Multivariable systems

Call number: LD2668 .T4 1963 H365Master of Scienc

Feedback for neuronal system identification

In order to estimate reliable models from noisy input-output data, system identification techniques usually require that the data be generated by a process with a fading memory. Non-equilibrium systems such as neuronal and chaotic models lack a fading memory. Their identification is challenging, in particular in the presence of input noise. In this thesis, we propose a methodology based on the prediction-error method for the identification of neuronal systems subject to input-additive noise. We build on the fundamental observation that while a neuronal model does not have a fading memory, it can be transformed into a fading memory system by output feedback. Our ideas can be generalized to any non-equilibrium system sharing this property. At the core of the methodology is the use of output feedback in experiment design. We provide a theoretical justification for this design choice, which has been exploited in neurophysiology since the invention of the voltage-clamp experiment. To investigate the problem of feedback for identification, we first address the estimation of simple non-equilibrium systems in Lure form, and show that feedback allows estimating the nonlinearity in a static experiment. We then address the estimation of conductance-based models. Assuming that an informed choice can be made on the elements of the model structure, we show that consistent parameter estimates can be obtained when noise is only present at the system input. Finally, we approach the problem from a black-box perspective, and propose identifying the neuronal internal dynamics using a universal approximator with Generalized Orthogonal Basis Functions.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) – Brasil (Finance Code 001

On-line estimation of steam boiler plant dynamics

Imperial Users onl

Proceedings of the 3rd Annual Conference on Aerospace Computational Control, volume 1

Conference topics included definition of tool requirements, advanced multibody component representation descriptions, model reduction, parallel computation, real time simulation, control design and analysis software, user interface issues, testing and verification, and applications to spacecraft, robotics, and aircraft

Limits on the Identification Time for Linear Systems

The problem, of estimating the impulse response of a linear system arises is adaptive, control problems and elsewhere. Often it is necessary to make the system identification in the presence of external noise disturbances. This work considers the problem of determining the time that is necessary to estimate the impulse response of a linear system with a specified variance. It is assumed that\u27 essentially no a priori knowledge about the unknown system is available, and that the output signal of the system is corrupted by an additive stationary noise signal. An ideal identifier is defined as a device that yields, for a given identification time, minimum variance estimates of samples of the unknown impulse response function. Statistical parameter estimation techniques are used to determine the identification time required by an ideal identifier. The results show that when the external disturbance is Gaussian and white, and the output signal energy is large compared to the power spectral density of the noise, the identification time is. proportional to the power spectral density of the noise and inversely proportional to the variance of the estimate and the mean square value of the input test signal. The identification time is independent of the impulse reopens® being estimated. . The identification times required by several practical identification schemes are calculated and compared to the identification time of the ideal identifier. It is established that, when the input test signal is optimized and the noise is white, the methods of cross correlation sampling input-output data, and matched filter identification are all equivalent to the ideal identifier. Depending upon the size of the variance in the impulse response estimate that is required it is concluded that, in the absence of a priori knowledge about the system, and when the rms response of the system to the input test signal is of the same order of magnitude as the variance of the external noise, the time required to identify an unknown system is an order of magnitude or more greater than the significant length of the impulse response. It is also concluded that, when the noise is white and the test signal is optimized, no measurement technique will yield a smaller identification time than that of the ideal identifier. It is pointed out that further reduction in identification time could probably be achieved by identification schemes making maximum use of all available a priori knowledge about the system

A Class of Predictive Adaptive Controls

A new class of control systems termed predictive adaptive controls is developed and the performance characteristics are investigated analytically and experimentally. The concepts of signal prediction, interval control, and synthesis of the control variable by a sum of orthonormal polynomials in t are introduced and developed in relation to adaptive control. A modified least squares integral index of performance is formulated and used as the criterion for system optimization. Control of dynamic processes is subdivided into intervals of a specified length T and prediction is used to obtain estimates of future values of system error. Minimization of the index of performance leads to a family of control laws which specify the structure of the controller. The resulting control configuration is optimum in a specific mathematical sense and is readily realizable with available physical components. The adaptive capability is achieved through time-varying gains which are specific functions of the unit impulse response of the dynamic process being controlled. Predictor design is presented in terms of the classical Wiener-Lee theory, and a relationship for control interval length as a function of prediction accuracy is developed. Preliminary design of the controller is considered from the viewpoints of relative weighting of system error and control effort, control interval length T, and the number of terms needed In the orthonormal polynomial sum approximation of the control variable. A method of obtaining an engineering estimate of the latter quantity is developed and 11 lustrated by three examples, two of which are investigated experimentally. Two applications of predictive adaptive control are investigated on an analog computer. The two dynamic processes used are a first-order process whose parameter varies over a range of ten to one and a second-order process whose parameter varies in such a manner that the process is unstable at one extremum and heavily damped at the other. The results of three basic experiments which evaluate the steady-state adaptability transient response, and statistical signal response of the two systems are reported. It is found that all three aspects of system performance improve with decreasing control interval length, but that the minimum value of the interval length which can be used is limited by the accuracy of the time-varying gain and controller circuitry. Improved performance which can be achieved by increasing the relative weighting of System error and control effort, is limited by saturation considerations. Theoretical, results that .point to the need., for keeping the control interval length short to preserve stability, prediction accuracy, and loss of control doe to process parameter drift are substantiated by the experimental results. For the two systems investigated it is found that satisfactory control Is achieved If the interval length is chosen so that process parameter drift Is no more than 4% per control interval, A figure of 5% was estimated originally, : A one-term approximation of the control variable is used to control .the first-order process- and Is found to give satisfactory performance. A four-term approximation Is found to give adequate control of the second- order process whereas the three-term approximation does not. These results bear out the predictions made in the-theoretical analyses