Control-relevant Parameter Estimation Research Articles

A Real-Time Nonlinear CRPE Predictive PHEV Energy Management System Design and HIL Evaluation

The explicit nonlinear model predictive control (eNMPC) approach is a challenging yet encouraging prospective to design model-based and real-time controllers for complicated automotive applications. The precision of the nonlinear control-oriented model may contribute to the superior performance of the controller but adversely affect the real-time capability and even the convergence of optimization solution involved in such an approach. To address this challenge, control-relevant parameter estimation (CRPE) is used to ameliorate the capability of the nonlinear control-oriented model to exploit the cardinal dynamics of a complex plug-in hybrid powertrain. As such, an eNMPC energy management system is developed to be implemented in to an experimental hardware with fettered memory and computational capabilities. Hardware-in-the-Loop (HIL) simulation, which is conducted on a dSPACE MicroAutoBox unit along with sophisticated high-fidelity Autonomie model shows a superior performance of the proposed control scheme for various driving scenarios while maintaining real-time capability.

A control-relevant approach to demand modeling for supply chain management

The development of control-oriented decision policies for inventory management in supply chains has drawn considerable interest in recent years. Modeling demand to supply forecasts is an important component of an effective solution to this problem. Drawing from the problem of control-relevant parameter estimation, this paper presents an approach for demand modeling in a production-inventory system that relies on a specialized weight to tailor the emphasis of the fit to the intended purpose of the model, which is to provide forecasts to inventory management policies based on internal model control or model predictive control. A systematic approach to generate this weight function (implemented using data prefilters in the time domain) is presented and the benefits demonstrated on a series of representative case studies. The multi-objective formulation developed in this work allows the user to emphasize minimizing inventory variance, minimizing starts variance, or their combination, as dictated by operational and enterprise goals.

CR-IDENT: A MATLAB TOOLBOX FOR MULTIVARIABLE CONTROL-RELEVANT SYSTEM IDENTIFICATION

This paper describes CR-IDENT, a Matlab-based toolbox that implements a comprehensive procedure for multivariable control-relevant system identification aimed primarily at process system applications. The toolbox consists of modules for multivariable input signal design (multisine and PRBS), frequency response estimation, and control-relevant frequency response curvefitting, leading to models whose end use is the design of high-performance control systems. An important component in the implementation of this design procedure is its reliance on a priori knowledge of the system of interest to design input signals meeting both theoretical and practical user requirements. Data from identification testing using these signals is the basis for the subsequent steps of frequency-response estimation and control-relevant parameter estimation, with the final result being a discrete-time state-space model that serves as the nominal model for Model Predictive Control. A high-purity distillation column example is presented to illustrate the benefits of the toolbox, from experiment design to closed-loop control.

A Control-Relevant Multivariable System Identification Methodology Based on Orthogonal Multifrequency Input Perturbations

Abstract In this paper, we propose a MIMO system identification method utilizing an orthogonal multifrequency input perturbation which we call the “zippered᾿ Schroeder-phased signal. Guidelines for the design of a plant-friendly “zippered᾿ Schroeder-phased input are presented based on a priori knowledge of open-loop time constants and closed-loop system response requirements of the system to be identified. A frequency response estimate is then obtained by applying discrete Fourier transforms (DFTs) to the data. Control-relevant parameter estimation via a frequency-weighted curvefitting problem formulation tailored for orthogonal signal perturbations is presented. Application of the methodology is shown for the case of Model Predictive Control of a high purity binary distillation column, a nonlinear, highly interactive system.

Multivariable frequency-response curve fitting with application to control-relevant parameter estimation

A weighted least-squares method for curve fitting multivariable, discrete-time transfer-function models from a known plant frequency response is presented. The algorithm supports the use of iterative, pre/post matrix-valued weighting functions, which commonly arise in control-relevant parameter estimation problems. A challenging case study involving an ill-conditioned, 40-tray high-purity binary distillation column model is presented. The closed-loop responses from the control-relevant model are superior to those obtained from unweighted multivariable estimation methods and one element-at-a-time estimation, in spite of the fact that these latter models display a closer ‘open-loop’ fit.

Control-Relevant Input Signal Design for Multivariable System Identification: Application to High-Purity Distillation

Input signal design issues associated with a discrete-time MIMO control-relevant identification methodology are the focus of this paper. Using a priori information such as the open-loop dominant time constants and desired closed loop speed-of-response, guidelines are presented for the design variables in two periodic, deterministic inputs: the PRBS and Schroeder-phased signals. The guidelines are illustrated on the Weischedel-McAvoy high purity distillation column, which represents an ill-conditioned, highly interactive system. The case study clearly demonstrates that the sensible use of open-loop experimental design and nonparametric estimation, followed by control-relevant parameter estimation, naturally results in a low-order model description capturing the directionality information important for control.

Systematic techniques for determining modelling requirements for SISO and MIMO feedback control

Abstract This paper presents a fundamental methodology for assessing modelling requirements of SISO and MIMO linear control problems. The main result is the formulation of a control-relevant parameter estimation problem (CRPEP), which suitably captures the interplay that occurs between controller sophistication, speed and shape of the closed-loop response, and set-point/disturbance directions affecting the closed-loop system. The CRPEP is used to explain the apparent dilemma between emphasis on low-frequency, steady-state behaviour versus high-frequency, initial time behaviour in modelling for SISO feedback control. For multivariable systems, solutions to the CRPEP are presented using prefiltered estimation of MIMO ARX models (model reduction case) and a state-space frequency-weighted estimation method (system identification case). The superior performance of reduced order and model predictive controllers obtained from control-relevant models is demonstrated on a subset of the Shell heavy oil fractionator problem.

Systematic Techniques for Determining Modeling Requirements for SISO and MIMO Feedback Control Problems

This paper presents a methodology by which the issue of modeling requirements for SISO and MIMO control of linear time-invariant plants is analyzed in a fundamental manner. The main result is the formulation of a Control-Relevant Parameter Estimation Problem (CRPEP) which explictly incorporates control considerations such as set point /disturbance directions and desired closed-loop transfer functions. The CRPEP is solved via prefiltered estimation of MIMO ARX models (model reduction case) and a state-space frequency-weighted estimation method (system identification case). Reduced-order and model predictive controllers obtained from these estimation techniques are demonstrated on a subset of the Shell Heavy Oil Fractionator problem.