Simulation of Adaptive LQ Control of Nonlinear Process

Vojtěšek, Jiří; Dostál, Petr

doi:10.24846/v21i3y201210

Cited by 10 publications

(9 citation statements)

References 4 publications

(8 reference statements)

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“…1DOF scheme is shown in Fig. 4 where Q(s) represents the transfer function of the controller in the feedback part and G(s) is ELM of the controlled system (8). Signal w is used for reference signal (wanted value), u is control input, y controlled output and e represents control error, e.g.…”

Section: Controller Designmentioning

confidence: 99%

“…where α is position of the root which affects the quality of control and polynomial n(s) is obtained from the spectral factorization of the identified polynomial a(s) in the denominator of the ELM (8). The use of this factorization connects the design of the polynomial d(s) with the controlled system which helps with reduces generality of the Pole-placement method.…”

Section: Controller Designmentioning

confidence: 99%

“…Experiences from the previous works [8], [9] have shown that used polynomial approach together with the Pole-placement method and Spectral factorization satisfies basic control requirements [7] such as stability, disturbance attenuation, reference signal tracking etc. and produces also beneficially the computation relations which are easily programmable.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive control of water level in real model of water tank

Vojtěšek

Dostál

2015

2015 20th International Conference on Process Control (PC)

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This paper shows control results of simulations on mathematical model and real measurements on the real model of the water tank. The level in the tank is controlled via input volumetric flow rate through the proportional solenoid valve. The adaptive approach with the recursive identification was employed here for controlling of this system. The controller could be tuned by the choice of the pole position in the pole-placement method. The benefit of this paper could be found also with the verification of the obtained simulation results on the real model of the water tank which shows usability of this control approach although the system has nonlinear properties. Control measurements also show hysteresis of the proportional solenoid valve which was used as an actuator in the control.

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Section: Controller Designmentioning

confidence: 99%

Section: Controller Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive control of water level in real model of water tank

Vojtěšek

Dostál

2015

2015 20th International Conference on Process Control (PC)

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“…Our experiments (Vojtesek and Dostal 2012) have shown that it is good to connect the choice of the polynomial d(s) with the parameters of the controlled system, for example with the use of spectral factorization of the polynomial a(s) in the numerator of G(s) in (1) known from the recursive identification…”

Section: ( ) ( )mentioning

confidence: 99%

Simulation Of 1DOF And 2DOFAdaptive Control Of The Water Tank Model

Vojtěšek

Dostál

Mašlan³

2015

ECMS 2015 Proceedings Edited By: Valeri M. Mladenov, Petia Georgieva, Grisha Spasov, Galidiya Petrova

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The water tank is a typical nonlinear system representing several types of systems like barrels, reservoirs, tanks etc. for liquid. The simulation, similarly as in other cases, can help with the understanding of the system's static and dynamic behavior and it is starting point for the controller design. The adaptive control in this work is based on the choice of the external linear model of the nonlinear system, parameters of which are identified recursively and the controller's parameters are also recomputed recursively according to the identified system. Basic control requirements are satisfied with the use of polynomial approach together with the Pole-placement method and spectral factorization. The resulting controller has also one tuning parameter which affects mainly the speed of the control and overshoots. Two control schemes with one degree-of-freedom and two degrees-of-freedom are tested and compared in the work.

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“…Because of the advantage of selfadaptation, adaptive controller can tolerate the uncertainties and external disturbances in closed-loop system [7,11,19,21]. In the adaptive control system, a common method for dealing with uncertainties including the parameters or structure is to design an adaptive parameter estimates algorithm or an auxiliary controller [3,4,14].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Proportional Differential Neural Network Control for Unknown Nonlinear System

Wang¹,

Li²,

Yang³

et al. 2016

SIC

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This paper presents an intelligent proportion-differential neural network (iPDNN) controller for unknown nonlinear systems. This controller is based on the intelligent proportion integration differentiation (iPID) controller. In an iPID controller system, a unknown nonlinear SISO system is regarded as an ultra-local two-order or one-order model and a lumped unknown dynamics (LUD) disturbance which contains the high-term and parametric uncertainties by the differential algebra and estimation method online. However, its performance of an iPID control depends on the precision and rapidity for estimating the LUD disturbance. Besides, it also influences the parameter in the ultra-local model. In order to compensate the estimation error of LUD disturbance, we put forward an extra radial basis function (RBF) neural network observer to estimate it. This extra observer cannot only ensure to acquire the estimation error rapidly, but also has an ability of self-learning. In addition, this iPDNN method can ensure the closed-loop system stability under the Lyapunov stability theory. Finally, in order to demonstrate its performance, an inverted pendulum plant has been applied and the results indicate this method is of efficiency.

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Simulation of Adaptive LQ Control of Nonlinear Process

Cited by 10 publications

References 4 publications

Adaptive control of water level in real model of water tank

Adaptive control of water level in real model of water tank

Simulation Of 1DOF And 2DOFAdaptive Control Of The Water Tank Model

Intelligent Proportional Differential Neural Network Control for Unknown Nonlinear System

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