Control System Identification Research Articles

A Covariance Function Based Approach to Networked System Identification

System identification for networked control is considered. Due to the random time-delays in the network, it can be difficult to work with a discrete-time model and a continuous-time model is therefore chosen here. A covariance function based method is proposed for estimating the parameters, which relies on the second order statistical properties of the output signal and that the input signal samples can be taken from a discrete-time white noise sequence. Numerical studies indicate that the method is robust to additive measurement noise and packet dropouts.

Variable step-size Griffiths’ algorithm for improved performance of feedforward/feedback active noise control

A new robust computationally efficient variable step-size LMS algorithm is proposed and it is applied for secondary path (SP) identification of feedforward and feedback active noise control (ANC) systems. The proposed variable step-size Griffiths’ LMS (VGLMS) algorithm not only uses a step-size, but also the gradient itself, based on the cross-correlation between input and the desired signal. This makes the algorithm robust to both stationary and non-stationary observation noise and the additional computational load involved for this is marginal. Further, in terms of convergence speed and error, it is better than those by the Normalized LMS (NLMS) and the Zhang’s method (Zhang in EURASIP J. Adv. Signal Process. 2008(529480):1–9, 2008). The convergence rate of the feedforward and feedback ANC systems with the VGLMS algorithm for SP identification is faster (by a factor of 2 and 3, respectively) compared with that using NLMS algorithm. For feedforward ANC, its convergence rate is faster (3 times) compared with Akhtar’s algorithm (Akhtar in IEEE Trans Audio Speech Lang Process 14(2), 2006). Also, for higher main path lengths compared with SP, the proposed algorithm is computationally efficient compared with Akhtar’s algorithm.

Integrated identification of RFX-mod active control system from experimental data and finite element model

The RFX-mod system for the active control of MHD instabilities has been operating for more than 2 years contributing to improvements of the discharge quality and length. In the past years a dynamic black-box model of the active coils and saddle probes has been developed to analyze and simulate the system electromagnetic behaviour, which is strongly dependent on the frequency due to the presence of passive conductors. Satisfactory results were achieved in reproducing most of experimental responses and the model was used to select and test the gains of operating PID regulators. However, difficulties have emerged in reproducing some flux distributions such as those corresponding to m = 0, low n current patterns. In view of higher current operation a further increase in control system performance and model accuracy is desirable, if not mandatory. To this purpose, new measures have been recently acquired in order to reassess data of critical magnetic couplings and an activity has been started aimed at the verification and integration of data from a white-box model provided by the 3D finite element electromagnetic code Cariddi. The above verification is also a preliminary step for the validation of the CarMa computational tool adaptation to RFX-mod, which has been undertaken to study the plasma resistive wall modes. The paper describes the results of a comparative analysis carried out on experimental and simulation data obtained with the black-box and white-box models.

System identification for process control: recent experience and outlook

This work reports the development of an identification technology and its application to advanced process control (APC) in the refining/petrochemical industry. The author will introduce model predictive control (MPC) technology which, in the last two decades, has become the standard tool in industrial APC. Model identification plays a crucial role in MPC technology and it is also the most time consuming and difficult task in MPC projects and maintenance. Key issues of identification for MPC will be discussed. The so called ASYM method of identification is outlined that provides systematic solutions to problems from plant test to model validation. Based on the method, both off-line and online identification packages have been developed. A large-scale industrial application will be shown. Considerable benefits are obtained using the new identification technology: 1 reduction of identification test time and model building time by over 70% 2 higher model quality for control 3 easier in use. Then, several MPC ...

System identification-based control of an unmanned autonomous wind-propelled catamaran

An autonomous catamaran, based on a modified Prindle-19 day-sailing catamaran and fitted with several sensors and actuators was built to test the viability of GPS-based system identification for precision control. Using an electric trolling motor for propulsion, and lead ballast to match all-up weight, several system identification passes were performed to excite system modes and model the dynamic response. The identification process used the Observer Kalman IDentification (OKID) method for identifying a linear time invariant plant model and associated pseudo-Kalman filter. System identification input was generated using a human pilot driving the catamaran on roughly straight line passes. A fourth order discrete time model was generated from the data, and showed excellent prediction results. Using these models, linear quadratic Gaussian (LQG) controllers were designed and tested with the electric trolling motor. These controllers demonstrated excellent line-tracking performance, with error standard deviations of less than 0.15 m. The wing-sail propulsion system was fitted, and these same controllers re-tested with the wing providing all propulsive thrust. Line-following performance and disturbance rejection were excellent, with the cross-track error standard deviations of approximately 0.30 m, in spite of wind speed variations of over 50% of nominal value.

Physics-based mathematical models for quantum devices via experimental system identification

We consider the task of intrinsic control system identification for quantum devices. The problem of experimental determination of subspace confinement is considered, and simple general strategies for full Hamiltonian identification and decoherence characterization of a controlled two-level system are presented.

System identification for control of a main irrigation canal pool

In this paper, a system identification for control procedure of a main irrigation canal pool characterized by exhibiting large variations in their dynamical parameters when it discharge regimes change is developed. This procedure delivers not only a nominal model, but also a reliable estimate of the canal pool parametric uncertainty associated with the model. The complete identification for control procedure from experiment design to model validation taking into account prior physical information is presented. It is shown that a linear second order model with an ARMAX structure and a time delay describes adequately the main nominal dynamical behavior of this canal pool. Application of system identification for control in control system design of water distribution in main irrigation canal pools responds to the current necessity of introducing more effective and robust control systems.

Nonlinear system identification for predictive control using continuous time recurrent neural networks and automatic differentiation

In this paper, a continuous time recurrent neural network (CTRNN) is developed to be used in nonlinear model predictive control (NMPC) context. The neural network represented in a general nonlinear state-space form is used to predict the future dynamic behavior of the nonlinear process in real time. An efficient training algorithm for the proposed network is developed using automatic differentiation (AD) techniques. By automatically generating Taylor coefficients, the algorithm not only solves the differentiation equations of the network but also produces the sensitivity for the training problem. The same approach is also used to solve the online optimization problem in the predictive controller. The proposed neural network and the nonlinear predictive controller were tested on an evaporation case study. A good model fitting for the nonlinear plant is obtained using the new method. A comparison with other approaches shows that the new algorithm can considerably reduce network training time and improve solution accuracy. The CTRNN trained is used as an internal model in a predictive controller and results in good performance under different operating conditions.

Modeling, Identification, Design, and Implementation of Nonlinear Automotive Idle Speed Control Systems—An Overview

Automotive idle speed control (ISC) is one of the most challenging aspects in engine control fields. Essentially it is a highly nonlinear, time-varying, complicated, and uncertain dynamic control problem. As the typical automotive transient operation, the quality of the ISC has a significant impact on fuel economy, emission levels (HC, CO, and NOx), combustion stability, transient response, and noise, vibration, and harshness characteristics. This paper presents a thorough review of various dynamic control technologies which have been successfully applied to ISC systems. In particular, practical implementations on a variety of different engine types are provided, which cover broad areas of control, including classical control, modern control, and intelligent control. Over 90 selected papers published during the last two decades are reviewed and then summarized from a control point of view. These control approaches can be generalized to the control of other automotive, electrical, mechanical, and aeronautical systems.

An adaptive dual control framework for QoS design

The widespread deployment of the advanced computer technology in business and industries has demanded the high standard on quality of service (QoS). For example, many Internet applications, i.e. online trading, e-commerce, and real-time databases, etc., execute in an unpredictable general-purpose environment but require performance guarantees. Failure to meet performance specifications may result in losing business or liability violations. As systems become distributed and complex, it has become a challenge for QoS design. The ability of on-line identification and auto-tuning of adaptive control systems has made the adaptive control theoretical design an attractive approach for QoS design. However, there is an inherent constraint in adaptive control systems, i.e. a conflict between asymptotically good control and asymptotically good on-line identification. This paper first identifies and analyzes the limitations of adaptive control for network QoS by extensive simulation studies. Secondly, as an approach to mitigate the limitations, we propose an adaptive dual control framework. By incorporating the existing uncertainty of on-line prediction into the control strategy and accelerating the parameter estimation process, the adaptive dual control framework optimizes the tradeoff between the control goal and the uncertainty, and demonstrates robust and cautious behavior. The experimental study shows that the adaptive dual control framework mitigate the limitations of the conventional adaptive control framework. Compared with the conventional adaptive control framework under the medium uncertainty, the adaptive dual control framework reduces the deviation from the desired hit-rate ratio from 40% to 13%.

The Atlantis Project: A GPS-Guided Wing-Sailed Autonomous Catamaran

ABSTRACT: An autonomous catamaran, based on a modified Prindle-19 day-sailing Catamaran, was built to test the viability of GPS-based system identification for precision control. The catamaran was fitted with several sensors and actuators to characterize the dynamics. Using an electric trolling motor, and lead ballast to match all-up weight, several system identification passes were performed to excite system modes and model the dynamic response. LQG controllers were designed based on the results of the system identification passes, and tested with the electric trolling motor. Line following performance was excellent, with cross track error standard deviations of less than 0.15 meters. The wing-sail propulsion system was fitted, and the controllers tested with the wing providing all forward thrust. Line following performance and disturbance rejection were excellent, with the cross track error standard deviations of approximately 0.30 meters, in spite of wind speed variations of over 50% of nominal value.

Adaptive parameter identification of servo control systems with noise and high-frequency uncertainties

When identifying the parameters of practical servo systems, the high-frequency system modes are frequently neglected in order to simplify the model. Additionally, avoiding measurement noise in physical experiments is virtually impossible Therefore, selecting an appropriate excitation signal is essential. Specifying white noise, or other such signals, as the input signal may cause the excitation of high-frequency uncertainties, which affects the reliability of the parameter identification process. The current study proposes a novel approach in which a particular class of chaotic signal is employed as the excitation signal in an adaptive parameter identification process. Since chaotic signals typically have stationary, continuous, and band-limited power spectra, they are suitable for on-line parameter identification. The present numerical and experimental results demonstrate that the use of a chaotic excitation signal in the identification process causes the estimated system parameters to converge within identifiable ranges, even when the system includes measurement noise and high-frequency uncertainties. The current results also show that there is good agreement between the dynamics of the real system and those of the estimated model within the operation bandwidth.

System identification and active vibration control of a composite I-beam using smart materials

This research concerns active vibration control of a 3.35-m-long composite I-beam in a cantilevered configuration by using peizoceramics smart materials, in particular the PZT (lead zirconate titanate), in the form of patches. These PZT patches are surface-bonded on the I-beam and function as actuators and sensors. A real-time data acquisition and control system is used to record the experimental data and to implement designed controllers. To assist control system design, open-loop testing and system identification are conducted. To excite the 3.35-m composite I-beam, a sweep sinusoidal input is used and then a state space model is derived for the system. This state space model is used for simulation and development of control algorithms. A positive position feedback (PPF) controller is designed and is simulated using the identified model. Simulations show the dramatic increase in damping of the beams which, when implemented experimentally, shows an increase in the damping up to 1100%. Experimental results verify the simulated results and demonstrate the effectiveness of active control of a civil structure using smart materials. Copyright © 2005 John Wiley & Sons, Ltd.

DEVELOPMENT OF A GUI FOR A SYSTEM IDENTIFICATION DEVICE USING MATLAB

This paper describes the development of a Graphical User Interface (GUI) for a system identification device using MATLAB. MATLAB is a well-known software package that is widely used for control systems design, signal processing, and system identification. However, beginners who are not familiar with MATLAB commands and system identification procedures sometimes find using it difficult, typically because there exist many different approaches to system identification. We propose the use of a GUI, especially suitable for use by beginners, to provide system identification procedures. Difficulties faced by beginners in the use of system identification might be eased by the use of a GUI. The effectiveness of the GUI is shown by a demonstration data in MATLAB.

Internal models in sensorimotor integration: perspectives from adaptive control theory

Internal models and adaptive controls are empirical and mathematical paradigms that have evolved separately to describe learning control processes in brain systems and engineering systems, respectively. This paper presents a comprehensive appraisal of the correlation between these paradigms with a view to forging a unified theoretical framework that may benefit both disciplines. It is suggested that the classic equilibrium-point theory of impedance control of arm movement is analogous to continuous gain-scheduling or high-gain adaptive control within or across movement trials, respectively, and that the recently proposed inverse internal model is akin to adaptive sliding control originally for robotic manipulator applications. Modular internal models' architecture for multiple motor tasks is a form of multi-model adaptive control. Stochastic methods, such as generalized predictive control, reinforcement learning, Bayesian learning and Hebbian feedback covariance learning, are reviewed and their possible relevance to motor control is discussed. Possible applicability of a Luenberger observer and an extended Kalman filter to state estimation problems—such as sensorimotor prediction or the resolution of vestibular sensory ambiguity—is also discussed. The important role played by vestibular system identification in postural control suggests an indirect adaptive control scheme whereby system states or parameters are explicitly estimated prior to the implementation of control. This interdisciplinary framework should facilitate the experimental elucidation of the mechanisms of internal models in sensorimotor systems and the reverse engineering of such neural mechanisms into novel brain-inspired adaptive control paradigms in future.

自動車用エンジン制御システムのモデリングおよび同定

自動車用エンジン制御システムのモデリングおよび同定

Stabilization and System Identification of Control Systems with Quantized Signals

Stabilization and System Identification of Control Systems with Quantized Signals

Microprocessor-based simulation of sampled data systems with/without a hold device using a set of sample-and-hold functions and Dirac delta functions

In the present work, Dirac delta function (DF) set and sample-and-hold functions (SHF) set are used for microprocessor-based simulation of discrete time as well as sample-and-hold systems. Such simulations are useful for identification of control systems with known input and output sequence. The presented method utilizes operational matrices of different orders in the DF and SHF domain to develop different operational transfer functions. A few open-loop as well as closed-loop systems have been studied and the simulation results obtained are compared with exact solutions derived with the help of z -transform analysis. Experiments have also been carried out to establish the validity of the proposal.

1/f-type fluctuation in human visuomotor transformation.

In the absence of vision of the limb, movements toward a visual target exhibit substantial errors which are considered to originate mainly in the visuomotor transformation process. To determine the time-dependent property of human visuomotor transformation, we investigated the error sequences in movements toward visual target using scaling analyses. When subjects could see their controlling limb, the error sequences could not be distinguished from a random sequence. On the other hand, when the controlling limb was invisible, the error sequences were not random in order, but exhibited 1/f-type time correlation. This finding that the variation in human visuomotor transformation shows 1/f-type fluctuation provides a significant index for mathematical modeling and system identification in human visuomotor control.

Modelling, Identification, and Passivity-Based Robust Control of Piezo-actuated Flexible Beam

This paper presents modelling, system identification, simulation, and experimental results for passivity-based robust control of piezo-actuated flexible beam. The flexible beam configuration considered is a cantilever aluminum beam with a piezoelectric transducer used as the actuator and tip-accelerometer as the sensor. The actuator and sensor are non-collocated. The Lagrangian formulation is used to obtain mathematical model of the flexible link dynamics with piezo actuator. For control design purposes, a finite dimensional approximate model is derived using assumed modes approach. It is shown that the approximate model compares very well with the experimentally identified model. Since the system is inherently not passive, passification techniques are used to render the system robustly passive which enables the use of passivity-based feedback control design. The controller design is validated both in simulation as well as in experiments. The simulation and experimental results demonstrate the effectiveness of controller in suppressing the tip vibrations of the link. The controller design is shown to be robust to both parametric uncertainties and unmodeled dynamics.