In this work, the model algorithmic control (MAC) method is applied to control the grade change operations in paper mills. The neural network model for the grade change operations is identified first and the impulse m...
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In this work, the model algorithmic control (MAC) method is applied to control the grade change operations in paper mills. The neural network model for the grade change operations is identified first and the impulse model is extracted from the neural network model. Results of simulations for MAC control of grade change operations are compared with plant operation data. The major contribution of the present work is the application of MAC in the industrial plants based on the identification of neural network models. We can confirm that the proposed MAC method exhibits faster responses and less oscillatory behavior compared to the plant operation data in the grade change operations.
This paper considers the design and analysis of a robust adaptive model algorithmic controller based on the internal modelcontrol structure. The Certainty Equivalence principle of adaptive control is used to combine ...
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ISBN:
(纸本)0780364953
This paper considers the design and analysis of a robust adaptive model algorithmic controller based on the internal modelcontrol structure. The Certainty Equivalence principle of adaptive control is used to combine a discrete-time robust adaptive law with a modelalgorithmic internal modelcontroller to obtain an adaptive modelalgorithmic internal modelcontrol scheme with provable guarantees of stability and robustness. The approach used parallels the earlier results obtained for adaptive internal modelcontrol. Nevertheless, there are some important differences which, together with the widespread interest in model algorithmic control, justifies a separate exposition.
A Generalized Hammerstein model with Impulse Response for symmetric nonlinear systems was presented in this paper. A hyper-quadratic object function was developed by adding highest order control input term with a symb...
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ISBN:
(纸本)9783037850695
A Generalized Hammerstein model with Impulse Response for symmetric nonlinear systems was presented in this paper. A hyper-quadratic object function was developed by adding highest order control input term with a symbolic function into the object function, and a constrained multi-step model algorithmic control for the non-minimum phase systems with open-loop stable characterization was established by forcing the control input with saturated limitation. The algorithm with one control policy can guarantee the simulative results without steady state deviation and the control input being converged to a varying region centered in the zero-point. Simulated results validated the constrained model algorithmic control for Generalized Hammerstein model with impulse response is reasonable and applicable.
control of pH neutralization processes is challenging in the chemical process industry because of their inherent strong nonlinearity. In this paper, the model algorithmic control (MAC) strategy is extended to nonlinea...
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control of pH neutralization processes is challenging in the chemical process industry because of their inherent strong nonlinearity. In this paper, the model algorithmic control (MAC) strategy is extended to nonlinear processes using Hammerstein model that consists of a static nonlinear polynomial function followed in series by a linear impulse response dynamic element. A new nonlinear Hammerstein MAC algorithm (named NLH-MAC) is presented in detail. The simulation control results of a pH neutralization process show that NLH-MAC gives better control performance than linear MAC and the commonly used industrial nonlinear propotional plus integral plus derivative (PID) controller. Further simulation experiment demonstrates that NLH-MAC not only gives good control response, but also possesses good stability and robustness even with large modeling errors.
model algorithmic control (MAC) is a relatively new design methodology successfully used by industries for the last several years. The objective of this paper is to investigate robustness properties of MAC, and evalua...
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model algorithmic control (MAC) is a relatively new design methodology successfully used by industries for the last several years. The objective of this paper is to investigate robustness properties of MAC, and evaluate the use of adaptive methods for real-time identification of the plant under closed-loop control. Some theoretical robustness properties of MAC are given in terms of classical qualities such as gain margin and phase margin for a wide class of systems. Although MAC is an output-feedback controller, it has a guaranteed continuous-time equivalent phase margin of 60°, and the upward gain margin can be made arbitrarily large by slowing down the reference trajectory. Some robustness properties of MAC are also given by a perturbation analysis of a miss-modeled plant impulse response. Preliminary results are discussed for on-line identification of the closed-loop plant using the canonical variate method. Performance of the identification of the plant in the presence of both input and measurement noise is given.
The robust stability of model Predictive control (MPC) is analyzed without performing any numerical computations. Some robust stability theorems are newly derived. They assure that a stable MPC system is easily realiz...
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The robust stability of model Predictive control (MPC) is analyzed without performing any numerical computations. Some robust stability theorems are newly derived. They assure that a stable MPC system is easily realized only by tuning a couple of control parameters even if some large plant-model mismatch exist. A guideline for the tuning the control parameters is also provided.
In the last two decades, model predictive control has made significant progress in research and industrial process control. This achievement is through the high ability of model predictive control to solve industrial ...
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In the last two decades, model predictive control has made significant progress in research and industrial process control. This achievement is through the high ability of model predictive control to solve industrial process control problems in the time domain. It is applicable in many control systems, such as delayed systems, univariate and multivariable systems, non-minimum phase systems, and unstable systems. This strategy is essentially for controlling delayed systems. It is possible to create a time delay in transmitting structure movement data in the systems that use sensors and receivers of sensor-recorded data, which is a factor to reduce the efficiency of the control system and even the instability of the structure. In this study, to investigate and compensate the effect of time delay in the hybrid vibration control system, an 11-story structure with a tuned mass damper and an MR damper was used by model algorithmic control (MAC) and nonlinear behavior of the structure in the control process. The linear and nonlinear structural models are created in OpenSEES, the control system in MATLAB;and the TCP/IP connection method was used to connect two programs. The fuzzy decision system is based on accelerating and decelerating movement. The structure has been subjected to seven earthquakes with maximum accelerations of 0.1 g to 1.0 g with an incremental step of 0.1 g. According to the results of incremental dynamic analysis, the average percentage of the fuzzy control system performance reduction, considering the time delay, is 15.28% and 38.57% for the maximum displacement and base shear in the linear structure, and 8.04% and 5.97% for the nonlinear structure, respectively. The assumed time delays have been effectively overcome using the model algorithmic control system (MAC), determining the prediction horizon of the structural behavior, and optimizing the control force in the control horizon. The performance of the MAC control system has been better than the fu
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