Time domain model order reduction using general orthogonal polynomials for K-power bilinear systems

In this paper, we propose a model order reduction (MOR) method based on general orthogonal polynomials for K-power bilinear systems in the time domain. Constructing proper projection matrices by solving a series of linear equations, a reduced K-power bilinear system is produced, which preserves the original coupled structure. It can match several expansion coefficients of the original output. Then the error bound of our algorithm is also investigated. Moreover, the stability of the reduced system is discussed as well. Finally, two numerical examples are provided to illustrate the effectiveness of our algorithm.     

Time-interval model reduction of bilinear systems

In this paper, a new method for model reduction of bilinear systems is presented. The proposed technique is from the family of gramian-based model reduction methods. The method uses time-interval generalised gramians in the reduction procedure rather than the ordinary generalised gramians and in such a way that it improves the accuracy of the approximation within the time-interval for which the method is applied. The time-interval generalised gramians are the solutions to the generalised time-interval Lyapunov equations. The conditions for these equations to be solvable are derived and an algorithm is proposed to solve these equations iteratively. The method is further illustrated with the help of two illustrative examples. The numerical results show that the method is more accurate than its previous counterpart which is based on the ordinary gramians.     

Generalised tangential interpolation for model reduction of discrete-time MIMO bilinear systems

In this article, we discuss a model order reduction method for multiple-input and multiple-output discrete-time bilinear control systems. Similar to the continuous-time case, we will show that a system can be characterised by a series of generalised transfer functions. This will be achieved by a multivariate Z-transform of kernels corresponding to an explicit solution formula for discrete-time systems. We will further address the problem of generalised tangential interpolation which naturally comes along with this approach. We will introduce a reasonable generalisation of the linear ?₂-norm. Based on this concept, we discuss the choice of interpolation points. Furthermore, an efficient discretisation of continuous-time systems is provided. The performance of the proposed method is evaluated in some numerical examples and compared with the method of balanced truncation for bilinear systems.     

Multi-order Arnoldi-based model order reduction of second-order time-delay systems

In this paper, we discuss the Krylov subspace-based model order reduction methods of second-order systems with time delays, and present two structure-preserving methods for model order reduction of these second-order systems, which avoid to convert the second-order systems into first-order ones. One method is based on a Krylov subspace by using the Taylor series expansion, the other method is based on the Laguerre series expansion. These two methods are used in the multi-order Arnoldi algorithm to construct the projection matrices. The resulting reduced models can not only preserve the structure of the original systems, but also can match a certain number of approximate moments or Laguerre expansion coefficients. The effectiveness of the proposed methods is demonstrated by two numerical examples.     

H2 optimal model order reduction by two-sided technique on Grassmann manifold via the cross-gramian of bilinear systems

In this paper, we discuss the optimal H₂ model order reduction (MOR) problem for bilinear systems. The H₂ optimal MOR problem of bilinear systems is considered as the minimisation problem on Grassmann manifold, which is stored as a quotient space of the noncompact Stifiel manifold. Grassmann manifold whose tangent space is endowed with a Riemannian metric is a Riemannian manifold. In its tangent space equipped with the Riemannian metric, we derive the negative gradients of the cost function, i.e. the steepest descent direction of the cost function. After that, the formulas of geodesic on Grassmann manifold are given. Then we perform linear searches along geodesics to obtain the optimal solutions. Thereby, a two-sided MOR iterative algorithm is proposed to construct an order-reduced bilinear system, which is used to simulate the output and input responses of the original bilinear system. Numerical examples demonstrate the effectiveness of our algorithm.     

A parameterised model order reduction method for parametric systems based on Laguerre polynomials

This paper presents a Laguerre polynomials-based parametrised model order reduction method for the parametric system in time domain. The method allows that the parametric dependence in system matrices is nonaffine. The method is presented via reducing an approximate polynomial parametric system based on Taylor expansion and Laguerre polynomials, resulting in a parametric reduced system that can accurately approximate the time response of the original parametric system over a wide range of parameter. The reduced parametric system obtained by proposed method can be implemented by two algorithms. Algorithm 1 is a direct way that is suitable for single-input multi-output parametric systems. Algorithm 2 is presented based on a connection to the Krylov subspace, which is efficient and suitable for multi-input multi-output parametric systems. The effectiveness of the proposed method is illustrated with two benchmarks in practical applications.     

An unconstrained H 2 model order reduction optimisation algorithm based on the Stiefel manifold for bilinear systems

In this paper, the optimal H ₂ model order reduction (MOR) problem for bilinear systems is explored. The orthogonality constraint of the cost function generated by the H ₂ MOR error makes it is posed not on the Euclidean space, but can be discussed on the Stiefel manifold. Then, the H ₂ optimal MOR problem of bilinear systems is turned into the unconstrained optimisation on the Stiefel manifold. The explicit expression of the gradient for the cost function on this manifold is derived. Full use of the geometry properties of this Stiefiel manifold, we propose a feasible and effective iterative algorithm to solve the unconstrained H ₂ minimisation problem. Moreover, the convergence of our algorithm is rigorously proved. Finally, two practical examples related to bilinear systems demonstrate the effectiveness of our algorithm.     

Model-order reductions for MIMO systems using global Krylov subspace methods

This paper presents theoretical foundations of global Krylov subspace methods for model order reductions. This method is an extension of the standard Krylov subspace method for multiple-inputs multiple-outputs (MIMO) systems. By employing the congruence transformation with global Krylov subspaces, both one-sided Arnoldi and two-sided Lanczos oblique projection methods are explored for both single expansion point and multiple expansion points. In order to further reduce the computational complexity for multiple expansion points, adaptive-order multiple points moment matching algorithms, or the so-called rational Krylov space method, are also studied. Two algorithms, including the adaptive-order rational global Arnoldi (AORGA) algorithm and the adaptive-order global Lanczos (AOGL) algorithm, are developed in detail. Simulations of practical dynamical systems will be conducted to illustrate the feasibility and the efficiency of proposed methods.     

H∞ model reduction for discrete-time singular systems

This paper investigates the problem of H_∞ model reduction for linear discrete-time singular systems. Without decomposing the original system matrices, necessary and sufficient conditions for the solvability of this problem are obtained in terms of linear matrix inequalities (LMIs) and a coupling non-convex rank constraint set. When these conditions are feasible, an explicit parametrization of the desired reduced-order models is given. Particularly, a simple LMI condition without rank constraint is derived for the zeroth-order H_∞ approximation problem. Finally, an illustrative example is provided to demonstrate the applicability of the proposed approach.     

On H2 model reduction of bilinear systems

The H₂ model reduction problem for continuous-time bilinear systems is studied in this paper. By defining the H₂ norm of bilinear systems in terms of the state-space matrices, the H₂ model reduction error is computed via the reachability or observability gramian. Necessary conditions for the reduced order bilinear models to be H₂ optimal are given. The gradient flow approach is used to obtain the solution of the H₂ model reduction problem. The formulation allows certain properties of the original models to be preserved in the reduced order models. The model reduction procedure developed can also be applied to finite-dimensional linear time-invariant systems. A numerical example is employed to illustrate the effectiveness of the proposed method.     

Optimal time-weighted H 2 model reduction for Markovian jump systems

This article addresses the optimal time-weighted H ₂ model reduction problem for Markovian jump linear systems. That is, for a given mean square stable Markovian jump system, our aim is to find a mean square stable jump system of lower order such that the time-weighted H ₂ norm of the corresponding error system is minimised. The time-weighted H ₂ norm of the system is first defined, and then a computational method is constructed. The computation requires the solution of two sets of recursive Lyapunov-type linear matrix equations associated with the Markovian jump system. To solve the optimal time-weighted H ₂ model reduction problem, we propose a gradient flow method for its solution. A necessary condition for minimality is derived, and a computational procedure is provided to obtain the minimising reduced-order model. The necessary condition generalises the standard result for systems when Markov jumps and the time-weighting term do not appear. Finally, two numerical examples are given to demonstrate the effectiveness of the proposed approach.     

Stabilisation of infinite-dimensional bilinear systems using a quadratic feedback control

This article discusses feedback stabilisation of bilinear systems defined on a Hilbert state space. We show that stabilising such a system reduces stabilising only its projection on a suitable subspace. Then we give a new stabilising control that minimises a quadratic cost and allows the decay estimate of the optimal trajectories. An illustrating example is presented.     

H∞ model reduction of Markovian jump linear systems

In this paper, the H_∞ model reduction problem for linear systems that possess randomly jumping parameters is studied. The development includes both the continuous and discrete cases. It is shown that the reduced order models exist if a set of matrix inequalities is feasible. An effective iterative algorithm involving linear matrix inequalities is suggested to solve the matrix inequalities characterizing the model reduction solutions. Using the numerical solutions of the matrix inequalities, the reduced order models can be obtained. An example is given to illustrate the proposed model reduction method.     

Regional controllability for infinite-dimensional bilinear systems: approach and simulations

In this study, we consider a regional controllability problem for a class of distributed bilinear systems evolving in a spatial domain Ω. A feedback control is used to steer the system state close to a desired profile at a final time T, only on ω a subregion of the system domain which may be interior or on the boundary of Ω. Our purpose is to prove that an optimal control exists, and characterised as a solution to an optimality system. Numerical algorithm is given and successfully illustrated by simulations.     

Internal positivity preserved model reduction

This article studies model reduction of continuous-time stable positive linear systems under the Hankel norm, H _∞ norm and H ₂ norm performance. The reduced-order systems preserve the stability as well as the positivity of the original systems. This is achieved by developing new necessary and sufficient conditions of the model reduction performances in which the Lyapunov matrices are decoupled with the system matrices. In this way, the positivity constraints in the reduced-order model can be imposed in a natural way. As the model reduction performances are expressed in linear matrix inequalities with equality constraints, the desired reduced-order positive models can be obtained by using the cone complementarity linearisation iterative algorithm. A numerical example is presented to illustrate the effectiveness of the given methods.     

On controllability of discrete-time bilinear systems by near-controllability

In this paper, controllability of discrete-time bilinear systems is studied. By applying a recent result on near-controllability, a new sufficient condition for controllability of the systems is presented, where controllability is proved by approximation with near-controllability. The new condition is algebraically verifiable and is hence easy to apply compared with a classical result on controllability of discrete-time bilinear systems, which can be effective even when the classical result does not work. Furthermore, the control inputs to achieve the transition of the systems between any given pair of states are approximately computable according to near-controllability. Therefore, near-controllability can be used to not only better characterize the system properties, but also prove controllability with computable control inputs. The new condition is then generalized to derive similar results on controllability and near-controllability of the systems. Finally, examples are given to illustrate the results of this paper.     

On controllability of two-dimensional discrete-time bilinear systems

In this paper, controllability of two-dimensional discrete-time bilinear systems is studied. Sufficient conditions for the systems to be controllable are presented. In particular, the control inputs are computed to achieve the state transition for the controllable systems by using the root locus technique. Examples are also provided to illustrate the results of the paper.     

On the regional control problem of the bilinear wave equation

The present research deals with regional optimal control problem of the bilinear wave equation evolving on a spatial domain $\mathrm{\Omega }\subset {\mathrm{ℝ}}^n,n\ge 1$. Such an equation is excited by bounded controls that act on the velocity term. It addresses the tracking of a desired state all over the time interval $[0,T]$ only on a subregion $\omega$ of $\mathrm{\Omega }$ with minimum energy. Then, we prove that an optimal control exists and is characterized as a solution to an optimality system. Algorithm for the computation of such a control is given and successfully illustrated through simulations.     

Optimal control of weakly coupled bilinear systems

The optimization of the time-invariant bilinear weakly coupled system with a quadratic performance criterion is considered. A sequence of linear state and costate equations is constructed such that the open-loop solution of the optimization problem is obtained in terms of the reduced-order subsystems. This leads to a reduction in the size of the required computations and allows parallel processing of information. The near-optimal closed-loop control is obtained in the form of a linear feedback law, with the feedback gains calculated from two reduced-order independent time-varying linear-quadratic optimal control problems.