For predictive control in industry often very long horizons for control error and manipulated signal are used because of the slow processes which take place in the petrochemical industry. In order to reduce the comput...
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Dynamic properties of a hybrid system are analyzed that has been developed to generate electric energy by utilizing alternative, renewable and waste energies. The solution is based on a system that consists of a high-...
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Dynamic properties of a hybrid system are analyzed that has been developed to generate electric energy by utilizing alternative, renewable and waste energies. The solution is based on a system that consists of a high-speed turbine, coupled to a three-phase permanent magnet synchronous generator, an AC/AC converter and a supervisory control unit. The function of the system is to utilize the energy content of some working medium by a special turbine that converts the energy content of the working medium into electric energy by the generator and the AC/AC converter. The alternative renewable energy sources may include solar energy (direct solar steam system or solar energy with heat exchanger), geothermal energy (with or without heat exchanger) and energy obtained from biomass. Various waste energy sources can also be utilized by applying the system such as the energy that can be obtained during the process of pressure reduction in steam, gas and fluid networks. The electric power generated by the system can be fed back to the utility mains or it can be used in stand-alone mode to supply a selected group of loads. The paper analyses a novel control strategy and principle of the system to ensure optimum performance and economy by using a special solution for ensuring the power equilibrium in the combined mechanical-electric drive.
With predictive control most of the computation time is spent for the simulation of the predicted variables and for the optimization if constraints or nonlinear processes are assumed. In addition to the known blocking...
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With predictive control most of the computation time is spent for the simulation of the predicted variables and for the optimization if constraints or nonlinear processes are assumed. In addition to the known blocking technique for the manipulated variable another possibility is calculating the control error not in each sampling point of the prediction horizon but only in some coincidence points. It will be shown that the best choice is to allocate the coincidence points exponentially thus that with small prediction steps more and with increasing prediction steps less coincidence points are considered. As a practical example the multivariable control of a distillation column model illustrates the benefits of the method presented.
Long-range optimal prediction algorithms use the predicted output for several steps ahead. The prediction based on traditionally estimated model parameters does not result in an optimal prediction if the measurements ...
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Long-range optimal prediction algorithms use the predicted output for several steps ahead. The prediction based on traditionally estimated model parameters does not result in an optimal prediction if the measurements are noisy or/and model structure differs from real process structure. In this paper two different identification schemes are presented and compared: long-range predictive single-model identification and simultaneous multi-step-ahead prediction identification. It is shown that the first method is easier to realize but the second one leads to more accurate results. Both methods are derived for a first-order model in details. Simulation runs and a level control example illustrate the algorithms presented.
In this paper, a direct learning control method for a class of switched systems is proposed. The objective of direct learning is to generate the desired control profile for a newly switched system without any feedback...
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In this paper, a direct learning control method for a class of switched systems is proposed. The objective of direct learning is to generate the desired control profile for a newly switched system without any feedback, even if the system may have uncertainties. This is achieved by exploring the inherent relationship between any two systems before and after a switch. The new method is applicable to a class of linear time varying, uncertain and switched systems, when the trajectory tracking control problem is concerned. A numerical simulation demonstrates the effectiveness of the proposed method.
The paper presents a control system design technique in delta domain for IMC (Internal Model control) structure. Also a generalised form for delta domain Dead-beat control algorithm is given, a hybrid implementation o...
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The paper presents a control system design technique in delta domain for IMC (Internal Model control) structure. Also a generalised form for delta domain Dead-beat control algorithm is given, a hybrid implementation of the controller (Z-domain combined with delta domain) is presented and its architecture is compared with the pure deltadomain implementation. The effect of placing the limitations in the IMC structure is also studied. The hybrid control structure is compared with a similar Dead-beat controller in Z-domain for second and third order plants (benchmarks). An illustrative sensitivity analysis between the hybrid control system and the Z-domain system has been performed.
In the paper there are presented some controller design methods in delta domain based on the delta model of the plant aiming to compare them with similar methods specific to continuous and discrete time design. Design...
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In the paper there are presented some controller design methods in delta domain based on the delta model of the plant aiming to compare them with similar methods specific to continuous and discrete time design. Design of PI, PID and Dead-beat algorithms are presented and exemplified for second and third order plants (benchmarks). The design of PID controllers use (i) optimization techniques based on Modulus Optimum method and (ii) pole cancellation method with imposing an "optimum" value for the phase margin. An illustrative sensitivity analysis between delta domain controllers and Z discrete time controllers has been made. Simulations are made regarding delta design and compared with results obtained through continuous design and Z-discrete implementation. Dead-beat controllers are considered optimal from the point of view of settling time. The results support as a viable alternative the design in delta domain and show less sensitivity in the case of delta controllers versus the discrete (Z) design.
For predictive control in industry often very long horizons for control error and manipulated signal are used because of the slow processes which take place in the petrochemical industry. In order to reduce the comput...
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For predictive control in industry often very long horizons for control error and manipulated signal are used because of the slow processes which take place in the petrochemical industry. In order to reduce the computational effort some commercial predictive control program packages offer the ability to reduce the number of points in both horizons but do not recommend how to select the points which have to be considered in the horizon of the control error and manipulated variable. In this work the authors introduce an optimal choice not only of the horizon lengths itself but also for the strategy of reducing the number of points in the horizons. A genetic optimization algorithm was used both for the search for the optimal length of the horizons and for the best allocation of the points in the horizons. The results of the optimization process where used to deduct a simple rule.
Most industrial processes are nonlinear. In such a case only a nonlinear model valid for the whole working area can ensure a good controller design. The nonlinear process is approximated by a multi-model consisting of...
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Most industrial processes are nonlinear. In such a case only a nonlinear model valid for the whole working area can ensure a good controller design. The nonlinear process is approximated by a multi-model consisting of the intelligent combination of some linear sub-models. As a very practical way the following identification strategy was used: independent model parameter estimation in the different working points and the calculation of the global valid model output as the weighted sum of the sub-models. As a weighting function the Gaussian function is used. The parameters of the Gaussian function were chosen either without or with optimization of the identification cost function. The global valid nonlinear model was used for model based predictive control. A heat exchanger example illustrates the method.
The paper describes a special system that makes it possible to generate electric and heat energy by utilising alternative, renewable and waste energies, in most cases, applying the principle of co-generation. The solu...
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