Showing papers on "System identification published in 2012"

Reinforcement Learning and Feedback Control: Using Natural Decision Methods to Design Optimal Adaptive Controllers

Abstract: This article describes the use of principles of reinforcement learning to design feedback controllers for discrete- and continuous-time dynamical systems that combine features of adaptive control and optimal control. Adaptive control [1], [2] and optimal control [3] represent different philosophies for designing feedback controllers. Optimal controllers are normally designed of ine by solving Hamilton JacobiBellman (HJB) equations, for example, the Riccati equation, using complete knowledge of the system dynamics. Determining optimal control policies for nonlinear systems requires the offline solution of nonlinear HJB equations, which are often difficult or impossible to solve. By contrast, adaptive controllers learn online to control unknown systems using data measured in real time along the system trajectories. Adaptive controllers are not usually designed to be optimal in the sense of minimizing user-prescribed performance functions. Indirect adaptive controllers use system identification techniques to first identify the system parameters and then use the obtained model to solve optimal design equations [1]. Adaptive controllers may satisfy certain inverse optimality conditions [4].

System Identification Methods for (Operational) Modal Analysis: Review and Comparison

Abstract: Operational modal analysis deals with the estimation of modal parameters from vibration data obtained in operational rather than laboratory conditions. This paper extensively reviews operational modal analysis approaches and related system identification methods. First, the mathematical models employed in identification are related to the equations of motion, and their modal structure is revealed. Then, strategies that are common to the vast majority of identification algorithms are discussed before detailing some powerful algorithms. The extraction and validation of modal parameter estimates and their uncertainties from the identified system models is discussed as well. Finally, different modal analysis approaches and algorithms are compared in an extensive Monte Carlo simulation study.

Photovoltaic Model Identification Using Particle Swarm Optimization With Inverse Barrier Constraint

Abstract: The photovoltaic (PV) model is used in simulation studies to validate system design such as the maximum power point tracking algorithm and microgrid system. It is often difficult to simulate a PV module characteristic under different environmental conditions due to the limited information provided by the manufacturers. In this paper, a new approach using particle swarm optimization (PSO) with inverse barrier constraint is proposed to determine the unknown PV model parameters. The proposed method has been validated with three different PV technologies and the results show that the maximum mean modeling error at maximum power point is less than 0.02% for Pmp and 0.3% for Vmp.

Model Predictive Control of Unknown Nonlinear Dynamical Systems Based on Recurrent Neural Networks

Abstract: In this paper, we present a neurodynamic approach to model predictive control (MPC) of unknown nonlinear dynamical systems based on two recurrent neural networks (RNNs). The echo state network (ESN) and simplified dual network (SDN) are adopted for system identification and dynamic optimization, respectively. First, the unknown nonlinear system is identified based on the ESN with input-output training and testing samples. Then, the resulting nonconvex optimization problem associated with nonlinear MPC is decomposed via Taylor expansion. To estimate the higher order unknown term resulted from the decomposition, an online supervised learning algorithm is developed. Next, the SDN is applied for solving the relaxed convex optimization problem to compute the optimal control actions over the predicted horizon. Simulation results are provided to demonstrate the effectiveness and characteristics of the proposed approach. The proposed RNN-based approach has many desirable properties such as global convergence and low complexity. It is shown that the RNN-based nonlinear MPC scheme is effective and potentially suitable for real-time MPC implementation in many applications.

An Overview of LPV Systems

Abstract: The framework of Linear Parameter Varying (LPV) systems concerns linear dynamical systems whose state-space representations depend on exogenous nonstationary parameters. Since its introduction by Shamma and Athans in 1988 to model gain-scheduling, the LPV paradigm has become a standard formalism in systems and controls, with many papers devoted to analysis, controller synthesis, and system identification of LPV models. This chapter reviews basic concepts and presents a representative selection of analytical approaches for LPV systems.

An Introduction to Identification

Abstract: This chapter contains sections titled: What Is Identification? Identification: A Simple Example Description of the Stochastic Behavior of Estimators Basic Steps in the Identification Process A Statistical Approach to the Estimation Problem Exercises

Robust Filtering and Smoothing with Gaussian Processes

Abstract: We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and the measurement function are described by non-parametric Gaussian process (GP) models. GPs are gaining increasing importance in signal processing, machine learning, robotics, and control for representing unknown system functions by posterior probability distributions. This modern way of system identification is more robust than finding point estimates of a parametric function representation. Our principled filtering/smoothing approach for GP dynamic systems is based on analytic moment matching in the context of the forward-backward algorithm. Our numerical evaluations demonstrate the robustness of the proposed approach in situations where other state-of-the-art Gaussian filters and smoothers can fail.

Issues in identification of control-oriented thermal models of zones in multi-zone buildings

Abstract: There is significant recent interest in applying model-based control techniques to improve the energy efficiency of buildings. This requires a predictive model of the building's thermal dynamics. Due to the complexity of the underlying physical processes, usually system identification techniques are used to identify parameters of a physics-based model. We investigate the effect of various model structures and identification techniques on the parameter estimates through a combination of analysis and experiments conducted in a commercial building. We observe that a second order model can reproduce the input-output behavior of a full-scale model (with 13 states). Even a single state model has enough predictive ability that it may be sufficient for control purposes. We also show that the application of conventional techniques to closed-loop data from buildings (that are collected during usual operation) leads to poor estimates; their inaccuracy becomes apparent only when forced-response data is used for validation where there is sufficient difference among various inputs and outputs. The results of this investigation are expected to provide guidelines on do's and don'ts in modeling and identification of buildings for control.

Hierarchical Least Squares Estimation Algorithm for Hammerstein–Wiener Systems

Abstract: This letter focuses on identification problems of a Hammerstein-Wiener system with an output error linear element embedded between two static nonlinear elements. A hierarchical least squares algorithm is presented for the Hammerstein-Wiener system by using the auxiliary model identification idea and the hierarchical identification principle. The major contributions of the present study are that the identification model is formulated by using the auxiliary model identification idea (the estimate of the unknown internal variable is replaced with the output of an auxiliary model) and that the bilinear parameter vectors in the identification model are estimated by using the hierarchical identification principle. The proposed hierarchical identification approach is computationally more efficient than the existing over-parametrization method.

Agnostic System Identification for Model-Based Reinforcement Learning

Abstract: A fundamental problem in control is to learn a model of a system from observations that is useful for controller synthesis. To provide good performance guarantees, existing methods must assume that the real system is in the class of models considered during learning. We present an iterative method with strong guarantees even in the agnostic case where the system is not in the class. In particular, we show that any no-regret online learning algorithm can be used to obtain a near-optimal policy, provided some model achieves low training error and access to a good exploration distribution. Our approach applies to both discrete and continuous domains. We demonstrate its efficacy and scalability on a challenging helicopter domain from the literature.

System identification, estimation and control for a cost effective open-source quadcopter

Abstract: This paper describes system identification, estimation and control of translational motion and heading angle for a cost effective open-source quadcopter — the MikroKopter. The dynamics of its built-in sensors, roll and pitch attitude controller, and system latencies are determined and used to design a computationally inexpensive multi-rate velocity estimator that fuses data from the built-in inertial sensors and a low-rate onboard laser range finder. Control is performed using a nested loop structure that is also computationally inexpensive and incorporates different sensors. Experimental results for the estimator and closed-loop positioning are presented and compared with ground truth from a motion capture system.

A physics-based approach to flow control using system identification

Abstract: Control of amplifier flows poses a great challenge, since the influence of environmental noise sources and measurement contamination is a crucial component in the design of models and the subsequent performance of the controller. A model-based approach that makes a priori assumptions on the noise characteristics often yields unsatisfactory results when the true noise environment is different from the assumed one. An alternative approach is proposed that consists of a data-based system-identification technique for modelling the flow; it avoids the model-based shortcomings by directly incorporating noise influences into an auto-regressive (ARMAX) design. This technique is applied to flow over a backward-facing step, a typical example of a noise-amplifier flow. Physical insight into the specifics of the flow is used to interpret and tailor the various terms of the auto-regressive model. The designed compensator shows an impressive performance as well as a remarkable robustness to increased noise levels and to off-design operating conditions. Owing to its reliance on only time-sequences of observable data, the proposed technique should be attractive in the design of control strategies directly from experimental data and should result in effective compensators that maintain performance in a realistic disturbance environment.

Singular Value Decomposition

Abstract: Digital information transmission is a growing field Emails, videos and so on are transmitting around the world on a daily basis Along the growth of using digital devises there is in some cases a great interest of keeping this information secureIn the field of signal processing a general concept is antenna transmission Free space between an antenna transmitter and a receiver is an example of a system In a rough environment such as a room with reflections and independent electrical devices there will be a lot of distortion in the system and the signal that is transmitted might, due to the system characteristics and noise be distortedSystem identification is another well-known concept in signal processing This thesis will focus on system identification in a rough environment and unknown systems It will introduce mathematical tools from the field of linear algebra and applying them in signal processing Mainly this thesis focus on a specific matrix factorization called Singular Value Decomposition (SVD) This is used to solve complicated inverses and identifying systemsThis thesis is formed and accomplished in collaboration with Combitech AB Their expertise in the field of signal processing was of great help when putting the algorithm in practice Using a well-known programming script called LabView the mathematical tools were synchronized with the instruments that were used to generate the systems and signals

Quantitative prediction of residual stress and hardness in case-hardened steel based on the Barkhausen noise measurement

Abstract: The aim of this study is to predict residual stress and hardness of a case-hardened steel samples based on the Barkhausen noise measurements. A data-based approach for building a prediction model proposed in the paper consists of feature generation, feature selection and model identification and validation steps. Features are selected with a simple forward-selection algorithm. A multivariable linear regression models are used in predictions. Throughout the selection and identification procedures a cross-validation is used to guarantee that the results are realistic and hold also for future predictions. The obtained prediction models are validated with an external validation data set. Prediction accuracy of the prediction models is good showing that the proposed modelling scheme can be applied to prediction of material properties.

Hybrid Time‐Frequency Blind Source Separation Towards Ambient System Identification of Structures

Abstract: This article will discuss how ambient system identification in noisy environments, in the presence of low-energy modes or closely-spaced modes, is a challenging task. Conventional blind source separation techniques such as second-order blind identification (SOBI) and Independent Component Analysis (ICA) do not perform satisfactorily under these conditions. Furthermore, structural system identification for flexible structures require the extraction of more modes than the available number of independent sensor measurements. This results in the estimation of a non-square modal matrix that is spatially sparse. To overcome these challenges, methods that integrate blind identification with time-frequency decomposition of signals have been previously presented. The basic idea of these methods is to exploit the resolution and sparsity provided by time-frequency decomposition of signals, while retaining the advantages of second-order source separation methods. These hybrid methods integrate two powerful time-frequency decompositions—wavelet transforms and empirical mode decomposition—into the framework of SOBI. In the first case, the measurements are transformed into the time-frequency domain, followed by the identification using a SOBI-based method in the transformed domain. In the second case, a subset of the operations are performed in the transformed domain, while the remaining procedure is conducted using the traditional SOBI method. A new method to address the under-determined case arising from sparse measurements is proposed. Each of these methods serve to address a particular situation: closely-spaced modes or low-energy modes. The proposed methods are verified by applying them to extract the modal information of an airport control tower structure located in Canada.

Online building thermal parameter estimation via Unscented Kalman Filtering

Abstract: This study demonstrates how an Unscented Kalman Filter augmented for parameter estimation can accurately learn and predict a building's thermal response. Recent studies of buildings' heating, ventilating, and air-conditioning systems have shown 25% to 30% energy conservation is possible with advanced occupant and weather responsive control systems. Hindering the widespread deployment of such prediction-based control systems is an inability to readily acquire accurate, robust models of individual buildings' unique thermal envelope. Low-cost generation of these thermal models requires deployment of online data-driven system identification and parameter estimation routines. We propose a novel gray-box approach using an Unscented Kalman Filter based on a multi-zone thermal network and validate it with EnergyPlus simulation data. The filter quickly learns parameters of a thermal network during periods of known or constrained loads and then characterizes unknown loads in order to provide accurate 48+ hour energy predictions. Besides enabling advanced controllers, the model and predictions could provide useful analysis, monitoring, and fault detection capabilities.

Identifying models of HVAC systems using semiparametric regression

Abstract: Heating, ventilation, and air-conditioning (HVAC) systems use a large amount of energy, and so they are an interesting area for efficiency improvements. The focus here is on the use of semiparametric regression to identify models, which are amenable to analysis and control system design, of HVAC systems. This paper briefly describes two testbeds that we have built on the Berkeley campus for modeling and efficient control of HVAC systems, and we use these testbeds as case studies for system identification. The main contribution of this work is that the use of semiparametric regression allows for the estimation of the heating load from occupancy, equipment, and solar heating using only temperature measurements. These estimates are important for building accurate models as well as designing efficient control schemes, and in our other work we have been able to achieve a reduction in energy consumption on a single room testbed using heating load estimation in conjunction with the learning-based model predictive control (LBMPC) technique. Furthermore, this framework is not restrictive to modeling nonlinear HVAC behavior, because we have been able to use this methodology to create hybrid system models that incorporate such nonlinearities.

Nonlinear Identification of a Minimal Neuromuscular Blockade Model in Anesthesia

Abstract: This brief presents new modeling and identification strategies to address many difficulties in the identification of anesthesia dynamics. The most commonly used models for the effect of muscle relaxants during general anesthesia comprise a high number (greater than eight) of pharmacokinetic and pharmacodynamic parameters. The main issue concerning the neuromuscular blockade system identification is that, in the clinical practice, the input signals (drug dose profiles to be administered to the patients) vary too little to provide a sufficient excitation of the system. The limited amount of measurement data also indicates a need for new identification strategies. A new single-input single-output Wiener model with two parameters is hence proposed to model the effect of atracurium. An extended Kalman filter approach is used to perform the online identification of the system parameters. This approach outperforms many conventional identification strategies, and shows good results regarding parameter identification and measured signal tracking, when evaluated on a large patient database. The new method proved to be adequate for the description of the system, even with the poor input signal excitation and the few measured data samples present in this application. It turns out that the method is of general validity for the identification of drug dynamics in the human body.

Model predictive control for wind turbines with distributed active flaps: incorporating inflow signals and actuator constraints

Abstract: This paper describes the implementation of system identification and controller design techniques using model predictive control (MPC) for wind turbines with distributed active flaps for load control. An aeroservoelastic model of the 5 MW NREL/Upwind reference wind turbine, implemented in the code DU_SWAMP, is used in an industry-based MPC controller design cycle, involving the use of dedicated system identification techniques. The novel multiple-input multiple-output MPC controllers, which incorporate flap actuator constraints and the use of local inflow measurement signals, are designed and implemented for various operating points. The controllers are evaluated in standard power production load cases and fatigue load reductions up to 27.3% are achieved. The distributed flaps controller scheme is also compared with simpler single-flap single-input single-output and individual pitch controller schemes. Copyright © 2011 John Wiley & Sons, Ltd.