Synthesis of adaptive critical control methods, identification and management

The main problem of dynamic plant control is the variability of the parameters of the control object, and we are talking even about the nature of external disturbances, the lack of information about their statistical or stochastic characteristics. An approach to the development of an adaptive controller based on the recurrent least squares method is proposed

Self-adaptive modal control for time-varying structures

International audienceFor the past several years, modal controllers are widely studied and used in the field of vibration or vibro-acoustics control. They are efficient but not robust, because these methods involve a reconstructor based on a modal truncation. When the dynamic behavior of the structure change, the controller and reconstructor must be updated to cope with the changes in the structure behavior, in order to maintain both performance and robustness. A solution is adaptive control but this approach needs some specific information not generally available particularly in the case of undergone modifications. This paper deals with a self-adaptive modal control based on a real-time identifier, which avoid the need of specific information. The identifier permits to update the controller and the reconstructor according to the changes of modal characteristics of time-varying structures. A classical algorithm of identification is used to obtain a state space model with an unspecified state vector. Then, based on this model, a well adapted transformation is carried out to get the modal characteristics from the expression of complex modes, including the mode shapes. As a criterion of running identification, the value of "variance-accounted for" (VAF) is employed to carry out the identifier only when the initial or previous model is not enough exact. A Linear Quadratic Gaussian Algorithm is employed in such a way that the control and observer can be optimized according to the updated modal model. By this way, a self-adaptive modal control is completed and can demonstrate some smart properties. The proposed methodology is carried out on a simple but representative time-varying mechanical discrete structure. An inertia modification leads not only to low modal frequency shifts but also to inversion of a mode shape which is shown to lead to unstable configuration when control system is not 2 updated. The overall procedure will be described through simulations and performed for different operating conditions, which will prove that mode shapes have to be precisely determined and updated in the controller and observer to guarantee a robust modal control with high performance in spite of the changes of structure

Development of PID Controller for Controlling Desired Level of Coupled Tank System

The industrial application of Coupled Tank System(CTS) are widely used especially in chemical process industries. The overall process need liquids to be pumped, stored in the tank and pumped again to another tank for certain desired level. Nevertheless, the level of liquid in tank need to be controlled and flow between two tanks must be regulated. This paper presents development of Proportional-Integral-Derivative (PID) controller for controlling the desired liquid level of the CTS. Various conventional techniques of PID tuning method will be tested in order to obtain the PID controller parameters. Simulation is conducted within MATLAB environment to verify the performances of the system in terms of Rise Time (Ts), Settling Time (Ts), Steady State Error (SSE) and Overshoot(OS). Four techniques which are trial and error method, auto-tuning method, Ziegler-Nichols (Z-N) method and Cohen-Coon (C-C) method will be implemented and all the performance results will be analyzed. It has been demonstrated that performances of CTS can be improved with appropriate technique of PID tuning methods

Refined instrumental variable estimation: maximum likelihood optimization of a unified Box–Jenkins model

For many years, various methods for the identification and estimation of parameters in linear, discretetime transfer functions have been available and implemented in widely available Toolboxes for MatlabTM. This paper considers a unified Refined Instrumental Variable (RIV) approach to the estimation of discrete and continuous-time transfer functions characterized by a unified operator that can be interpreted in terms of backward shift, derivative or delta operators. The estimation is based on the formulation of a pseudo-linear regression relationship involving optimal prefilters that is derived from an appropriately unified Box–Jenkins transfer function model. The paper shows that, contrary to apparently widely held beliefs, the iterative RIV algorithm provides a reliable solution to the maximum likelihood optimization equations for this class of Box–Jenkins transfer function models and so its en bloc or recursive parameter estimates are optimal in maximum likelihood, prediction error minimization and instrumental variable terms

Direct data‐driven design of switching controllers

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Study of Two Instrumental Variable Methods for Closed-Loop Multivariable System Identification.

New control strategies are based on the model of the process and it is thus necessary to identify the systems to be controlled. It is also often necessary to identify them during closed-loop operation in order to maintain efficient operation and product quality. Some results of multivariable closed-loop identification carried out on a simulated 2 x 2 linear time-invariant system, using two new versions of instrumental variable methods called IV4D and IV4UP as the identification methods, are presented. In each case pseudorandom binary signals (PRBS), or dithers, are applied to the outputs of the feedback controllers. Algorithms IV4D and IV4UP are created in a four step environment where iterations are performed to obtain the best possible estimated model. For IV4D only the dither is used as part of the instrument. For IV4UP only the part of the input that comes from the dither is used for the instrument. This is obtained with the estimated model and with the description of the controllers using the closed-loop transfer function between the dither and the input to the process. The implementation is made to be run in MatLab and it uses several of the functions defined in its System Identification Toolbox (Ljung, 1991). Both instrumental variable (IV) algorithms perform very well identifying closed-loop multivariable systems under the influence of white noise and correlated noise disturbances. The two new instrumental variable methods are compared with the prediction error method, PEM, and with IV4, the regular instrumental variable open-loop algorithm, both of them are obtained from the MatLab System Identification Toolbox. IV4 does not perform well in closed-loop operation. From the simulated results, the performances of the new IV algorithms are the best but, PEM\u27s performance is very close. Finally, real plant data are analyzed with IV4D and its results are compared with the results of other identification methods, PEM and Dynamic Matrix Identification (DMI) (Cutler and Yocum, 1991). For this closed-loop real plant data PEM is the best that performs followed by IV4D, while DMI does not perform well