Large-scale Linear Dynamical Systems Research Articles

Order Reduction of Large-Scale Linear Dynamic Systems Using Balanced Truncation with Modified Cauer Continued Fraction

This article discusses a new technique to reduce the order of high-dimensional, continuous, linear time-invariant dynamic systems. The proposed approach utilizes the merits of the balanced truncation method, and modified Cauer continued fraction expansion. The reduced model denominator was retrieved via the concept of Lyapunov balancing, and state truncation and numerator coefficients were obtained by using a modified Cauer continued fraction. The proposed technique circumvents the steady-state gain discrepancy and steady-state error mismatch of the conventional balanced truncation method, and the instability issue of continued fraction expansion. The proposed method assures steady-state gain approximation, few moments, Markov parameters, stability, and other essential control features of a high-order system. The efficacy and applicability of the proposed technique are demonstrated using some typical test systems adopted from the literature. This method gives improved performance indices like minimum H∞ norm, H2 norm, a minimum ISE (integral squared error), IAE (integral absolute error), and retains approximately the full IRE (impulse response energy) of the existing system in the reduced model. The simulation findings of the proposed technique are compared with other current model reduction methods in recent literature using MATLAB.

Algebraic Decomposition of Model Predictive Control Problems.

This paper is concerned with the application of model predictive control (MPC) to large-scale linear dynamical systems with linear inequality constraints. A decomposition is proposed of such problems into sets of independent MPCs of lower dimensions that preserves all information about the system, cost function, and constraints. Different from previous work, the constraints are incorporated in the decomposition procedure, which is attained by generalizing a previously developed technique to simultaneously block diagonalize a set of matrices. This approach is applied to practical examples involving large-scale systems with inequality constraints. It is shown that the computational complexity and the CPU time required to solve the transformed MPC problems are lower than those required by the solution of the original MPC problem.

Exact Decomposition of Optimal Control Problems via Simultaneous Block Diagonalization of Matrices.

In this paper, we consider optimal control problems (OCPs) applied to large-scale linear dynamical systems with a large number of states and inputs. We attempt to reduce such problems into a set of independent OCPs of lower dimensions. Our decomposition is 'exact' in the sense that it preserves all the information about the original system and the objective function. Previous work in this area has focused on strategies that exploit symmetries of the underlying system and of the objective function. Here, instead, we implement the algebraic method of simultaneous block diagonalization of matrices (SBD), which we show provides advantages both in terms of the dimension of the subproblems that are obtained and of the computation time. We provide practical examples with networked systems that demonstrate the benefits of applying the SBD decomposition over the decomposition method based on group symmetries.

A framework for randomized time-splitting in linear-quadratic optimal control

Inspired by the successes of stochastic algorithms in the training of deep neural networks and the simulation of interacting particle systems, we propose and analyze a framework for randomized time-splitting in linear-quadratic optimal control. In our proposed framework, the linear dynamics of the original problem is replaced by a randomized dynamics. To obtain the randomized dynamics, the system matrix is split into simpler submatrices and the time interval of interest is split into subintervals. The randomized dynamics is then found by selecting randomly one or more submatrices in each subinterval. We show that the dynamics, the minimal values of the cost functional, and the optimal control obtained with the proposed randomized time-splitting method converge in expectation to their analogues in the original problem when the time grid is refined. The derived convergence rates are validated in several numerical experiments. Our numerical results also indicate that the proposed method can lead to a reduction in computational cost for the simulation and optimal control of large-scale linear dynamical systems.

Conventional and Evolutionary Order Reduction Techniques for Complex Systems

Order reduction of the large-scale linear dynamic systems (LSLDSs) using stability equation technique mixed with the conventional and evolutionary techniques is presented in the paper. The reduced system (RS) is obtained by mixing the advantages of the two methods. For the conventional technique, the numerator of the RS is achieved by using the Pade approximations and improved Pade approximations, whereas the denominator is obtained by the stability equation technique (SET). For the evolutionary technique, numerator of the RS is obtained by minimizing the integral square error (ISE) between transient responses of the original and the RS using the genetic algorithm (GA), and the denominator is obtained by the stability equation method. The proposed RS retains almost all the essential properties of the original system (OS). The viability of the proposed technique is proved by comparing its time, frequency responses, time domain specifications, and ISE with the new and popular methods available in the literature.

Efficient Space–Time Reduced Order Model for Linear Dynamical Systems in Python Using Less than 120 Lines of Code

A classical reduced order model (ROM) for dynamical problems typically involves only the spatial reduction of a given problem. Recently, a novel space–time ROM for linear dynamical problems has been developed [Choi et al., Space–tume reduced order model for large-scale linear dynamical systems with application to Boltzmann transport problems, Journal of Computational Physics, 2020], which further reduces the problem size by introducing a temporal reduction in addition to a spatial reduction without much loss in accuracy. The authors show an order of a thousand speed-up with a relative error of less than 10−5 for a large-scale Boltzmann transport problem. In this work, we present for the first time the derivation of the space–time least-squares Petrov–Galerkin (LSPG) projection for linear dynamical systems and its corresponding block structures. Utilizing these block structures, we demonstrate the ease of construction of the space–time ROM method with two model problems: 2D diffusion and 2D convection diffusion, with and without a linear source term. For each problem, we demonstrate the entire process of generating the full order model (FOM) data, constructing the space–time ROM, and predicting the reduced-order solutions, all in less than 120 lines of Python code. We compare our LSPG method with the traditional Galerkin method and show that the space–time ROMs can achieve O(10−3) to O(10−4) relative errors for these problems. Depending on parameter–separability, online speed-ups may or may not be achieved. For the FOMs with parameter–separability, the space–time ROMs can achieve O(10) online speed-ups. Finally, we present an error analysis for the space–time LSPG projection and derive an error bound, which shows an improvement compared to traditional spatial Galerkin ROM methods.

Renovation in the Modified Pole Clustering Technique for the Linear Dynamic Systems

The authors propose a new mixed order reduction technique for large-scale linear dynamic systems (LSLDSs). The denominator of the original LSLDS is reduced by the proposed renovated pole clustering technique, whereas the reduced numerator is obtained by using the Pade approximations technique. The proposed technique is an improved form of the modified pole clustering technique suggested by Vishwakarma. It is well known that the low value of pole cluster centres, provides better matching of original LSLDS with low order reduced system (LORS). So by small modifications in the modified pole clustering technique, provides a lower value of pole cluster centres in comparison to all the techniques based on pole cluster. The proposed technique guarantees the preservation of stability and steady-state value of the Original LSLDS into a LORS with a deficient value of integral square error (ISE). Its supremacy is justified by taking two LSLDSs from the literature. The time and frequency responses of the proposed LORS are obtained by using MATLAB/Simulink and compared with other popular reduction techniques from the literature. The step time qualitative properties and performance index (ISE) of the proposed LORS is also compared with a few recent famous techniques which support the goodness and simplicity of the proposed technique. The proposed technique is also applicable to multi-input multi-output (MIMO) LSLDSs.

Order Abatement of Linear Dynamic Systems Using Renovated Pole Clustering and Cauer Second Form Techniques

A mixed-method for the order abatement of large-scale linear dynamic systems (LSLDSs) into desired low order systems is proposed. The denominator of the abated system (AS) is obtained by the renovated pole clustering technique, while the numerator is obtained by Cauer second form technique. The AS is stable if the original system (OS) is stable and retains all the essential time and frequency response specifications of the OS. The proposed mixed method is an improved version of the modified pole clustering technique. As we know, the magnitude of the pole cluster center plays an important role in the clustering technique for the abatement of LSLDS. Less magnitude of pole cluster center, better approximations matching of AS with the OS has occurred. The magnitude of dominated pole cluster centers obtained by the modified pole clustering technique or other pole clustering techniques is large compared to the proposed technique. Hence the proposed technique provides better approximations matching between OS and AS during the transient period compared to other clustering techniques, which supports the replacement of OS with better AS. The efficacy of the proposed technique is verified by taking three numerical examples and compared with other recent and famous techniques available in the literature.

Space–time reduced order model for large-scale linear dynamical systems with application to Boltzmann transport problems

A classical reduced order model for dynamical problems involves spatial reduction of the problem size. However, temporal reduction accompanied by the spatial reduction can further reduce the problem size without losing much accuracy, which results in a considerably more speed-up than the spatial reduction only. Recently, a novel space–time reduced order model for dynamical problems has been developed [17], where the space–time reduced order model shows an order of a hundred speed-up with a relative error of 10−4 for small academic problems. However, in order for the method to be applicable to a large-scale problem, an efficient space–time reduced basis construction algorithm needs to be developed. We present the incremental space–time reduced basis construction algorithm. The incremental algorithm is fully parallel and scalable. Additionally, the block structure in the space–time reduced basis is exploited, which enables the avoidance of constructing the reduced space–time basis. These novel techniques are applied to a large-scale particle transport simulation with million and billion degrees of freedom. The numerical example shows that the algorithm is scalable and practical. Also, it achieves a tremendous speed-up, maintaining a good accuracy. Finally, error bounds for space-only and space–time reduced order models are derived.

$\mathcal{H}_2$-Optimal Model Reduction Using Projected Nonlinear Least Squares

In many applications throughout science and engineering, model reduction plays an important role replacing expensive large-scale linear dynamical systems by inexpensive reduced order models that capture key features of the original, full order model. One approach to model reduction finds reduced order models that are locally optimal approximations in the $\mathcal{H}_2$ norm, an approach taken by the Iterative Rational Krylov Algorithm (IRKA) among others. Here we introduce a new approach for $\mathcal{H}_2$-optimal model reduction using the projected nonlinear least squares framework previously introduced in [J. M. Hokanson, SIAM J. Sci. Comput. 39 (2017), pp. A3107--A3128]. At each iteration, we project the $\mathcal{H}_2$ optimization problem onto a finite-dimensional subspace yielding a weighted least squares rational approximation problem. Subsequent iterations append this subspace such that the least squares rational approximant asymptotically satisfies the first order necessary conditions of the original, $\mathcal{H}_2$ optimization problem. This enables us to build reduced order models with similar error in the $\mathcal{H}_2$ norm but using far fewer evaluations of the expensive, full order model compared to competing methods. Moreover, our new algorithm only requires access to the transfer function of the full order model, unlike IRKA which requires a state-space representation or TF-IRKA which requires both the transfer function and its derivative. Applying the projected nonlinear least squares framework to the $\mathcal{H}_2$-optimal model reduction problem open new avenues for related model reduction problems.

Trajectory Tracking Control Design for Large-Scale Linear Dynamical Systems With Applications to Soft Robotics

This article presents new results to control process modeled through linear large-scale systems. Numerical methods are widely used to model physical systems, and the finite-element method is one of the most common methods. However, for this method to be precise, it requires a precise spatial mesh of the process. Large-scale dynamical systems arise from this spatial discretization. We propose a methodology to design an observer-based output feedback controller. First, a model reduction step is used to get a system of acceptable dimension. Based on this low-order system, two linear matrix inequality problems provide us, respectively, with the observer and controller gains. In both the cases, model and reduction errors are taken into account in the computations. This provides robustness with respect to the reduction step and guarantees the stability of the original large-scale system. Finally, the proposed method is applied to a physical setup-a soft robotics platform-to show its feasibility.

Unstable modes in projection-based reduced-order models: How many can there be, and what do they tell you?

Projection methods provide an appealing way to construct reduced-order models of large-scale linear dynamical systems: they are intuitively motivated and fairly easy to compute. Unfortunately, the resulting reduced models need not inherit the stability of the original system. How many unstable modes can these reduced models have? This note investigates this question, using theory originally motivated by iterative methods for linear algebraic systems and eigenvalue problems, and illustrating the theory with a number of small examples. From these results follow rigorous upper bounds on the number of unstable modes in reduced models generated via orthogonal projection, for both continuous- and discrete-time systems. Can anything be learned from the unstable modes in reduced-order models? Several examples illustrate how such instability can helpfully signal transient growth in the original system.

Structure-Preserving-Based Model-Order Reduction of Parameterized Interconnect Systems

In recent years, the continued miniaturization of VLSI circuits leads to the need for more efficient simulation of large-scale linear dynamical systems with ever-increasing state-space dimension. The linear dynamical systems in VLSI circuit simulation are RC or RCL models of the VLSI circuit’s interconnected system. Model-order reduction is an important technique to reduce such a high complexity. Along the technology scaling, the indetermination in the manufacturing process causes variations in the critical dimensions and interlevel dielectric thickness of interconnects, which could make the system performance unpredictable and produce a significant parametric yield loss. An efficient exploitation of these design activities results in a parameterized model-order reduction technique able to reduce large systems of equations with respect to all of the variable parameters of the circuit, (i.e., resistance, capacitance, and inductance). For this purpose, we propose a novel parameterized model-order reduction method for interconnect variation systems, which is based on an efficient and reliable combination of invariable structure-preserving model-order reduction (SPMOR) methods and variable parameter retaining schemes. It is referred to as a parameterized interconnect model-order reduction via structure-preserving algorithm. The SPMOR model yields the same form of the original state equation and preserves the passivity of the parameterized RC and RLC networks, like the well-known passive reduced-order interconnect algorithm for nonparameterized RC and RLC networks. The most important benefit it entails is the ability to preserve the probability characteristic of the original interconnect systems. Pertinent numerical examples are proposed to validate the proposed SPMOR approach.

Minimum cost input/output design for large-scale linear structural systems

In this paper, we provide optimal solutions to two different (but related) input/output design problems involving large-scale linear dynamical systems, where the cost associated to each directly actuated/measured state variable can take different values, but is independent of the input/output performing the task. Under these conditions, we first aim to determine and characterize the input/output placement that incurs in the minimum cost while ensuring that the resulting placement achieves structural controllability/observability. Further, we address a constrained variant of the above problem, in which we seek to determine the minimum cost placement configuration, among all possible input/output placement configurations that ensures structural controllability/observability, with the lowest number of directly actuated/measured state variables. We develop new graph-theoretical characterizations of cost-constrained input selections for structural controllability and properties that enable us to address both problems by reduction to a weighted maximum matching problem — efficiently addressed by algorithms with polynomial time complexity (in the number of state variables). Finally, we illustrate the obtained results with an example.

Joint Association Graph Screening and Decomposition for Large-Scale Linear Dynamical Systems

This paper studies large-scale dynamical networks where the current state of the system is a linear transformation of the previous state, contaminated by a multivariate Gaussian noise. Examples include stock markets, human brains, and gene regulatory networks. We introduce a transition matrix to describe the evolution, which can be translated to a directed Granger transition graph, and use the concentration matrix of the Gaussian noise to capture the second-order relations between nodes, which can be translated to an undirected conditional dependence graph. We propose regularizing the two graphs jointly in topology identification and dynamics estimation. Based on the notion of joint association graph (JAG), we develop a joint graphical screening and estimation (JGSE) framework for efficient network learning in big data. In particular, our method can predetermine and remove unnecessary edges based on the joint graphical structure, referred to as JAG screening, and can decompose a large network into smaller subnetworks in a robust manner, referred to as JAG decomposition. JAG screening and decomposition can reduce the problem size and search space for fine estimation at a later stage. Experiments on both synthetic data and real-world applications show the effectiveness of the proposed framework in large-scale network topology identification and dynamics estimation.

Pairwise control principle in large-scale systems

Pairwise control principle in large-scale systems The purpose of the paper is present an algorithm of partially decentralized control design for one type of large-scale linear dynamical system. The pairwise autonomous principle is preferred where design conditions are derived in the bounded real lemma form, and global system stability is reproven to formulate potential application principle in fault tolerant control. The validity of the proposed method is demonstrated by the numerical example.

Algebraic Condition for Decomposition of Large-Scale Linear Dynamic Systems

Algebraic Condition for Decomposition of Large-Scale Linear Dynamic SystemsThe paper concerns the problem of decomposition of a large-scale linear dynamic system into two subsystems. An equivalent problem is to split the characteristic polynomial of the original system into two polynomials of lower degrees. Conditions are found concerning the coefficients of the original polynomial which must be fulfilled for its factorization. It is proved that knowledge of only one of the symmetric functions of those polynomials of lower degrees is sufficient for factorization of the characteristic polynomial of the original large-scale system. An algorithm for finding all the coefficients of the decomposed polynomials and a general condition of factorization are given. Examples of splitting the polynomials of the fifth and sixth degrees are discussed in detail.

On Padé-type model order reduction of J-Hermitian linear dynamical systems

A simple, yet powerful approach to model order reduction of large-scale linear dynamical systems is to employ projection onto block Krylov subspaces. The transfer functions of the resulting reduced-order models of such projection methods can be characterized as Padé-type approximants of the transfer function of the original large-scale system. If the original system exhibits certain symmetries, then the reduced-order models are considerably more accurate than the theory for general systems predicts. In this paper, the framework of J-Hermitian linear dynamical systems is used to establish a general result about this higher accuracy. In particular, it is shown that in the case of J-Hermitian linear dynamical systems, the reduced-order transfer functions match twice as many Taylor coefficients of the original transfer function as in the general case. An application to the SPRIM algorithm for order reduction of general RCL electrical networks is discussed.

Decentralized iterative learning control methods for large scale linear dynamic systems

Decentralized iterative learning control methods are presented for a class of large scale interconnected linear dynamic systems, in which an iterative learning controller in each subsystem operates on its local subsystem exclusively with no exchange of information between subsystems. Sufficient conditions for convergence of the algorithms are given and numerical examples are illustrated to show the validity of the algorithms. In particular, the algorithms are useful for systems having large uncertainty of interconnected terms.

Algebraic Conditions for the Decomposition of Large-Scale Linear Dynamic Systems

The paper concern the problem of the decomposition large scale linear dynamic system into two-subsystems. The equivalent problem is to split the characteristic polynomial of the original system into two polynomials of lower degrees. There are founded the conditions concerning the coefficients of the original polynomial which must be fullfilled for its factorization. It is proved that knowledge only one of the symetric functions of the polynomial of lower degree is satisfactory for factorization the characteristic polynomial of the original large scale system. The algorithm for finding all the coefficients of the lower degree polynomials are given, and the general condition of factorization in the shape of resultant. The example of spliting the polynomial of 5 degree is considered with all details.