Decentralized robust control for a class of uncertain large-scale interconnected nonlinear dynamical systems

The problem of the decentralized robust control for a class of large-scale interconnected nonlinear dynamical systems with input interconnection and external interconnection perturbations is considered. Based on the stabilizability of each nominal isolated subsystem (i.e., the isolated subsystem in the absence of interconnection perturbations), a class of decentralized local state feedback controllers is proposed, and some sufficient conditions are derived by making use of the Lyapunov stability criterion such that uncertain large-scale interconnected systems can be stabilized asymptotically by these decentralized state feedback controllers. For large-scale systems with only input interconnection perturbations, such decentralized controllers become a class of decentralized stabilizing state feedback controllers. That is, the decentralized stability of such large-scale systems can be guaranteed always by using the decentralized state feedback controllers proposed in the paper. Finally, a numerical example is given to demonstrate the validity of the results.     

A locally computed suboptimal control strategy for a class of interconnected systems

In this paper, a locally computed suboptimal control strategy for a class of interconnected systems is introduced. First, optimal statefeedback control equations are derived for a finite-horizon quadratic cost. Then, the control for each subsystem is separated into two portions. The first portion stabilizes the isolated subsystem, and the second portion corresponds to the interactions. To achieve a locally calculable control, an approximation to the optimal control equations is introduced, and two iterative suboptimal control algorithms are developed. In the first algorithm, the initial conditions of subsystems are assumed to be known; in the second algorithm, this information is replaced by statistical distributions. The orders of errors in the iterations of the algorithm and in the suboptimality are given in terms of interconnections. An example with comparisons is also included to show the performance of the approach.     

Iterative learning control for discrete-time switched singular systems

This paper deals with the problem of iterative learning control for a class of discrete-time switched singular systems with arbitrary switching rules. According to the characteristics of the systems, two types of iterative learning algorithms are proposed and the corresponding convergence conditions of the algorithms are established. Under some given assumptions, the algorithms can ensure the system state converges to the desired state trajectory on a finite time interval. Finally, two numerical examples are constructed to support the theoretical analysis.     

Decentralized stabilization of a class of interconnected systems

Abstract. This paper is concerned with the decentralized stabilization of continuous and discretelinear interconnected systems with the structural constraints about the interconnection matri-ces. For the continuous case,the main improvement in the paper as compared with the corre-sponding results in the literature is to extend the considered class of systems from S to S“ (bothwill be defined in the paper) without resulting in high decentralized gain and difficult numericalcomputation. The algorithm for obtaining decentralized state feedback control to stable theoverall system is presented. The discrete case and some very useful results are discussed aswell.     

Decentralized optimum load frequency control of interconnected power systems

A new approach for designing a linear regulator for the problem of load frequency control (LFC) of interconnected power systems is developed. The control is specified to be of proportional-plus-integral (P-I) form and is only a function of the measurable states. The LFC problem is formulated as a parameter optimization problem.This work was supported in part by the National Research Council of Canada, Grant No. A4146.     

Decentralized controllers design for open-channel hydraulic systems via eigenstructure assignment

In this paper, we propose the design of both a proportional (P) and a proportional integral (PI) decentralized controller for open-channel hydraulic systems by assigning the closed-loop eigenstructure. The system dynamic is described by a linear, time-invariant model deduced from the Saint-Venant equations. A constant-volume control law is designed, satisfying the requirement of decentralization, typical of large-scale systems like the hydraulic one herein examined. The synthesis procedure followed in this paper allows us to derive a parametric expression for the set of feedback gains of decentralized controllers which achieve the desired eigenvalue assignment. The free parameters in this parametric expression can be used to assign eigenvectors as close to the desired ones as possible, while achieving the required eigenvalue assignment.     

Generalizations of the nonstationary multisplitting iterative method for symmetric positive definite linear systems

In this paper, we generalize the nonstationary parallel multisplitting iterative method for solving the symmetric positive definite linear systems. With several choices of variable weighting matrices, the convergence properties of these generalized methods can be improved. Finally, the numerical comparison of several nonstationary parallel multisplitting methods are shown.     

A remark on uniqueness of large solutions for elliptic systems of competitive type

We prove that the semilinear system Δu=a(x)upvq, Δv=b(x)urvs in a smooth bounded domain Ω⊂RN has a unique positive solution with the boundary condition u=v=+∞ on ∂Ω, provided that p,s>1, q,r>0 and (p−1)(s−1)−qr>0. The main novelty is imposing a growth on the possibly singular weights a(x), b(x) near ∂Ω, rather than requiring them to have a precise asymptotic behavior.     

The existence of minimal large positive solutions for a class of nonlinear cooperative systems

In this paper we establish the existence of the minimal large positive solution for a general class of nonlinear cooperative systems including the simplest prototype of García-Melián et al. (2016). Precisely, based on the existence of a large positive supersolution, we can infer the existence of the minimal large positive solution. Moreover, we also give some sufficient easily computable conditions for the existence of a large positive supersolution. Our results generalize, very substantially, some of the findings of García-Melián et al. (2016) adopting a rather novel methodology.     

Iterative learning control design of nonlinear multiple time-delay systems

Most available results on iterative learning control address trajectory tracking problem for systems without multiple time-delay. This paper is concerned with iterative learning control design for nonlinear systems with multiple delays, in which external disturbances and output measurement noises are involved. We obtain some new and interesting criteria to guarantee the convergence of the tracking error in the sense of the λ − norm. It will be shown that the convergence of the system outputs to the desired trajectory is ensured in the absence of disturbances and output measurement noises. In the presence of disturbance and measurement noises, we estimate the upper bound of the tracking error. In order to confirm the validity of the present results, numerical simulation is also presented.     

Decentralized robust control design for uncertain delay systems

We consider a class of large-scale uncertain delay systems. The uncertain parameter vector in the system is possibly fast time-varying. It may be nonlinear in the system dynamics. No statistical or fuzzy information of the uncertainty is known. Based on only the possible bound of the uncertain parameter, a decentralized linear robust control is proposed, which renders the system asymptotically stable.     

Boundaries of the receding horizon control for interconnected systems

In recent years, the finite-horizon quadratic minimization problem has become popular in process control, where the horizon is constantly rolled back. In this paper, this type of control, which is also called the receding horizon control, is considered for interconnected systems. First, the receding horizon control equations are formulated; then, some stability conditions depending on the interconnection norms and the horizon lengths are presented. For -coupled systems, stability results similar to centralized systems are obtained. For interconnected systems which are not -coupled, the existence of a horizon length and a corresponding stabilizing receding horizon control are derived. Finally, the performance of a locally computed receding horizon control for time-invariant and time-varying systems with different updating intervals is examined in an example.     

An incremental least squares algorithm for large scale linear classification

In this work we consider the problem of training a linear classifier by assuming that the number of data is huge (in particular, data may be larger than the memory capacity). We propose to adopt a linear least-squares formulation of the problem and an incremental recursive algorithm which requires to store a square matrix (whose dimension is equal to the number of features of the data). The algorithm (very simple to implement) converges to the solution using each training data once, so that it effectively handles possible memory issues and is a viable method for linear large scale classification and for real time applications, provided that the number of features of the data is not too large (say of the order of thousands). The extensive computational experiments show that the proposed algorithm is at least competitive with the state-of-the-art algorithms for large scale linear classification.     

A class of new iterative methods for general mixed variational inequalities

In this paper, we use the auxiliary principle technique to suggest a class of predictor-corrector methods for solving general mixed variational inequalities. The convergence of the proposed methods only requires the partially relaxed strongly monotonicity of the operator, which is weaker than co-coercivity. As special cases, we obtain a number of known and new results for solving various classes of variational inequalities and related problems.     

On a class of dynamical systems with emerging cluster structure

In this paper we discuss and analyze a two-parameter family of systems of quadratic ordinary differential equations of interest in applied sciences, whose dynamics exhibits an emerging cluster structure.     

Decentralized optimal control of a group of dynamical objects

The problem of optimal control of a group of coupled dynamical objects is considered. The cases are examined in which the centralized control of a group of objects is impossible. Fast real-time optimal control algorithms of each of the dynamical systems are described that use information exchanged between group members in the course of control. The proposed methods supplement the earlier developed real-time optimal control methods for an individual dynamical system. The results are illustrated using optimal control of two coupled mathematical pendulums as an example.     

Model-based iterative learning control of Parkinsonian state in thalamic relay neuron

Although the beneficial effects of chronic deep brain stimulation on Parkinson’s disease motor symptoms are now largely confirmed, the underlying mechanisms behind deep brain stimulation remain unclear and under debate. Hence, the selection of stimulation parameters is full of challenges. Additionally, due to the complexity of neural system, together with omnipresent noises, the accurate model of thalamic relay neuron is unknown. Thus, the iterative learning control of the thalamic relay neuron’s Parkinsonian state based on various variables is presented. Combining the iterative learning control with typical proportional–integral control algorithm, a novel and efficient control strategy is proposed, which does not require any particular knowledge on the detailed physiological characteristics of cortico-basal ganglia-thalamocortical loop and can automatically adjust the stimulation parameters. Simulation results demonstrate the feasibility of the proposed control strategy to restore the fidelity of thalamic relay in the Parkinsonian condition. Furthermore, through changing the important parameter—the maximum ionic conductance densities of low-threshold calcium current, the dominant characteristic of the proposed method which is independent of the accurate model can be further verified.     

Global exponential stability of almost periodic solution for a large class of delayed dynamical systems

Research on delayed neural networks with variable self-inhibitions, interconnection weights, and inputs is an important issue. In this paper, we discuss a large class of delayed dynamical systems with almost periodic self-inhibitions, inter-connection weights, and inputs. This model is universal and includes delayed systems with time-varying delays, distributed delays as well as combination of both. We prove that under some mild conditions, the system has a unique almost periodic solution, which is globally exponentially stable. We propose a new approach, which is independent of existing theory concerning with existence of almost periodic solution for dynamical systems.     

An inexact secant algorithm for large scale nonlinear systems of equalities and inequalities

In this paper, an inexact secant algorithm in association with nonmonotone technique and filter is proposed for solving the large scale nonlinear systems of equalities and inequalities. The systems are transformed into a continuous constrained optimization solved by inexact secant algorithm. Global convergence of the proposed algorithm is established under the reasonable conditions. Numerical results validate the effectiveness of our approach.     

Iterative computation of negative curvature directions in large scale optimization

In this paper we deal with the iterative computation of negative curvature directions of an objective function, within large scale optimization frameworks. In particular, suitable directions of negative curvature of the objective function represent an essential tool, to guarantee convergence to second order critical points. However, an “adequate” negative curvature direction is often required to have a good resemblance to an eigenvector corresponding to the smallest eigenvalue of the Hessian matrix. Thus, its computation may be a very difficult task on large scale problems. Several strategies proposed in literature compute such a direction relying on matrix factorizations, so that they may be inefficient or even impracticable in a large scale setting. On the other hand, the iterative methods proposed either need to store a large matrix, or they need to rerun the recurrence. On this guideline, in this paper we propose the use of an iterative method, based on a planar Conjugate Gradient scheme. Under mild assumptions, we provide theory for using the latter method to compute adequate negative curvature directions, within optimization frameworks. In our proposal any matrix storage is avoided, along with any additional rerun.