Predictive control dead-time processes

One of the possible approaches to control of dead-time processes is application of predictive control methods. In technical practice often occur higher order processes when a design of an optimal controller leads to complicated control algorithms. One of the possibilities of control of such processes is their approximation by lower-order model with dead-time (time-delay). The first part of the paper deals with a design of an algorithm for predictive control of high-order processes which are approximated by a second-order model of the process with time-delay. The second part of the paper deals with a design of an analogical algorithm for predictive control of multivariable processes with time-delay. The predictive controllers are based on the recursive computation of predictions which was extended for the time-delay system. The designed control algorithms were verified by simulation. © 2017, World Scientific and Engineering Academy and Society. All rights reserved

Predictive control of multivariable time-delay systems

In technical practice often occur multivariable processes with time delay. Time-delays are mainly caused by the time required to transport mass, energy or information, but they can also be caused by processing time or accumulation. In a multivariable system each input may influence all system outputs. The design of a controller for such a system must be quite sophisticated if the system is to be controlled adequately. One of the possible approaches to control of multivariable time-delay processes is application of predictive control methods. The paper deals with design of an algorithm for predictive control of multivariable processes with time-delay. The predictive controller is based on the recursive computation of predictions which was extended for the time-delay system. The control of a multivariable system with two steps of time-delay was verified by simulation. © The Authors, published by EDP Sciences, 2017

Modeling and supervisory control design for a combined cycle power plant

The traditional control strategy based on PID controllers may be unsatisfactory when dealing with processes with large time delay and constraints. This paper presents a supervisory model based constrained predictive controller (MPC) for a combined cycle power plant (CCPP). First, a non-linear dynamic model of CCPP using the laws of physics was proposed. Then, the supervisory control using the linear constrained MPC method was designed to tune the performance of the PID controllers by including output constraints and manipulating the set points. This scheme showed excellent tracking and disturbance rejection results and improved performance compared with a stand-alone PID controller’s scheme

Continuous-time anti-windup generalized predictive control of uncertain processes with input constraints and time delays

In this paper, a design problem of a continuous-time anti-windup generalized predictive control (CAGPC) system using coprime factorization approach for uncertain processes with input constraints and time delays is considered. The uncertainty of the process is considered as an uncertain time delay. To reduce the effect of the input constraint and uncertain delay, controller for strong stability of the closed-loop system is designed. As a practical appeal, the effectiveness of the proposed design scheme is confirmed by a simulated application to an industrial process with input constraint and uncertain time delay.</p

Neural Network Based Min-Max Predictive Control. Application to a Heat Exchanger

IFAC Adaptation and Learning in Control and Signal Processing. Cemobbio-Como. Italy. 2001Min-max model predictive controllers (MMMPC) have been proposed for the control of linear plants subject to bounded uncertainties. The implementation of MMMPC suffers a large computational burden due to the numerical optimization problem that has to be solved at every sampling time. This fact severely limits the class of processes in which this control is suitable. In this paper the use of a Neural Network (NN) to approximate the solution of the min-max problem is proposed. The number of inputs of the NN is determined by the order and time delay of the model together with the control horizon. For large time delays the number of inputs can be prohibitive. A modification to the basic formulation is proposed in order to avoid this later problem. Simulation and experimental results are given using a heat exchanger

INDUSTRIAL INSTRUMENTATION FOR CONTROL OF CHEMICAL PROCESSES

Automation and instrumentation are of extreme importance for industrial processes, as these require control in the manufacture of their products. The milk pasteurization process presents challenging control problems, including non-linear dynamic behavior and multivariable interaction, making it very difficult to control milk and water temperature by conventional on-offcontrollers, since temperature is an important variable for ensure the quality of milk. To improve the milk pasteurisation process, the model predictive control (MPC) non-linear is suggested. This model show superior performance in keeping milk and water temperatures at the desired set points without any oscillation and overshoot compared to other controllers, as is the case ofgeneric model control (GMC). Another very common problem in the chemical industry are the processes with a large time delay. In view of, that the proportional-integral-derivative (PID) controller does not present good performance due to the large time delay and model/plant mismatches, it is recommended to use a new controller, which consists of the combination ofdynamic matrix control (DMC) algorithm with the PID control, to control the residual oil outlet temperature of an industrial coke furnace. This combination supports the DMC control performance and the simple structure of PID control. The method performance is achieved in terms of set point tracking and disturbance rejection. Many control algorithms are tested to eliminatethis problem, but are limited by the cost, hardware and complexity

Rejection of mismatched disturbances for systems with input delay via a predictive extended state observer

[EN] The problem of output stabilization and disturbance rejection for input-delayed systems is tackled in this work. First, a suitable transformation is introduced to translate mismatched disturbances into an equivalent input disturbance. Then, an extended state observer is combined with a predictive observer structure to obtain a future estimation of both the state and the disturbance. A disturbance model is assumed to be known but attenuation of unmodeled components is also considered. The stabilization is proved via Lyapunov-Krasovskii functionals, leading to sufficient conditions in terms of linear matrix inequalities for the closed-loop analysis and parameter tuning. The proposed strategy is illustrated through a numerical example.PROMETEOII/2013/004; Conselleria d'Educacio; Generalitat Valenciana, Grant/Award Number: TIN2014-56158-C4-4-P-AR; Ministerio de Economia y Competitividad, Grant/Award Number: FPI-UPV 2014; Universitat Politecnica de ValenciaSanz Diaz, R.; García Gil, PJ.; Fridman, E.; Albertos Pérez, P. (2018). Rejection of mismatched disturbances for systems with input delay via a predictive extended state observer. International Journal of Robust and Nonlinear Control. 28(6):2457-2467. https://doi.org/10.1002/rnc.4027S24572467286Stability and Stabilization of Systems with Time Delay. (2011). IEEE Control Systems, 31(1), 38-65. doi:10.1109/mcs.2010.939135Fridman, E. (2014). Introduction to Time-Delay Systems. Systems & Control: Foundations & Applications. doi:10.1007/978-3-319-09393-2Watanabe, K., & Ito, M. (1981). A process-model control for linear systems with delay. 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Automatica, 58, 131-138. doi:10.1016/j.automatica.2015.05.013Basturk, H. I. (2017). Cancellation of unmatched biased sinusoidal disturbances for unknown LTI systems in the presence of state delay. Automatica, 76, 169-176. doi:10.1016/j.automatica.2016.10.006Sanz, R., Garcia, P., Albertos, P., & Zhong, Q.-C. (2016). Robust controller design for input-delayed systems using predictive feedback and an uncertainty estimator. International Journal of Robust and Nonlinear Control, 27(10), 1826-1840. doi:10.1002/rnc.3639Mondie, S., & Michiels, W. (2003). Finite spectrum assignment of unstable time-delay systems with a safe implementation. IEEE Transactions on Automatic Control, 48(12), 2207-2212. doi:10.1109/tac.2003.820147Zhong, Q.-C. (2004). On Distributed Delay in Linear Control Laws—Part I: Discrete-Delay Implementations. IEEE Transactions on Automatic Control, 49(11), 2074-2080. doi:10.1109/tac.2004.837531Zhou, B., Lin, Z., & Duan, G.-R. (2012). 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Flexible Implementation of Model Predictive Control Using Sub-Optimal Solutions

The on-line computational demands of model predictive control (MPC) often prevents its application to processes where fast sampling is necessary. This report presents a strategy for reducing the computational delay resulting from the on-line optimization inherent in many MPC formulations. Recent results have shown that feasibility, rather than optimality, is a prerequisite for stabilizing MPC algorithms, implying that premature termination of the optimization procedure may be valid, without compromising stability. The main result included in the report is a termination criterion for the on-line optimization algorithm giving rise to a sub-optimal, yet stabilizing, MPC algorithm. The termination criterion, based on an associated delay-dependent cost index, quantifies the trade-off between successively improved control profiles resulting form the optimization algorithm and the potential performance degradation due to increasing computational delay. It is also shown how the cost index may be used in a dynamic scheduling application, where the processor time is shared between two MPC tasks executing on the same CPU

Analisis Perbandingan Supervisory Predictive Control (SPC) Untuk Konfigurasi Cascade Dan Parallel Pada Performansi Sistem Pengaturan Proses Dengan Variabel Waktu Tunda

Banyak sistem yang memiliki fenomena waktu tunda (time delay) dalam dinamikanya. Pada sistem pengaturan proses waktu tunda sering terjadi yang disebabkan oleh bentuk fisik plant, aksi kontroler dan operasi aktuator. Sistem yang mengalami waktu tunda dapat menyebabkan sistem tersebut menjadi time varying. Adanya waktu tunda didalam suatu sistem kontrol umpan balik akan menjadi kendala yang serius dalam mencapai performansi yang baik. Kontrol prediktif merupakan metode kontrol yang populer dalam menangani sistem dengan dinamika yang lambat. Penerapan metode ini dapat dilakukan dalam dua level yaitu regulatory dan supervisory. Pada penelitian ini, akan dibandingkan penerapan dari kontroler PI dan prediktif pada level regulatory dan supervisory dengan konfigurasi cascade dan parallel. Penerapan dari keempat metode tersebut akan dilakukan pada sistem pengaturan level PCT-100. Penerapan sistem kontrol prediktif ini mampu menghilangkan efek dari waktu tunda dan memperbaiki performansi sistem dengan mempercepat respon. Konfigurasi cascade pada kontroler prediktif yang berada pada level supervisory mendapatkan hasil yang paling bagus yaitu memiliki konstanta waktu paling kecil sebesar 1.026 detik saat pengujian perubahan set point. ================================================================================================================== Many processes include time delay phenomena in their inner dynamics. In process control system time delay usually happen because measuring, controller action, and actuator operation, which are possibly time-varying. Thus there is an increasing interest in studying time delay systems in all scientific areas, especially in control engineering. Predictive control is a popular technique to control slow dynamical systems. This controller can applied in two level that are regulatory and supervisory. In this research, comparison between PI and Predictive controller in level regulatory and supervisory with cascade and parallel configuration will analyze. Implementation of this control method will be applied to level control systems PCT-100. Predictive controller can eliminate the impact of time delay and improve system performance with fast time constant. Predictive control in supervisory level with cascade configuration has the best response with the smallest time constant 1.206 second when set point variation test