Modeling the system of suboptimal robust tracking under unknown upper bounds on the uncertainties and external disturbances

This paper addresses a problem of suboptimal robust tracking for a discrete-time plant under unknown upper bounds on external disturbances. The transfer function of the nominal plant model is assumed to be known, whereas the upper bounds on the external disturbance, measurement noise, and coprime factor perturbations are assumed to be unknown. The results of numerical modeling of the algorithm of suboptimal robust tracking based on relaxed verification of the estimates of upper bound in the closed loop and the use of control criterion associated with such verification as the identification criterion are presented and analyzed. The results of numerical modeling illustrate efficiency of the proposed method of control design.     

On the Achievable Delay Margin Using LTI Control for Unstable Plants

Handling delays in control systems is difficult and is of long-standing interest. It is well known that, given a finite-dimensional linear time-invariant (FDLTI) plant and controller forming a strictly proper stable feedback connection, closed-loop stability will be maintained under a small delay in the feedback loop, although most closed loop systems become unstable for large delays. One previously unsolved fundamental problem in this context is whether, for a given FDLTI plant, an arbitrarily large delay margin can be achieved using LTI control. Here, we adopt a frequency domain approach and demonstrate that, for a strictly proper real rational plant, there is a uniform upper bound on the delay that can be tolerated when using an LTI controller, if and only if the plant has at least one closed right half plane pole not at the origin. We also give several explicit upper bounds on the achievable delay margin, and, in some special cases, demonstrate that these bounds are tight.     

一类多时延异质多智能体系统的一致性

针对一类具有多变时延的一阶与二阶异构多智能体系统,研究了一致性问题。首先在异质系统中设计了一致性控制算法,通过将一阶、二阶系统重排和模型变换,使原系统分解成多个简单子系统;其次,利用李雅普洛夫稳定理论,分别在固定有向拓扑结构和切换有向拓扑结构下,以线性矩阵不等式的形式给出了系统达到平均一致的充分条件;再次,通过求解一组可行的线性矩阵不等式,得到了多时延的一个容许上界;然后讨论了时延导数和控制增益对时延容许上界的影响,得出了时延上界与时延导数和控制增益分别成正相关及反相关,时延导数未知时的时延上界相对较小的结论;最后的仿真结果表明了理论结果的有效性。     

A New $H_{{bm infty}}$ Stabilization Criterion for Networked Control Systems

This note is concerned with robust H_infin control of linear networked control systems with time-varying network-induced delay and data packet dropout. A new Lyapunov-Krasovskii functional, which makes use of the information of both the lower and upper bounds of the time-varying network-induced delay, is proposed to drive a new delay-dependent H_infin stabilization criterion. The criterion is formulated in the form of a non-convex matrix inequality, of which a feasible solution can be obtained by solving a minimization problem in terms of linear matrix inequalities. In order to obtain much less conservative results, a tighter bounding for some term is estimated. Moreover, no slack variable is introduced. Finally, two numerical examples are given to show the effectiveness of the proposed design method.     

Robust tracking with unknown upper bounds on the perturbations and measurement noise

We consider the robust tracking problem for a discrete time control object with unknown upper bounds on the perturbations. The transition function in the nominal model of the controllable object is assumed to be known, while upper bounds on the external perturbation, measurement noise, and operator perturbations with respect to both output and control are assumed to be unknown or rough. Current estimates of upper bounds on the perturbations are obtained from measurement data with the quality criterion of the tracking problem as the identification criterion.     

不确定时滞组合系统的分散鲁棒镇定

讨论不确定时滞组合系统的分散自适应鲁棒镇定问题．外部扰动存在于子系统内部,可以是非线性或时变的,且不确定项和时滞存在于互联项中．不确定项和外部扰动是有界的,但上界未知．利用自适应律估计未知的上界,设计了非线性无记忆控制器．采用非线性控制器可保证闭环组合系统的解一致有界。且系统状态是一致渐近稳定的．仿真结果表明了该设计方法的有效性.     

Adaptive suboptimal robust control of the first-order plant

For a linear first-order plant, consideration was given to minimization of the deviation of its output from the specified value in an uncertain environment. Both the parameters of the plant itself and the upper bounds of the external disturbances and operator perturbations in output and control were assumed to be unknown. The suboptimal adaptive control was constructed on the basis of an approximate solution of the problem of optimal identification from the measurement data where the performance of the problem of control was used as an identification criterion.     

时滞不确定组合系统的鲁棒分散输出控制

讨论了时滞不确定组合系统的鲁棒分散输出控制问题．不确定项存在于子系统内部,可以是非线性或时交的：而且时滞存在互联项中,并满足匹配条件．不确定项是有界的,但界是未知的．利用自适应律来估计未知的上界,设计出分散无记忆输出控制器．基于Lyapunov稳定性理论和Lyapunov-Krasovskii函数,该控制器能够保证闭环系统的解是终极一致有界的．最后的仿真结果说明该设计方法是有效的．     

Worst-case/deterministic identification in H∞: the continuous-time case

Results obtained by the authors (1991) worst-case/deterministic H_∞ identification of discrete-time plants are extended to continuous-time plants. The problem involves identification of the transfer function of a stable strictly proper continuous-time plant from a finite number of noisy point samples of the plant frequency response. The assumed information consists of a lower bound on the relative stability of the plant, an upper bound on a certain gain associated with the plant, an upper bound on the roll-off rate of the plant, and an upper bound on the noise level. Concrete plans of identification algorithms are provided for this problem. Explicit worst-case/deterministic error bounds for each algorithm establish that they are robustly convergent and (essentially) asymptotically optimal. Additionally, these bounds provide an a priori computable H_∞ uncertainty specification, corresponding to the resulting identified plant transfer function, as an explicit function of the plant and noise prior information and the data cardinality     

H ∞ observer design for uncertain nonlinear discrete‐time systems with time‐delay: LMI optimization approach

We present a robust H _∞ observer for a class of nonlinear discrete‐time systems. The class under study includes an unknown time‐varying delay limited by upper and lower bounds, as well as time‐varying parametric uncertainties. We design a nonlinear H _∞ observer, by using the upper and lower bounds of the delay, that guarantees asymptotic stability of the estimation error dynamics and is also robust against time‐varying parametric uncertainties. The described problem is converted to a standard optimization problem, which can be solved in terms of linear matrix inequalities (LMIs). Then, we expand the problem to a multi‐objective optimization problem in which the maximum admissible Lipschitz constant and the minimum disturbance attenuation level are the problem objectives. Finally, the proposed observer is illustrated with two examples. Copyright © 2014 John Wiley & Sons, Ltd.     

Numerical analysis of worst-case end-to-end delay bounds in FIFO tandem networks

This paper addresses the problem of computing end-to-end delay bounds for a traffic flow traversing a tandem of FIFO multiplexing network nodes using Network Calculus. Numerical solution methods are required, as closed-form delay bound expressions are unknown except for few specific cases. For the methodology called the Least Upper Delay Bound, the most accurate among those based on Network Calculus, exact and approximate solution algorithms are presented, and their accuracy and computation cost are discussed. The algorithms are inherently exponential, yet affordable for tandems of up to few tens of nodes, and amenable to online execution in cases of practical significance. This complexity is, however, required to compute accurate bounds. As the LUDB may actually be larger than the worst-case delay, we assess how close the former is to the latter by computing lower bounds on the worst-case delay and measuring the gap between the lower and upper bound.     

On exponential stability of linear networked control systems

This paper addresses exponential stability of linear networked control systems. More specifically, the paper considers a continuous‐time linear plant in feedback with a linear sampled‐data controller with an unknown time varying sampling rate, the possibility of data packet dropout, and an uncertain time varying delay. The main contribution of this paper is the derivation of new sufficient stability conditions for linear networked control systems taking into account all of these factors. The stability conditions are based on a modified Lyapunov–Krasovskii functional. The stability results are also applied to the case where limited information on the delay bounds is available. The case of linear sampled‐data systems is studied as a corollary of the networked control case. Furthermore, the paper also formulates the problem of finding a lower bound on the maximum network‐induced delay that preserves exponential stability as a convex optimization program in terms of linear matrix inequalities. This problem can be solved efficiently from both practical and theoretical points of view. Finally, as a comparison, we show that the stability conditions proposed in this paper compare favorably with the ones available in the open literature for different benchmark problems. Copyright © 2012 John Wiley & Sons, Ltd.     

Stability of uncertain piecewise affine systems with time delay: delay-dependent Lyapunov approach

This article addresses the problem of robust stability of piecewise affine (PWA) uncertain systems with unknown time-varying delay in the state. It is assumed that the uncertainty is norm bounded and that upper bounds on the state delay and its rate of change are available. A set of linear matrix inequalities (LMIs) is derived providing sufficient conditions for the stability of the system. These conditions depend on the upper bound of the delay. The main contributions of the article are as follows. First, new delay-dependent LMI conditions are derived for the stability of PWA time-delay systems. Second, the stability conditions are extended to the case of uncertain PWA time delay systems. Numerical examples are presented to show the effectiveness of the approach.     

New Approach on Robust Delay-Dependent ${H}_infty$ Control for Uncertain T--S Fuzzy Systems With Interval Time-Varying Delay

This paper investigates the robust ${H}_{infty }$ control for Takagi--Sugeno (T--S) fuzzy systems with interval time-varying delay. By employing a new and tighter integral inequality and constructing an appropriate type of Lyapunov functional, delay-dependent stability criteria are derived for the control problem. Because neither any model transformation nor free weighting matrices are employed in our theoretical derivation, the developed stability criteria significantly improve and simplify the existing stability conditions. Also, the maximum allowable upper delay bound and controller feedback gains can be obtained simultaneously from the developed approach by solving a constrained convex optimization problem. Numerical examples are given to demonstrate the effectiveness of the proposed methods.      

Performance of a neural adaptive tracking controller formulti-input nonlinear dynamical systems in the presence of additive andmultiplicative external disturbances

We discuss the tracking problem in the presence of additive and multiplicative external disturbances, for affine in the control nonlinear dynamical systems, whose nonlinearities are assumed unknown. Based on a recurrent high order neural network (RHONN) model of the unknown plant, a smooth control law is designed to guarantee the uniform ultimate boundedness of all signals in the closed loop. Certain measures are utilized to test its performance. The controller, which can be viewed as a nonlinear combination of three high order neural networks, does not require knowledge regarding upper bounds on the optimal weights, modeling error and external disturbances. Simulations performed on a simple example illustrate the approach     

Delay‐dependent Stability and Static Output Feedback Control of 2‐D Discrete Systems with Interval Time‐varying Delays

This paper is concerned with the problems of delay‐dependent stability and static output feedback (SOF) control of two‐dimensional (2‐D) discrete systems with interval time‐varying delays, which are described by the Fornasini‐Marchesini (FM) second model. The upper and lower bounds of delays are considered. Applying a new method of estimating the upper bound on the difference of Lyapunov function that does not ignore any terms, a new delay‐dependent stability criteria based on linear matrix inequalities (LMIs) is derived. Then, given the lower bounds of time‐varying delays, the maximum upper bounds in the above LMIs are obtained through computing a convex optimization problem. Based on the stability criteria, the SOF control problem is formulated in terms of a bilinear matrix inequality (BMI). With the use of the slack variable technique, a sufficient LMI condition is proposed for the BMI. Moreover, the SOF gain can be solved by LMIs. Numerical examples show the effectiveness and advantages of our results.     

Robust design of feedback integrated with iterative learning control for batch processes with uncertainties and interval time-varying delays

A robust feedback integrated with iterative learning control (FILC) scheme for batch processes with uncertain perturbations and interval time-varying delay is developed. The batch process is modeled as a two-dimensional (2D) Rosser system with a delay varying in a range. The design of FILC scheme is transformed into a robust control problem of uncertain 2D system. New delay-range-dependent stability criteria and stabilization conditions are derived in terms of linear matrix inequalities (LMIs), which depend on not only the difference between the upper and lower delay bounds but also the upper delay bound of the interval time-varying delay. Parameterized characterizations for stabilizing the controller are given in terms of the feasibility solutions to the LMIs. Applications to injection velocity control show that the proposed FILC achieve the design objectives well.     

A learning result for continuous-time recurrent neural networks

The following learning problem is considered, for continuous-time recurrent neural networks having sigmoidal activation functions. Given a “black box” representing an unknown system, measurements of output derivatives are collected, for a set of randomly generated inputs, and a network is used to approximate the observed behavior. It is shown that the number of inputs needed for reliable generalization (the sample complexity of the learning problem) is upper bounded by an expression that grows polynomially with the dimension of the network and logarithmically with the number of output derivatives being matched.     

Worst case identification of continuous time systems via interpolation

We consider a worst case robust control oriented identification problem recently studied by several authors. This problem is one of identification in the continuous time setting. We give a more general formulation of this problem. The available a priori information in this paper consists of a lower bound on the relative stability of the plant, a frequency dependent upper bound on a certain gain associated with the plant, and an upper bound on the noise level. The available experimental information consists of a finite number of noisy plant point frequency response samples. The objective is to identify, from the given a priori and experimental information, an uncertain model that includes a stable nominal plant model and a bound on the modeling error measured in norm. Our main contributions include both a new identification algorithm and several new ‘explicit’ lower and upper bounds on the identification error. The proposed algorithm belongs to the class of ‘interpolatory algorithms’ which are known to possess a desirable optimality property under a certain criterion. The error bounds presented improve upon the previously available ones in the aspects of both providing a more accurate estimate of the identification error as well as establishing a faster convergence rate for the proposed algorithm.     

Stability analysis of systems with uncertain time-varying delays

Stability of systems in the presence of bounded uncertain time-varying delays in the feedback loop is studied. The delay parameter is assumed to be an unknown time-varying function for which the upper bounds on the magnitude and the variation are given. The stability problem is treated in the integral quadratic constraint (IQC) framework. Criteria for verifying robust stability are formulated as feasibility problems over a set of frequency-dependent linear matrix inequalities. The criteria can be equivalently formulated as semi-definite programs (SDP) using Kalman-Yakubovich-Popov lemma. As such, checking robust stability can be performed in a computationally efficient fashion.