Soft computing based controllers for automotive air conditioning system with variable speed compressor

The inefficient On/Off control for the compressor operation has long been regarded as the major factor contributing to energy loss and poor cabin temperature control of an automotive air conditioning (AAC) system. In this study, two soft computing based controllers, namely the proportional-integral-derivative (PID) based controllers tuned using differential evolution (DE) algorithm and an adaptive neural network based model predictive controller (A-NNMPC), are proposed to be used in the regulation of cabin temperature through proper compressor speed modulation. The implementation of the control schemes in conjunction with DE and neural network aims to improve the AAC performance in terms of reference tracking and power efficiency in comparison to the conventional On/Off operation. An AAC experimental rig equipped with variable speed compressor has been developed for the implementation of the proposed controllers. The dynamics of the AAC system is modelled using a nonlinear autoregressive with exogenous inputs (NARX) neural network. Based on the plant model, the PID gains are offline optimized using the DE algorithm. Experimental results show that the DE tuned PID based controller gives better tracking performance than the Ziegler-Nichols tuning method. For A-NNMPC, the identified NARX model is incorporated as a predictive model in the control system. It is trained in real time throughout the control process and therefore able to adaptively capture the time varying dynamics of the AAC system. Consequently, optimal performance can be achieved even when the operating point is drifted away from the nominal condition. Finally, the comparative assessment indicates clearly that A-NNMPC outperforms its counterparts, followed by DE tuned PID based controller and the On/Off controller. Both proposed control schemes achieve up to 47% power saving over the On/Off operation, indicating that the proposed control schemes can be potential alternatives to replace the On/Off operation in an AAC system

플라즈마 식각 장치를 위한 적응모델예측제어기의 설계

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 화학생물공학부(에너지환경 화학융합기술전공), 2019. 2. 이윤우.The semiconductor etching process, which is one of the most critical processes in the manufacturing of semiconductors and one that comprises numerous steps, requires higher sophistication as 10 nm semiconductors are mass produced. Currently, the semiconductor etching process is mostly done by physical and/or chemical etching with plasma. In addition, the plasma etching is getting increasingly popular with the miniaturization of the process to a scale of less than 10 nm. The result of a plasma etching process is represented in the form of an etch profile which is determined by the plasma variables. Therefore, the performance of the process depends on these variables, and it is essential to measure and control them in real time. Although research on the control of plasma etching processes has been actively carried out, the plasma etching process strongly relies on the experience and skill level of seasoned engineers at the industry level. This is because a plasma-based system is very complicated and sensitive, and has a time-varying characteristics. However, even though previous studies show excellent results, they employed invasive diagnostic tools, and have single variable control schemes where a counter change of another plasma variable during control actions for other variables might occur due to the highly interactive plasma characteristics. Moreover, they did not consider the time-varying characteristics of plasma-based systems. Therefore, this thesis proposes a multivariable control strategy which can cope with interaction effects and a design of an adaptive model predictive controller which maintains its performance wherein systems vary with time. At first, the plasma variables which are considered as controlled variables were selected as the electron density and the electron temperature. This is because one of the etch profile, especially etch rate, can be expressed as functions of those plasma variables and the variables can be measured by the optical emission spectroscopy which is a non-invasive diagnostic tool. The plasma variables were paired with instrumental variables through singular value decomposition and relative gain array for constructing the optimal multivariable system model. Two parallel proportional integral derivative (PID) controllers were designed based on the output errors then conducted plasma variable control simulations. Through the simulations, the exist of interaction between the variables was obviously verified. For resolving the interaction effectively, decoupler controllers were applied to the PID controllers. As it performed the control experiment of the Ar plasma electron density and electron temperature excellently, the possibility of multivariable control of plasma-based system is demonstrated. In spite of the meaningful control results using the PID controllers, there are obvious limitations in relation to the exquisiteness and to the characteristics of decoupler controllers as it highly dependent to the accuracy of the system model. In order to maintain performance even in the case of a system change, an advanced control strategy is required and model predictive control can be an alternative. Therefore, a model predictive controller was designed where the Bayesian optimization is used as tuning method for the maximization of the exquisiteness. The controller verified its capability as it conducted Ar plasma electron density control in a drift-free system. However, the performance of it deteriorated in control simulations of time-varying systems and in a control experiment performed in a system where O2 plasma was injected into an Ar plasma system inducing the high nonlinearity. Therefore, a more advanced control strategy which can overcome the difficulty was required. In an adaptive control method, once the information from the system is entered into the adjustment mechanism part, the part makes a decision to deliver it to the controller. Then the controller is modified in accordance with the decision and releases the optimal control action. The typical adaptive control algorithm, which is the recursive least squares algorithm, was used in this thesis. Using the algorithm with Kalman filter interpretation, the time-delay effect which comes from the plasma etching reactor can be considered. The recursive model parameter estimator utilizing this algorithm was tuned by Bayesian optimization. When the recursive model parameter estimator detects changes of the system model parameters in real time and transmits it to the model predictive controller, the controller calculates the optimal manipulated variable based on the modified model. The adaptive model predictive controller performed the same simulations and experiment as those performed by the model predictive controller. Unlike the model predictive controller, the proposed controller performed control excellently even when the system changes over time. Numerically, it showed the improved control ability by 24.7% and 30.4% in terms of the mean absolute percentage error and the number of deviated sample, respectively compared to the conventional model predictive controller. These results demonstrate that the adaptive model predictive controller designed in this theses is invaluable for plasma-based system control and is the effective controller for the plasma etching reactor. It is expected to make a significant contribution to plasma-based processes that require high sophistication and flexibility.수 많은 공정으로 이루어진 반도체 제조 공정 내에서 가장 큰 비중을 차지하고 있는 반도체 식각 공정은 최근 10 nm급 반도체의 양산이 이뤄짐에 따라 식각의 높은 정교성이 요구되고 있다. 반도체 식각 공정은 현재 산업계에선 플라즈마를 이용하여 물리적, 화학적 식각을 일으키는 방법을 통해 이루어지고 있으며, 공정이 10 nm 급 이하 스케일로 미세화된 후로 이 방법이 더욱 각광 받고 있다. 공정의 결과는 식각 프로필을 기준으로 결정되는 데 이 식각 프로필이 플라즈마 변수들에 크게 의존함이 입증됨에 따라 이 변수들을 실시간으로 측정하고 제어하는 것이 공정의 핵심이 되었다. 그동안 플라즈마 변수 제어에 관한 연구들이 활발히 진행되어 왔으나 아직까지 산업계에선 그 이론들을 바로 활용하지 못하고 경험 많은 엔지니어의 감에 의존하고 있다. 그 이유는 근본적으로 시스템이 매우 복잡하고 예민할 뿐 아니라 시간에 따라 변하는 특성을 갖고 있기 때문이다. 그러나 이전의 연구들은 훌륭한 결과를 보였음에도 불구하고, 플라즈마 시스템에 직접적으로 영향을 미치는 침투성 센서를 이용했거나, 플라즈마 변수들과 장치 변수들이 서로 복잡하게 얽혀 있어 야기되는 상호작용을 간과할 수밖에 없는 단변수 제어를 수행한 데에 그치고 있다. 게다가 외란 때문에 발생되는 시간에 따라 변하는 특성을 고려하지 못하고 있다. 따라서, 본 학위논문에서는 변수간 상호작용을 최소화하는 다변수 제어 전략과 시스템이 시간에 따라 변하는 상황에서도 성능이 악화되지 않는 적응모델예측제어기의 설계를 제안한다. 먼저, 전자 밀도와 전자 온도가 제어 대상이 되는 플라즈마 변수로 선정되었다. 이는 식각 프로필, 특히 식각률이 이 변수들에 대한 함수로 표현될 수 있으며, 이 변수들은 침투성 센서인 광학적 발광 분광법을 통해 측정될 수 있기 때문이다. 그 다음에, 최적의 다변수 시스템 정의를 위해 특이치 분석과 상대이득배열을 이용하여 가장 효과적으로 제어를 수행할 수 있는 장치 변수 선정이 이루어졌다. 이를 바탕으로 두 개의 병렬로 연결된 비례적분미분제어기를 설계, 아르곤 플라즈마 전자 밀도와 전자 온도의 제어 시뮬레이션을 수행하였다. 해당 시뮬레이션에서 변수들 간 상호 작용이 확연함을 입증하였으며 이를 효과적으로 해결하기 위해 디커플러 제어기가 비례적분미분제어기에 결합되었다. 이 제어기는 아르곤 플라즈마의 전자 밀도와 전자 온도 제어를 훌륭하게 수행함으로써 다변수 플라즈마 시스템의 제어 가능성을 분명하게 입증하였다. 다변수 플라즈마 시스템의 제어 가능성이 입증 됐음에도 불구하고, 이 제어 전략은 비례적분미분제어기의 정교성 측면에서의 한계와 디커플러 제어기의 시스템 모델에 대한 높은 의존도 특성으로 인한 한계가 존재한다. 시스템이 변하는 상황에서도 성능을 유지하기 위해선 더욱 수준 높은 제어 전략이 요구되며, 모델예측제어가 그 대안이 될 수 있다. 모델예측제어기의 설계는 제어의 정교성을 극대화 시키기 위해 베이시안 최적화 기법을 통해 이루어졌다. 이 모델예측제어기는 인위적인 외란이 적용되지 않은 순수 아르곤 플라즈마 시스템에서의 전자 밀도 제어를 훌륭하게 수행함으로써 그 성능을 입증하였다. 하지만, 시스템이 시간에 따라 변하는 상황을 모사한 시뮬레이션과 산소 플라즈마가 아르곤 플라즈마 시스템에 주입되어 시스템 변화를 야기시키는 상황에서 수행된 제어 실험에서 제어기의 성능이 확연히 악화됨을 확인할 수 있었다. 따라서 이를 극복할 수 있는 더욱 발전된 제어 전략이 요구되었다. 적응 제어 기법은 시스템에서 얻어진 정보를 조절 메커니즘 부분으로 보내 실시간으로 제어기의 수정 사항을 결정하여 이를 바탕으로 제어를 수행하는 기법이다. 본 학위논문에서는 대표적인 적응 제어 알고리즘인 순환형 최소자승법 알고리즘을 활용하였다. 이 알고리즘에 칼만 필터 해석을 접목시킴에 따라, 플라즈마 식각 장치로부터 비롯되는 시간 지연의 효과를 고려할 수 있게 하였다. 이 알고리즘이 탑재된 순환형 모델 파라미터 추정기는 베이시안 최적화 기법을 통해 튜닝되었다. 순환형 모델 파라미터 추정기가 실시간으로 감지하는 모델 파라미터의 변화를 모델예측제어기에 전달하면 수정된 모델을 기반으로 제어기는 최적의 조절 변수를 계산한다. 이렇게 설계된 적응모델예측제어기는 앞서 모델예측제어기가 수행한 것과 동일한 시뮬레이션과 실험을 수행하였다. 모델예측제어기와 달리 적응모델예측제어기는 시간에 따라 시스템이 변하는 상황에서도 훌륭한 제어를 수행하였으며, 평균절대오차율을 기준으로 했을 때 기존의 모델예측제어기보다 24.7%의 향상된 제어 성능을 보여주었다. 이 결과는 본 학위논문에서 제안하고 있는 적응모델예측제어기가 시스템의 변화가 빈번한 플라즈마 시스템에서의 제어에 매우 가치 있음과 더불어 플라즈마 식각 장치에 유효한 제어기라는 것을 반증한다. 이 결과가 플라즈마 기반 시스템을 대상으로 하는 모든 제어 공정의 발전에 크게 이바지할 것을 기대하는 바이다.Abstract i Contents v List of Figures viii List of Tables xii CHAPTER 1. Introduction 1 1.1. Research motivation 1 1.2. Research objectives 4 1.3. Description of the equipment used in this thesis 5 1.4. Outline of the thesis 9 CHAPTER 2. Design of Multi-Input Multi-Output Controller for Plasma-based System 10 2.1. Introduction 10 2.2. Theoretical backgrounds 13 2.2.1. Estimation of plasma variables from optical emission spectroscopy 13 2.2.2. The influence of oxygen in plasma etching reactor 16 2.2.3. Singular value decomposition and condition number 18 2.2.4. Relative gain array method 21 2.2.5. Multi-loop control system 23 2.3. MIMO control results in the Ar plasma system 31 2.3.1. Variable selection and pairing 31 2.3.2. Disturbance rejection control results for SISO systems 37 2.3.3. Simulation of multi-loop control scheme and decoupling control scheme 41 2.3.4. Set-point tracking control experiment of multi-loop controller with decoupler controllers 58 2.4. Concluding remarks 62 CHAPTER 3. Disturbance Rejection Control of Plasma Electron Density by Model Predictive Controller 64 3.1. Introduction 64 3.2. Model predictive control 68 3.2.1. Concept of model predictive control 68 3.2.2. Description of model predictive control algorithm 71 3.2.2.1. State estimation algorithm 71 3.2.2.2. Optimization algorithm 76 3.3. Design of model predictive controller for Ar plasma system 78 3.3.1. System identification of Ar plasma system 78 3.3.2. Optimal MPC weight parameters from integral squared error based Bayesian optimization 80 3.3.3. Experimental results of Ar plasma electron density control 84 3.4. Disturbance rejection control using model predictive controller 86 3.4.1. Development of time-varying system model for control simulation 86 3.4.2. Design of model predictive controller for disturbance rejection control 91 3.4.3. Experimental result of disturbance rejection control in Ar/O2 plasma system 101 3.5. Concluding remarks 104 CHAPTER 4. Design of Adaptive Model Predictive Controller for Plasma Etching Reactor 106 4.1. Introduction 106 4.2. Recursive model parameter estimation 112 4.2.1. Recursive least squares algorithm with forgetting factor 113 4.2.2. Recursive least squares algorithm with Kalman filter interpretation 116 4.3. Adaptive model predictive control algorithm 119 4.4. Time-varying system control using adaptive model predictive controller 123 4.4.1. Initial system identification (Scaling method) 123 4.4.2. Design of adaptive model predictive controller for time-varying system 125 4.4.3. Set-point tracking control results in drifted system 143 4.5. Concluding remarks 152 CHAPTER 5. Conclusion 154 5.1. Summary of contributions 154 5.2. Future work 157 Nomenclature 159 References 167 Abstract in Korean (국문초록) 174Docto

Analysis and Application of Advanced Control Strategies to a Heating Element Nonlinear Model

open4siSustainable control has begun to stimulate research and development in a wide range of industrial communities particularly for systems that demand a high degree of reliability and availability (sustainability) and at the same time characterised by expensive and/or safety critical maintenance work. For heating systems such as HVAC plants, clear conflict exists between ensuring a high degree of availability and reducing costly maintenance times. HVAC systems have highly non-linear dynamics and a stochastic and uncontrollable driving force as input in the form of intake air speed, presenting an interesting challenge for modern control methods. Suitable control methods can provide sustainable maximisation of energy conversion efficiency over wider than normally expected air speeds and temperatures, whilst also giving a degree of “tolerance” to certain faults, providing an important impact on maintenance scheduling, e.g. by capturing the effects of some system faults before they become serious.This paper presents the design of different control strategies applied to a heating element nonlinear model. The description of this heating element was obtained exploiting a data driven and physically meaningful nonlinear continuous time model, which represents a test bed used in passive air conditioning for sustainable housing applications. This model has low complexity while achieving high simulation performance. The physical meaningfulness of the model provides an enhanced insight into the performance and functionality of the system. In return, this information can be used during the system simulation and improved model based and data driven control designs for tight temperature regulation. The main purpose of this study is thus to give several examples of viable and practical designs of control schemes with application to this heating element model. Moreover, extensive simulations and Monte Carlo analysis are the tools for assessing experimentally the main features of the proposed control schemes, in the presence of modelling and measurement errors. These developed control methods are also compared in order to evaluate advantages and drawbacks of the considered solutions. Finally, the exploited simulation tools can serve to highlight the potential application of the proposed control strategies to real air conditioning systems.openTurhan, T.; Simani, S.; Zajic, I.; Gokcen Akkurt, G.Turhan, T.; Simani, Silvio; Zajic, I.; Gokcen Akkurt, G

Development, implementation and performance evaluation of a self-tuning regulator adaptive controller applied to a FCC process

Orientador: Rubens Maciel FilhoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia QuímicaResumo: Este trabalho teve como principal objetivo o desenvolvimento e implementação de um controlador adaptativo do tipo regulador auto-ajustável (STR - Self Tuning Regulator), com a subsequente comparação de seu desempenho com um controlador PID (proporcionalintegrativo-derivativo) e dois controladores preditivos: um preditivo baseado em redes neurais artificiais e um controlador DMC (Dynamic Matrix Control). Esses esquemas de controle foram todos implementados na ferramenta de simulação desenvolvida, o FCCGUI (Fluid Catalytic Cracking Graphical User Interface). Como modelo para estimativa dos parâmetros do controlador adaptativo foi treinada e validada uma rede neural. Esse modelo caixa-preta forneceu uma abordagem eficiente para identificação e controle não-linear do processo de craqueamento catalítico. Para implementação do controlador adaptativo foram estruturadas três novas malhas de controle PID a partir de estudos estatísticos desenvolvidos para a análise dos efeitos das variáveis de processo e suas interações. Dentre essas novas malhas de controle, optou-se pela implementação do controle adaptativo no par manipulada-controlada CTCV-SEVER (abertura de catalisador regenerado - severidade da reação). Após aperfeiçoamentos e reestruturações no simulador FCCGUI, foram realizadas várias simulações para avaliação gráfica e numérica do desempenho do controlador através do critério de desempenho dinâmico ITAE (Integral of Time and Absolute Error). O controlador adaptativo apresentou bons resultados, tanto para testes servo quanto para regulatórios em comparação com a estratégia PID sem adaptação, bem como para as demais estratégias disponíveis no simulador, MPC-RNA (Model Predictive Control baseado em uma Rede Neural Artificial) e DMC. A capacidade de ajuste dos parâmetros do controlador torna-o uma estratégia promissora para sistemas que sofrem com alterações contínuas em suas variáveis de processo ou mudanças de setpointAbstract: This work had as main objective the development and implementation of an selftuning regulator (STR) adaptive controller, with subsequent comparison of its performance with a PID (proportional-integral-derivative) controller and two predictive controllers, namely a predictive based on artificial neural networks (MPC-ANN) and a dynamic matrix controller (DMC). These control schemes were all implemented in the developed simulation tool, the FCCGUI - Fluid Catalytic Cracking Graphical User Interface. An artificial neural network, used as a model to estimate controller parameters, was trained and validated. This black box model provided an efficient approach for identification and nonlinear control of the catalytic cracking process. To implement the adaptive controller, three new PID control loops were structured based on statistical studies designed to analyze the effects of process variables and their interactions. The implementation of adaptive control was chosen to be in the manipulated-controlled pair CTCV-SEVER (regenerated catalyst valve opening - reaction severity). After restructuring and improvements in the simulator FCCGUI, several simulations were performed for graphical and numerical evaluation of controller performance through ITAE (Integral of Time and Absolute Error) dynamic performance criterion. The adaptive controller presented good results for both tests: servo and regulatory, in comparison with PID strategy without adaptation and other strategies available to the simulator, MPC-ANN and DMC. The ability to adjust the parameters of the controller makes it a promising strategy for systems that suffer from continuous changes in their process variables or setpointsDoutoradoDesenvolvimento de Processos QuímicosDoutor em Engenharia Químic

Setpoint Tracking Predictive Control in Chemical Processes Based on System Identification

A Kraft recovery boiler in a pulp-paper mill provides a means for recovery of the heat energy in spent liquor and recovery of inorganic chemicals while controlling emissions. These processes are carried out in a combined chemical recovery unit and steam boiler that is fired with concentrated black liquor and which produces molten smelt. Since the recovery boiler is considered to be an essential part of the pulp-paper mill in terms of energy resources, the performance of the recovery boiler has to be controlled to achieve the highest efficiency under unexpected disturbances. This dissertation presents a new approach for combining system identification technique with predictive control strategy. System identification is the process of building mathematical models of dynamical systems based on the available input and output data from the system. Predictive control is a strategy where the current control action is based upon a prediction of the system response at some number of time steps into the future. A new algorithm uses an i-step-ahead predictor integrated with the least-square technique to build the new control law. Based on the receding horizon predictive control approach, the tracking predictive control law is achieved and performs successfully on the recovery boiler of the pulp-paper mill. This predictive controller is designed from ARX coefficients that are computed directly from input and output data. The character of this controller is governed by two parameters. One parameter is the prediction horizon as in traditional predictive control and the other parameter is the order of the ARX model. A recursive version of the developed algorithm can be evolved for real-time implementation. It includes adaptive tuning of these two design parameters for optimal performance. The new predictive control is proven to be a significant improvement compared to a conventional PID controller, especially when the system is subjected to noise and disturbances

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

Robust predictive control for respiratory CO2 gas removal in closed-loop mechanical ventilation: an in-silico study

In this study a physiological closed-loop system for arterial CO2 partial pressure control was designed and comprehensively tested using a set of models of the respiratory CO2 gas exchange. The underlying preclinical data were collected from 12 pigs in presence of severe changes in hemodynamic and pulmonary condition. A minimally complex nonlinear state space model of CO2 gas exchange was identified post hoc in different lung conditions. The control variable was measured noninvasively using the endtidal CO2 partial pressure. For the simulation study the output signal of the controller was defined as the alveolar minute volume set value of an underlying adaptive lung protective ventilation mode. A linearisation of the two-compartment CO2 gas exchange model was used for the design of a model predictive controller (MPC). It was augmented by a tube based controller suppressing prediction errors due to model uncertainties. The controller was subject to comparative testing in interaction with each of the CO2 gas exchange models previously identified on the preclinical study data. The performance was evaluated for the system response towards the following five tests in comparison to a PID controller: recruitment maneuver, PEEP titration maneuver, stepwise change in the CO2 production, breath-hold maneuver and a step in the reference signal. A root mean square error of 2.69 mmHg between arterial CO2 partial pressure and the reference signal was achieved throughout the trial. The reference-variable response of the model predictive controller was superior regarding overshoot and settling time

A Robust Adaptive Dead-Time Compensator with Application to A Solar Collector Field

This paper describes an easy-to-use PI controller with dead-time compensation that presents robust behaviour and can be applied to plants with variable dead-time. The formulation is based on an adaptive Smith predictor structure plus the addition of a filter acting on the error between the output and its prediction in order to improve robustness. The implementation of the control law is straightforward, and the filter needs no adjustment, since it is directly related to the plant dead-time. An application to an experimentally validated nonlinear model of a solar plant shows that this controller can improve the performance of classical PID controllers without the need of complex calculations.Ministerio de Ciencia y Tecnología TAP95-37