Reliable linear-quadratic control for singularly perturbed systems

This paper studies the reliable linear-quadratic (LQ) state-feedback control for singularly perturbed systems. By time-scale decomposition, the full-order system is decomposed into the slow and fast subsystems. The reliable composite control law is given based on the designs of the two subsystems. The designed composite control guarantees the robust stability and LQ performance of the full-order closed-loop system despite any actuator outages within a selected subset of actuators. An illustrative example is presented to demonstrate the approach addressed in this paper.     

A real Schur form method for modeling singularly perturbed systems

A method is presented for modeling a two-time-scale system in the singularly perturbed form. The method uses an ordered real Schur decomposition, which can be efficiently computed using standard subroutines from EISPACK. Three results are given. First, it is shown that any two-time-scale system can be modeled in the singularly perturbed form by a transformation into an ordered real Schur form, followed by balancing. Second, under some conditions on the ordered real Schur decomposition, a procedure is given to achieve the modeling task with all fast variables chosen from the original state variables. Third, necessary and sufficient conditions are given to achieve modeling by permutation of the original state variables     

Low frequency positive real control for delta operator systems

A strictly positive real control problem for delta operator systems in a low frequency range is presented by using the generalized Kalman–Yakubovic?–Popov lemma. The objective of the strictly positive real control problem is to design a controller such that the transfer function is strictly positive real and the resulting closed-loop system is stable. Sufficient conditions for the low frequency strictly positive real controller of the closed-loop delta operator systems are presented in terms of solutions to a set of linear matrix inequalities. A numerical example is given to illustrate the effectiveness and potential for the developed techniques.     

control for fast sampling discrete-time singularly perturbed systems

This paper is concerned with the H_∞ control problem via state feedback for fast sampling discrete-time singularly perturbed systems. A new H_∞ controller design method is given in terms of solutions to linear matrix inequalities (LMIs), which eliminates the regularity restrictions attached to the Riccati-based solution. A method for evaluating the upper bound of singular perturbation parameter with meeting a prescribed H_∞ performance bound requirement is also given. Furthermore, the results are extended to robust controller design for fast sampling discrete-time singularly perturbed systems with polytopic uncertainties. Numerical examples are given to illustrate the validity of the proposed methods.     

Optimal control of discrete-time singularly perturbed systems

In this paper, we present a new algorithm for solving the optimal control of discrete-time singularly perturbed systems. The main idea of this algorithm is based on two steps. First, the Hamiltonian difference equation is reduced to the backward recursive form rather than the forward recursive form. Second, the bilinear transformation is applied to transform the derived non-symmetric discrete-time Riccati equations into continuous-time equations. In order to improve the efficiency of this scheme, two matrix permutations are introduced into this algorithm by taking into account the previous work of Gajic and Shen (1991). Therefore, substantial numerical advantages are gained; namely, computation and memory requirements. The F-8 aircraft model is used to illustrate the efficiency of the proposed method.     

Suboptimal control of singularly perturbed systems and periodicoptimization

A traditional approach to singularly perturbed optimal control problems is based on an approximation of these problems by reduced problems which are obtained via the formal replacement of the fast variables by the states of equilibrium of the fast subsystems considered with frozen slow variables and controls. It is shown that such an approximation is valid if and only if certain families of periodic optimization problems admit steady state solutions. It is also shown how the solutions of these problems can be used to construct suboptimal controls for singularly perturbed problems when approximation by reduced problems is not possible     

Economic model predictive control of nonlinear singularly perturbed systems

We focus on the development of a Lyapunov-based economic model predictive control (LEMPC) method for nonlinear singularly perturbed systems in standard form arising naturally in the modeling of two-time-scale chemical processes. A composite control structure is proposed in which, a “fast” Lyapunov-based model predictive controller (LMPC) using a quadratic cost function which penalizes the deviation of the fast states from their equilibrium slow manifold and the corresponding manipulated inputs, is used to stabilize the fast dynamics while a two-mode “slow” LEMPC design is used on the slow subsystem that addresses economic considerations as well as desired closed-loop stability properties by utilizing an economic (typically non-quadratic) cost function in its formulation and possibly dictating a time-varying process operation. Through a multirate measurement sampling scheme, fast sampling of the fast state variables is used in the fast LMPC while slow-sampling of the slow state variables is used in the slow LEMPC. Appropriate stabilizability assumptions are made and suitable constraints are imposed on the proposed control scheme to guarantee the closed-loop stability and singular perturbation theory is used to analyze the closed-loop system. The proposed control method is demonstrated through a nonlinear chemical process example.     

Averaging of three time scale singularly perturbed control systems

We investigate the asymptotic properties of singularly perturbed control systems with three time scales. We apply the averaging method to construct a limiting system for the slowest motion in the form of a differential inclusion. Sufficient conditions for the uniform convergence of the slowest trajectories are given.     

New results for near-optimal control of linear multiparameter singularly perturbed systems

In this paper, we consider the linear quadratic optimal control problem for multiparameter singularly perturbed systems in which N lower-level fast subsystems are interconnected through a higher-level slow subsystem. Different from the existing methods, a new method is developed to design a near-optimal controller which does not depend on the unknown small parameters. It is shown that the resulting controller in fact achieves an O(||μ||²) approximation to the optimal cost of the original optimal control problem.     

Sliding surface design for singularly perturbed systems

The equilibrium manifold of a singularly perturbed system has a close relationship with the sliding surface of a variable structure system (VSS). The fast time and slow timeresponses has a similar behaviour to the 'reaching mode' and 'sliding mode', respectively. This paper aims to equip the powerful composite control method with robustness through variable structure control design. The major bridge in between is a Lyapunov function. It is found that a singularly perturbed system in sliding mode may preserve two-time-scale attribute, in which a new equilibrium manifold exists on the sliding surface. Sliding motions that are attracted to the manifold can therefore be referred to as 'sliding mode in sliding mode'.     

Quadratic-type Lyapunov functions for singularly perturbed systems

Asymptotic and exponential stability of nonlinear singularly perturbed systems are investigated via Lyapunov stability techniques. A quadratic-type Lyapunov function for a singularly perturbed system is obtained as a weighted sum of quadratic-type Lyapunov functions of two lower order systems. Estimates of domain of attraction, of upper bound on perturbation parameter, and of degree of exponential stability are obtained. The method is illustrated by studying the stability of a synchronous generator connected to an infinite bus.     

Robust state estimation for singularly perturbed systems

This paper deals with the design of interval observers for singularly perturbed linear systems. The full-order system is first decoupled into slow and fast subsystems. Then, using the cooperativity theory, an interval observer is designed for the slow and fast subsystems assuming that the measurement noise and the disturbances are bounded and the singular perturbed parameter is uncertain. This decoupling leads to two observers that estimate the lower and upper bounds for the feasible state domain. A numerical example shows the efficiency of the proposed technique.     

Constrained feedback control of imperfectly known singularly perturbed non-linear systems

Global attractors are investigated for a class of imperfectly known, singularly perturbed, dynamic control systems. The uncertain systems are modelled as non-linear perturbations to a known non-linear idealized system and are represented by two time-scale subsystems. The two subsystems, which depend on a scalar singular perturbation parameter, represent a singularly perturbed system which has the property that the system reduces to one of lower order when the singular perturbation parameter is set to zero. It is assumed that the full-order system is subject to constraints on the control inputs. A class of constrained feedback controllers is developed which assures global uniform attraction of a compact set, containing the state origin, for all values of the singular perturbation parameter less than some threshold value.     

Optimal control of ε-coupled and singularly perturbed distributed-parameter systems

Many distributed-parameter systems consist of interconnected subsystems involving fast and slow physical phenomena or reducing to a number of independent subsystems when a scalar parameter ε is zero. The purpose of this paper is to treat the control of such systems by invoking the ε-coupling and singular perturbation approaches developed by Kokotovic and his co-workers for lumped-parameter large-scale systems. In the case of ε- coupled distributed-parameter systems it is shown that the optimal state feedback matrix can be approximated by a Volterra-MacLaurin series with coefficients determined by solving two lower-order decoupled Riccati and linear equations. By using an mth-order approximation of the optimal feedback matrix, one obtains a (2m+1)th order approximation of the optimal performance function. In the singular perturbation approach the result is that for an O(ε²) suboptimal control one must solve two decoupled Riccati equations, one for the fast and one for the slow subsystem, and then construct appropriately the composite control law. By using only the Riccati equation for the slow subsystem, one obtains an O(ε) suboptimal control. The singular perturbation technique is then used to treat interconnected distributed-parameter systems involving may strongly coupled slow subsystems and weakly coupled fast subsystems.     

On the model-based networked control for singularly perturbed systems with nonlinear uncertainties

In this paper, the model-based networked control is addressed for a class of singularly perturbed control systems with nonlinear uncertainties. An approximate linear slow and fast control system of the plant, which can be obtained by omitting the nonlinear uncertainties, are used as a model to estimate the state behavior of the plant between transmission times. The stability of model-based networked control systems is investigated under the assumption that the controller/actuator is updated with the sensor information at constant time intervals. It is shown that there exists the allowable upper bound of the singular perturbation parameter such that the model-based networked control system is globally exponentially stable.     

Robust stabilization of singularly perturbed systems

We discuss the problem of designing stabilizing controllers for singularly perturbed systems on the basis of simplified models. In [1], it was shown that a constant gain output feedback controller designed on the basis of the simplified model need not stabilize the ‘true’ system containing both fast and slow modes. This phenomenon was then expanded to include the case where the simplified system is strictly proper in [2]. The objectives of this note are threefold: (i) to show that, given any proper system and any stabilizing controller for it that is proper but not strictly proper, there exists a singular perturbation of the system that is destabilized by that controller, (ii) to show that any strictly proper controller for a singularly perturbed system designed on the basis of a reduced order model will stabilize the true system for sufficiently small values of the fast dynamics parameter, and (iii) to provide a characterization, in the same spirit as [3,4], of the set of all strictly proper controllers that stabilize a given proper plant. By combining these results, it is possible to generate the class of all robustly stabilizing controllers for a given singularly perturbed system.     

Near-optimum regulators for stochastic linear singularly perturbed systems

This paper presents a new approach to the decomposition and approximation of linear-quadratic-Gaussian estimation and control problems for singularly perturbed systems. The Kalman filter is decomposed into separate slow-mode and fast-mode filters via the use of a decoupling transformation. A near-optimal control law is derived by approximating the coefficients of the optimal control law. The order of approximation of the optimal performance is0(mu^{N})whereNis the order of approximation of the coefficients.     

Near optimal smoothing for singularly perturbed linear systems

A two time-scale lower order design is proposed as a near optimal solution to the fixed-interval smoothing problem for systems with slow and fast modes. The slow mode smoothing solution, in the limit as the perturbation parameter μ → 0, tends to that of the reduced-order problem; and the near optimal fast mode smoother is simply a weighted sum of lower order two time-scale filters. While the near optimal solution affords a significant reduction in computational complexity, the performance degradation, as illustrated in an example, is typically negligible.     

Sensitivity analysis of singularly perturbed systems

A new variational approach is described to compute singular sensitivity functions of finite dimensional systems with respect to changes in system structure. The reduced nominal model and its adjoint system are used to define singular sensitivity functions. A relation between the present method and singularly perturbed optimal control theory is established. It is shown that the present variational approach gives a computationally effective method in singular sensitivity analysis. The method is applied to singularly perturbed linear ordinary differential equations and singular optimal control problem.