborne wind energy technology, named Kitenergy, for the conversion of high-altitude wind energy into electricity. The research activities carried out in the last five years, including theoretical analyses, numerical simulations, and experimental tests, indicate that Kitenergy could bring forth a revolution in wind energy generation, providing renewable energy in large quantities at a lower cost than fossil energy. This work investigates three important theoretical aspects: the evaluation of the performance achieved by the employed control law, the optimization of the generator operating cycle, and the possibility to generate continuously a constant and maximal power output. These issues are tackled through the combined use of modeling, control, and optimization methods that result to be key technologies for a significant breakthrough in renewable energy generation. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]