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An algorithm for adaptive feedforward cancellation (AFC) of high frequency measurable disturbances for a class of nonlinear dynamical systems is developed. A rectangular local linear model (RLLM) network is used to model nonlinear dynamical behaviour of the system and to predict the effect of the disturbances. Another network of the same kind containing local linear compensation units is used as a...
In this paper, we propose and solve the problem of feedback linearization of single-input control affine time-varying nonlinear systems. Feedback linearizability of time-varying is shown to be equivalent to the orbital feedback linearizability (or feedback linearization by state-dependent time scaling) of an extended version of the time-varying system. Necessary and sufficient conditions for feedback...
The present research work aims at the development of a systematic method to arbitrarily assign the zero dynamics of a nonlinear system by constructing the requisite synthetic output maps. The minimum-phase synthetic output maps constructed can be made statically equivalent to the original output maps, and therefore, they could be directly used for nonminimum-phase compensation purposes. Specifically,...
It is difficult to identify some chemical processes which are operated in complex environments and the operation conditions are changed frequently. In this paper we combine the two effective identification tools, multiple models and dynamic neural networks, and propose a new class of identification approach. A hysteresis switching algorithm is used to select the best model in each time. The convergence...
The problem of estimating the state of a discrete-time linear system can be addressed by minimizing an estimation cost function depending on a batch of the recent measurement and input vectors. This problem has been solved by introducing a general receding-horizon objective function that includes also a weighted penalty term related to the prediction of the state. For such an estimator, convergence...
For single-input single-output uncertain nonlinear system, we show how the problem of global robust stabilization via recursive design can be solved by using less conservative bounding functions. The system is assumed to have a gain bounded unknown nonlinear function with semi-strict feedback structure. Moreover we extend the obtained controller to have disturbance attenuation property by modifying...
This paper considers the problem of robust stabilization and disturbance attenuation for a class of uncertain singularly perturbed systems with norm-bounded nonlinear uncertainties. It is shown that the state feedback gain matrices can be determined to guarantee the stability of the closed-loop system for all ε ∊(0,∞). Based on this key result and some standard Riccati inequality approaches for robust...
A framework for identification oriented robust controller design is developed. The model is identified from open-loop ilo-data and contains parametric uncertainties as well as an additive and norm-bounded error. The set of all robustly stabilising controllers, that additionally guarantee robust performance is characterised by a system of second order cones, which can be efficiently solved by interior...
Generally, controlling or monitoring induction motors requires the knowledge of electrical parameters which might not be accurately available, and are anyway known to vary while the motor is operating. For these reasons, various algorithms have been proposed for the identification of these parameters, usually based on stator currents and rotor speed measurements, and generally validated by simulations...
This paper describes a Genetic Algorithm with Resolution Adaptation (GARA) used to solve System Identification (SI) problems. The algorithm is both hybrid, because it combines the genetic search with a fast local hill climbing method to improve the speed of convergence, and adaptive, because it uses information on the actual convergence of the population to modify the resolution of the binary representation...
This paper presents a modified set theoretic framework for estimating the state of a linear dynamic system based on uncertain measurements. The measurement errors are assumed to be unknown but bounded by ellipsoidal sets. Based on this assumption, a recursive state estimator is (re-)derived in a tutorial fashion. It comprises both the prediction step (time update), i.e., propagation of a set of feasible...
The software technology provides a highly interactive and powerful learning environment for control engineering disciplines. This paper describes the development and implementation of Linear Systems Theory Education Tool, which contains lectures connected through graphical user interface with electronic manuscripts and examples Matlab and Simulink. Linear Systems Theory Education Tool is a graphical...
The flexible, multidisciplinary curricula of the Automation & Control program in electrical engineering at the University of Säo Paulo are described. The context of undergraduate engineering education at the university is emphasized, and student and faculty feedback, as well as the difficulties encountered, are reported.
Necessary and sufficient conditions for feedback passivity (passifiablity) of nonsquare linear systems published in Russian and Western literarure are surveyed. New output G-passifiability conditions for nonsquare linear systems are given. The proofs are based on Yakubovich-Kalman-Popov (Kalman-Yakubovich) lemma.
Web based instruction is increasingly attracting the interest of Universities. In this paper, an Internet based course on neural networks, developed in the University of Algarve, is discussed. A special attention is given to techniques of student self-assessment. A structure of knowledge, to enable the automatic creation of on-line courses, is proposed.
The solution to the standard predictive control problem is a continuous function of the state, the reference signal, the noise and the disturbances and hence can be approximated arbitrarily close by a feed-forward neural network. This leads to an analytic constrained predictive controller that combines constraint handling with speed and is applicable to fast systems and complex control problems with...
This work deals with some practical aspects of fuzzy PID controllers regarding multivariable non-linear cascade compensation. This contribution concerns to the task of compensation for multivariable disturbances on the manipulated variable. The strategy consists in establishing a fuzzy rule base for all disturbance variables which afect the manipulated variable and compensate the control variable...
This work deals with some practical aspects of fuzzy PID controllers regarding multivariable non-linear feedforward compensation. This contribution concerns to the task of compensation for multivariable disturbances on the controlled variable. The strategy consist in establishing a fuzzy rule base for all disturbance variables and compensate the control variable by feedforward action by means of a...
The paper describes a graduate course in qualitative modelling and process supervision which is given at the Technical University Hamburg-Harburg. The aim is to show the students a systematic way for describing, analysing and controlling dynamical systems on the basis of qualitative information. The course includes lectures about the theoretical background of the methods, exercises with the QuaMo-Toolbox...
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