The application of a new PID autotuning method for the steam/water loop in large scale ships

In large scale ships, the most used controllers for the steam/water loop are still the proportional-integral-derivative (PID) controllers. However, the tuning rules for the PID parameters are based on empirical knowledge and the performance for the loops is not satisfying. In order to improve the control performance of the steam/water loop, the application of a recently developed PID autotuning method is studied. Firstly, a 'forbidden region' on the Nyquist plane can be obtained based on user-defined performance requirements such as robustness or gain margin and phase margin. Secondly, the dynamic of the system can be obtained with a sine test around the operation point. Finally, the PID controller's parameters can be obtained by locating the frequency response of the controlled system at the edge of the 'forbidden region'. To verify the effectiveness of the new PID autotuning method, comparisons are presented with other PID autotuning methods, as well as the model predictive control. The results show the superiority of the new PID autotuning method

A robust PID autotuning method for steam/water loop in large scale ships

During the voyage of the ship, disturbances from the sea dynamics are frequently changing, and the ship's operation mode is also varied. Hence, it is necessary to have a good controller for steam/water loop, as the control task is becoming more challenging in large scale ships. In this paper, a robust proportional-integral-derivative (PID) autotuning method is presented and applied to the steam/water loop based on single sine tests for every sub-loop in the steam/water loop. The controller is obtained during which the user-defined robustness margins are guaranteed. Its performance is compared against other PID autotuners, and results indicate its superiority

Nonlinear predictive control applied to steam/water loop in large scale ships

In steam/water loop for large scale ships, there are mainly five sub-loops posing different dynamics in the complete process. When optimization is involved, it is necessary to select different prediction horizons for each loop. In this work, the effect of prediction horizon for Multiple-Input Multiple-Output (MIMO) system is studied. Firstly, Nonlinear Extended Prediction Self-Adaptive Controller (NEPSAC) is designed for the steam/water loop system. Secondly, different prediction horizons are simulated within the NEPSAC algorithm. Based on simulation results, we conclude that specific tuning of prediction horizons based on loop’s dynamic outperforms the case when a trade-off is made and a single valued prediction horizon is used for all the loops

Effect of control horizon in model predictive control for steam/water loop in large-scale ships

This paper presents an extensive analysis of the properties of different control horizon sets in an Extended Prediction Self-Adaptive Control (EPSAC) model predictive control framework. Analysis is performed on the linear multivariable model of the steam/water loop in large-scale watercraft/ships. The results indicate that larger control horizon values lead to better loop performance, at the cost of computational complexity. Hence, it is necessary to find a good trade-off between the performance of the system and allocated or available computational complexity. In this original work, this problem is explicitly treated as an optimization task, leading to the optimal control horizon sets for the steam/water loop example. Based on simulation results, it is concluded that specific tuning of control horizons outperforms the case when only a single valued control horizon is used for all the loops

A low computational cost, prioritized, multi-objective optimization procedure for predictive control towards cyber physical systems

Cyber physical systems consist of heterogeneous elements with multiple dynamic features. Consequently, multiple objectives in the optimality of the overall system may be relevant at various times or during certain context conditions. Low cost, efficient implementations of such multi-objective optimization procedures are necessary when dealing with complex systems with interactions. This work proposes a sequential implementation of a multi-objective optimization procedure suitable for industrial settings and cyber physical systems with strong interaction dynamics. The methodology is used in the context of an Extended Prediction self-adaptive Control (EPSAC) strategy with prioritized objectives. The analysis indicates that the proposed algorithm is significantly lighter in terms of computational time. The combination with an input-output formulation for predictive control makes these algorithms suitable for implementation with standardized process control units. Three simulation examples from different application fields indicate the relevance and feasibility of the proposed algorithm

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Improved PID controller based on piecewise affine function in data driven control framework

In recent years, with the rapid developments of science and technology, practical applications in various fields such as chemical, machinery, electronics and electricity industries have caused the process to become more complex. This subsequently causes the modelling of the plant using first principles or system identification to become more difficult. In general, the PID controller has been successfully applied in various applications. However, the PID gains which are proportional

Controle de nível de tanques interativos baseados em técnicas de redes neurais artificiais

Orientador: Ana Maria Frattini FiletiDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia QuímicaResumo: O controle de nível de tanques interativos a partir da vazão é um sistema MIMO (multiple input multiple output), que envolve uma série de desafios como não linearidades acentuadas, interação entre as variáveis do processo e tempos mortos e, por isso, nem sempre pode ser controlado por técnicas de controle convencionais como o PID. Rede neurais artificiais (RNA) são uma técnica de processamento paralelo capaz de capturar relações bastante não lineares entre várias variáveis de entradas e várias variáveis de saídas. Dessa forma, diversas técnicas de controle utilizando RNA tem sido propostas para processos em que o controle feedback tradicional possa não funcionar satisfatoriamente. O presente trabalho visava testar a viabilidade experimental de duas técnicas de controle baseadas em redes neurais aplicadas no controle de nível em tanques interativos: o controle preditivo baseado em redes neurais (MPC-RNA), que consiste em utilizar um modelo neural do processo e um algoritmo de otimização para obter uma performance satisfatória; e o controle neural inverso, que é uma técnica de controle baseada na predição da variável manipulada diretamente das variáveis controladas. Além disso, o trabalho também visava comparar a performance das duas técnicas mencionadas com a performance do controlador PID convencional. Os experimentos foram realizados no sistema de tanques interativos do Laboratório de controle e automação (LCAP) na Unicamp. Ambos os níveis dos tanques acoplados eram controlados a partir da manipulação das potências das duas bombas que regulavam as vazões. Uma válvula intermediária manual conectava os tanques e gerava não linearidades, bem como interação entre os níveis, o que dificultava o controle PID. A aplicação experimental das três técnicas mencionadas foi feita por meio de um programa desenvolvido em MATLAB® e um CLP foi utilizado para fazer a aquisição dos dados da planta. Uma comparação entre as duas técnicas de controle baseadas em redes neurais mostrou que o controle neural inverso não foi capaz de seguir o setpoint satisfatoriamente, já que a técnica deixou um offset. Enquanto isso, a técnica MPC-RNA foi capaz de seguir o setpoint mais rapidamente e com menores overshoots do que o PID. A performance melhor do MPC-RNA em relação ao PID pode ser atribuída a capacidade do algoritmo de controle preditivo de minimizar os desvios entre a saída desejada e predita, e a habilidade das redes neurais artificiais de lidar com não linearidades e interação entre variáveis manipuladas e controladas. Além disso, o controlador MPC-RNA acopla a estratégia feedback e feedforward, dessa forma, compensando desvios entre o valor real e o valor predito a partir do modelo distúrbioAbstract: The level control of interactive tanks adjusting flow rates is a multiple input multiple output (MIMO) system that poses many challenges in the control problem, such as nonlinearities, interactions between manipulated and process variables and dead times. Therefore, conventional techniques such as the Proportional Integral Derivative (PID) controller might not work properly in this process. Artificial neural network (ANN) is a parallel processing technique that can capture highly nonlinear relationships among input and output variables. Hence, some control techniques that use ANN have been proposed for processes in which traditional feedback techniques may not work properly. This work aimed to test the experimental feasibility of two control techniques based on artificial neural networks applied to level control in coupled tanks: the model predictive control based on neural modeling (MPC-ANN) and an inverse neural network control. In the first strategy, an artificial neural network model of the process and an optimization algorithm are used to derive a satisfactory error performance. The second one is a control technique based on predicting the manipulated variables straight from the measurements of the process variables. Moreover, this work aimed to compare the performance of the two techniques mentioned with the conventional PID. The experiments were carried out using interactive tanks set up in of the Laboratory of Control and Automation at the University of Campinas (UNICAMP). Both levels of coupled tanks were to be controlled by manipulating the power of the two pumps that regulates output flow rates. An intermediate manual valve connected the tanks, generating nonlinearities and interaction between the levels, which made the success of PID control more difficult. The experimental application of the three mentioned techniques was performed with algorithm developed in MATLAB® and using a PLC to acquire the plant data. The comparison between the two-control neural network control techniques showed that the inverse neural control was not capable to track the set-point satisfactorily since it left an offset while the MPC-ANN was capable to track the set-point faster than the PID and it left smaller overshoots than the PID. The MPC-ANN performed better than the PID due to the capacity of model predictive control algorithm to minimize the deviations between the desired and predicted outputs, and the ability of artificial neural networks to deal with nonlinearities and interactions between manipulated and controlled variables. Besides, MPC-ANN couples feedback and feedforward strategy so it compensates model plant mismatches with the disturbance modelMestradoEngenharia QuímicaMestre em Engenharia Química1776016CAPE

Sistema inteligente para o controle de pressão De redes de distribuição de água abastecidas Por bombas associadas em paralelo

The objective of this research is to develop an intelligent system based on artificial neural networks for water distribution systems that operate with pumps associated in parallel. The system aims at process automation and the definition of operating state for electric motors (on, off or with partial rotation), aiming at the same time the pressure control and reduction of electric power consumption. The developed intelligent system is a generic one, which allows the application of control structure in similar processes, and it was applied in a fully instrumented test rig that emulates a real system of water supply. The results showed that the performance of the artificial neural network is quite satisfactory, and thus can be successfully implemented in other similar water distribution systems in order to reduce consumption of water and electric energy, decrease costs of maintenance, and increase the degree of reliability of operational procedures.O objetivo desta pesquisa é desenvolver um sistema inteligente baseado em redes neurais artificiais para redes de distribuição de água que operam com bombas associadas em paralelo. O sistema tem por finalidade a automação do processo e a definição do estado de funcionamento dos motores elétricos (ligado, desligado ou com rotação parcial), visando simultaneamente ao controle de pressão e à redução do consumo de energia elétrica. O sistema inteligente desenvolvido é genérico, o que permite a aplicação da estrutura de controle em processos semelhantes, e foi aplicado em uma bancada experimental totalmente instrumentalizada que emula um sistema real de abastecimento de água. Os resultados mostraram que o desempenho da rede neural artificial é bastante satisfatório, e, assim, poderá ser implementada com sucesso em outros sistemas de distribuição de água similares, a fim de proporcionar redução do consumo de água e energia elétrica, diminuição dos custos de manutenção e aumento do grau de confiabilidade dos procedimentos operacionais