Parametric regulation of economic growth based on nonautonomous computable general equilibrium models

This paper provides some results of generalizing the theory of parametric regulation to the classes of nonautonomous continuous- and discrete-time dynamic systems. We present assertions on the existence of solutions to a series of variational calculus problems and on the continuous dependence of performance criteria on uncontrolled functions. The computable general equilibrium model (CGE model) of national economy sectors serves for illustrating the efficiency of the proposed parameter identification method for high-dimensional mathematical models. And finally, the CGE model of national economy sectors is used to analyze the sources of economic growth and to demonstrate the efficiency of the theory of parametric regulation for elaboration of government’s policy in the field of economic growth.     

Identification of a class of continuous time-varying linear systems via block pulse functions

Starting from the relations between entries of block pulse operational matrices, block pulse regression equations corresponding to the original differential equation models with time-varying parameters are obtained. Based on simple forms of the regression equations, algorithms developed in discrete-time model identification can be applied directly to estimate the time-varying parameters of the continuous models without much modification. Compared with other identification methods for the same problem, this new method is simple, and can be applied to both batch and recursive estimations of continuous time-varying linear systems from their sampled input and output data by means of digital computers.     

Dynamic SISO and MISO system approximations based on optimalLaguerre models

A general procedure, based on the knowledge of the input and the output signals, is proposed to approximate the prescribed linear time-invariant (LTI) systems by means of optimal Laguerre models. The main contribution of this paper is to apply the Newton Raphson's iterative technique to compute the so-called optimal Laguerre pole in a continuous time case (or optimal time scale factor in a discrete-time case) and especially to show that the gradient and the Hessian can be expressed analytically. Moreover, the excitations used are not limited to the ones that ensure the orthogonality of the outputs of Laguerre filters (i.e., Dirac delta or white noise) as is usually done in existing methods, however persistently exciting input signal(s) are used. The proposed procedure, will be directly formulated for SISO systems, SISO systems being a special case with the number of inputs equal to one. The proposed algorithm has direct applications in system identification, model reduction, and noisy modeling     

Realization of Petrinets: a system theoretic approach

Petrinets have become a useful tool for modelling various classes of systems, especially those involving parallel computations and concurrent processes. The present paper deals with a method for identification of the Petrinet model from a given set of input-output observations. The method actually involves a modification of the system identification techniques of linear system theory. It has been shown that, by assuming the net to be a discrete-time linear dynamical system, it is possible to arrive at a reduced-order Petrinet model for a dynamical process. This procedure will enable one to obtain a Petrinet model for subsequent analysis systematically, thus bypassing normally used trial-and-error techniques.     

A new kernel-based approach for linear system identification

This paper describes a new kernel-based approach for linear system identification of stable systems. We model the impulse response as the realization of a Gaussian process whose statistics, differently from previously adopted priors, include information not only on smoothness but also on BIBO-stability. The associated autocovariance defines what we call a stable spline kernel. The corresponding minimum variance estimate belongs to a reproducing kernel Hilbert space which is spectrally characterized. Compared to parametric identification techniques, the impulse response of the system is searched for within an infinite-dimensional space, dense in the space of continuous functions. Overparametrization is avoided by tuning few hyperparameters via marginal likelihood maximization. The proposed approach may prove particularly useful in the context of robust identification in order to obtain reduced order models by exploiting a two-step procedure that projects the nonparametric estimate onto the space of nominal models. The continuous-time derivation immediately extends to the discrete-time case. On several continuous- and discrete-time benchmarks taken from the literature the proposed approach compares very favorably with the existing parametric and nonparametric techniques.     

Robust multi-parametric model predictive control for LPV systems with application to anaesthesia

We present a multi-parametric model predictive controller (mpMPC) for discrete-time linear parameter-varying (LPV) systems based on the solution of the mpMPC problem for discrete-time linear time-invariant (LTI) systems. The control method yields a controller that adapts to parameter changes of the LPV system. This is accomplished by an add-on unit to the implementation of the mpMPC for LTI systems. No modification of the optimal mpMPC solution for LTI systems is needed. The mpMPC for LPV systems is entirely based on simple computational steps performed on-line. This control design method could improve the performance and robustness of a mpMPC for LPV systems with slowly varying parameters. We apply this method to process systems which suffer from slow variation of system parameters due, for example, to aging or degradation. As an illustrative example the reference tracking control problem of the hypnotic depth during intravenous anaesthesia is presented: the time varying system matrix mimics an external disturbance on the hypnotic depth. In this example the presented mpMPC for LPV systems shows a reduction of approximately 60% of the reference tracking error compared to the mpMPC for LTI systems.     

离散时间高阶不确定线性多个体系统保性能一致性分析

本文研究了存在参数不确定性的离散时间高阶多个体系统保性能一致性问题,给出了一种设计其线性一致性协议的方法.首先,通过模型转换的方法将该问题转换为一组离散时间不确定系统的稳定性问题;然后,构造合适的Lyapunov函数并利用离散时间系统稳定性理论,推导出一个使离散时间高阶不确定多个体系统获得保性能一致的LMI充分条件;接着,以一致性序列的形式给出参数不确定条件下的离散时间高阶多个体系统的一致性收敛结果.最后,参数不确定的对比数值仿真验证了本文理论的正确性和有效性.     

Resource-constrained project scheduling with flexible resource profiles in continuous time

This paper addresses the resource-constrained project scheduling problem with flexible resource profiles (FRCPSP) in continuous time. In contrast to the discrete-time system, each task may start, end, or change its resource allocation at any point in time. The additional decisions for the continuous times of these events greatly amplify the problem complexity. We propose a mixed-integer linear programming model together with problem-specific inequalities and heuristic time limits, both of which are applied in the branch-and-cut procedure. In addition, the fractional period-width preprocessing and heuristic as well as the event estimation method are proposed to estimate the time and event parameters. Through the computational results, we investigate the pros and cons of the continuous-time model against the discrete-time counterpart both in terms of solution quality and runtimes, as well as the effectiveness of the preprocessing and different solution procedures.     

Identification of non-linear processes using reciprocal multiquadric functions

In this paper radial basis function (RBF) networks are used to model general non-linear discrete-time systems. In particular, reciprocal multiquadric functions are used as activation functions for the RBF networks. A stepwise regression algorithm based on orthogonalization and a series of statistical tests is employed for designing and training of the network. The identification method yields non-linear models, which are stable and linear in the model parameters. The advantages of the proposed method compared to other radial basis function methods and backpropagation neural networks are described. Finally, the effectiveness of the identification method is demonstrated by the identification of two non-linear chemical processes, a simulated continuous stirred tank reactor and an experimental pH neutralization process.     

The explicit linear quadratic regulator for constrained systems

For discrete-time linear time invariant systems with constraints on inputs and states, we develop an algorithm to determine explicitly, the state feedback control law which minimizes a quadratic performance criterion. We show that the control law is piece-wise linear and continuous for both the finite horizon problem (model predictive control) and the usual infinite time measure (constrained linear quadratic regulation). Thus, the on-line control computation reduces to the simple evaluation of an explicitly defined piecewise linear function. By computing the inherent underlying controller structure, we also solve the equivalent of the Hamilton-Jacobi-Bellman equation for discrete-time linear constrained systems. Control based on on-line optimization has long been recognized as a superior alternative for constrained systems. The technique proposed in this paper is attractive for a wide range of practical problems where the computational complexity of on-line optimization is prohibitive. It also provides an insight into the structure underlying optimization-based controllers.     

Realization theory of discrete-time linear switched systems

The paper presents realization theory of discrete-time linear switched systems. We present necessary and sufficient conditions for an input–output map to admit a discrete-time linear switched system realization. In addition, we present a characterization of minimality of discrete-time linear switched systems in terms of reachability and observability. Further, we prove that minimal realizations are unique up to isomorphism. We also discuss algorithms for converting a linear switched system to a minimal one and for constructing a state-space representation from input–output data. The paper uses the theory of rational formal power series in non-commutative variables.     

A Galerkin discretisation-based identification for parameters in nonlinear mechanical systems

In the paper, a new parameter identification method is proposed for mechanical systems. Based on the idea of Galerkin finite-element method, the displacement over time history is approximated by piecewise linear functions, and the second-order terms in model equation are eliminated by integrating by parts. In this way, the lost function of integration form is derived. Being different with the existing methods, the lost function actually is a quadratic sum of integration over the whole time history. Then for linear or nonlinear systems, the optimisation of the lost function can be applied with traditional least-squares algorithm or the iterative one, respectively. Such method could be used to effectively identify parameters in linear and arbitrary nonlinear mechanical systems. Simulation results show that even under the condition of sparse data or low sampling frequency, this method could still guarantee high accuracy in identifying linear and nonlinear parameters.     

Optimal digital simulations for random linear systems with integration constraints

A generalized approach involving concepts from optimization theory is developed for realizing optimal digital simulations for linear, time-varying, continuous dynamical systems having random inputs by modifying discrete input signal variances. The minimization of a cost functional based on the state covariance matrices of the continuous system and its discrete model leads to a two-point boundary value problem which can be solved by known numerical techniques. The result is a systematic procedure for determining optimal digital simulations under the constraints that the numerical integration formula and integration step size have been specified in advance. An example is presented to illustrate the procedure, including a verification using Monte Carlo simulation runs.     

基于H表示的时变随机Markov 跳跃系统的能观性

研究时变连续和离散随机Markov 跳跃系统(SMJSs) 的能观性问题. 基于H-表示方法将时变SMJSs 转化为等价的时变线性系统, 根据线性系统理论得到时变连续和离SMJSs 的能观性Gramian 矩阵判据. 数值仿真表明了所得结论的正确性.     

A second-order maximum principle for discrete-time bilinear control systems with applications to discrete-time linear switched systems

A powerful approach for analyzing the stability of continuous-time switched systems is based on using optimal control theory to characterize the “most unstable” switching law. This reduces the problem of determining stability under arbitrary switching to analyzing stability for the specific “most unstable” switching law. For discrete-time switched systems, the variational approach received considerably less attention. This approach is based on using a first-order necessary optimality condition in the form of a maximum principle (MP), and typically this is not enough to completely characterize the “most unstable” switching law. In this paper, we provide a simple and self-contained derivation of a second-order necessary optimality condition for discrete-time bilinear control systems. This provides new information that cannot be derived using the first-order MP. We demonstrate several applications of this second-order MP to the stability analysis of discrete-time linear switched systems.     

Reduced-order modelling of linear discrete-time systems via continued-fraction expansion of a z transfer function about z = 1 and z = a alternately

A new time-domain procedure is suggested for obtaining reduced-order models of linear time-invariant discrete-time systems. The procedure is based on presenting a new form of continued-fraction expansion (CFE) about z = 1 and z = a alternately, and deriving a realization form for the CFE. An algorithm is presented for obtaining the new CFE of the z transfer function of a linear discrete-time system from its state-space model directly, without having to determine the corresponding rational z transfer function. Also presented is a systematic approach to deriving two similarity transformation matrices: one is used to transform a state-space model from a general form to the CFE canonical form, and the other is used to transform a state-space model from the phase-variable canonical form to the CFE canonical form. Finally, an approximate aggregation matrix is constructed to relate the state vector of the original system to that of a reduced model obtained by the present method. The proposed procedure is illustrated with examples.     

Lazy learning for local modelling and control design

This paper presents local methods for modelling and control of discrete-time unknown non-linear dynamical systems, when only input-output data are available. We propose the adoption of lazy learning, a memory-based technique for local modelling. The modelling procedure uses a query-based approach to select the best model configuration by assessing and comparing different alternatives. A new recursive technique for local model identification and validation is presented, together with an enhanced statistical method for model selection. A lso, three methods to design controllers based on the local linearization provided by the lazy learning algorithm are described. In the first method the lazy technique returns the forward and inverse models of the system which are used to compute the control action to take. The second is an indirect method inspired by self-tuning regulators where recursive least squares estimation is replaced by a local approximator. The third method combines the linearization provided by the local learning techniques with optimal linear control theory, to control non-linear systems about regimes which are far from the equilibrium points. Simulation examples of identification and control of non-linear systems starting from observed data are given.     

一类非线性离散时间系统的模糊辨识

对一类非线性离散时间系统提出了模糊辨识方法,此方法用与未知参数向量成线性关系的模糊逻辑系统作为辨识模型,并通过自适应学习律对此模糊逻辑系统中的未知参数进行自适应调节,文中证明了此方法可使辨识误差收敛到原点的一个邻域内。仿真结果验证了此方法的有效性。     

广义卡尔曼-布西滤波算法识别系统参数

根据结构力学与卡尔曼滤波相模拟的理论,构造了一种新的用于连续系统参数识别的广义卡尔曼—布西滤波计算格式．该算法运用了结构力学中的串联子结构拼装方法,在每一步子结构拼装的同时嵌入对系统状态和参数的估计以实现系统参数的识别,可以离线计算的数据都通过精细积分算法预先获得。