Predictive control of multizone heating,ventilation and air-conditioning systems in non-residential buildings

In France, buildings account for a large part of the energy consumption and carbon emissions. Both are mainly due to heating, ventilation and air-conditioning (HVAC) systems. Because older, oversized or poorly maintained systems may be using more energy and costing more to operate than necessary, new management approaches are needed. In addition, energy efficiency can be improved in central heating and cooling systems by introducing zoned operation. So, the present work deals with the predictive control of multizone HVAC systems in non-residential buildings. First, a real non-residential building located in Perpignan (south of France) has been modelled using the EnergyPlus software. We used the predicted mean vote (PMV) index as a thermal comfort indicator and developed low-order ANN-based models to be used as controller's internal models. A genetic algorithm allowed the optimization problem to be solved. In order to appraise the proposed management strategy, it has been compared to basic scheduling techniques. Using the proposed strategy, the operation of all the HVAC subsystems is optimized by computing the right time to turn them on and off, in both heating and cooling modes. Energy consumption is minimized and thermal comfort requirements are met. So, the simulation results highlight the pertinence of a predicitive approach for multizone HVAC systems management.     

基于人体热舒适性指标PMV的暖通空调控制器

分析了人类对暖通空调系统的要求,介绍了暖通空调控制的现状,提出了一种新的基于人体热舒适性指标PMV的暖通空调控制器,该控制器能满足人类对暖通空调系统健康、舒适和节能的要求,是一种理想的暖通空调控制器。     

Low computational cost technique for predictive management of thermal comfort in non-residential buildings

In France, non-residential buildings account for a significant part of energy consumption. A large part of this consumption is due to HVAC (Heating, Ventilation and Air-Conditioning) systems, which are in most cases poorly handled. The present work deals with an efficient approach allowing energy consumption to be minimized while still ensuring thermal comfort. We propose a predictive control strategy for existing zoned HVAC systems and consider the PMV (Predicted Mean Vote) index as a thermal comfort indicator. In order to test this strategy, we modelled a non-residential building located in Perpignan (south of France) using the EnergyPlus software. The twofold aim is to limit the times during which the HVAC sub-systems are turned on and to ensure a satisfactory thermal comfort when people are working in the considered building. This predictive approach, computationally tractable, allows thermal comfort requirements to be met without wasting energy.     

Gain Scheduling Control applied to Thermal Barrier in systems of indirect passive heating and cooling of buildings

A critical point for a new Thermal Barrier technique of indirect heating and cooling of buildings under construction is to maintain a constant temperature during the entire year. Such an effect can be obtained with the use of the proposed Gain Scheduling Control (GSC) system which implements a novel Fuzzy-Mixing Gain-Scheduling (FMGS) strategy that is based on the idea of fuzzy mixing (weighting) of local (modal) values of certain (automatically designed) control parameters. An important advantage of this approach is that the same scheme can be used both for scheduling controller gains and for fuzzy mixing the supply fluids in a temperature optimization procedure. Experiments show both excellent performance and effectiveness of the proposed HVAC control system.     

Support vector regression model predictive control on a HVAC plant

Some industrial and scientific processes require simultaneous and accurate control of temperature and relative humidity. In this paper, support vector regression (SVR) is used to build the 2-by-2 nonlinear dynamic model of a HVAC system. A nonlinear model predictive controller is then designed based on this model and an optimization algorithm is used to generate online the control signals within the control constraints. Experimental results show good control performance in terms of reference command tracking ability and steady-state errors. This performance is superior to that obtained using a neural fuzzy controller.     

Predictive functional control based on fuzzy T-S model for HVAC systems temperature control

In heating, ventilating and air-conditioning (HVAC) systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno (T-S) fuzzy model was proposed to control HVAC systems. The T-S fuzzy model of stabilized controlled process was obtained using the least squares method, then on the basis of global linear predictive model from T-S fuzzy model, the process was controlled by the predictive functional controller. Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model. Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness. Compared with the conventional PID controller, this control strategy has the advantages of less overshoot and shorter setting time, etc.     

Predictive functional control based on fuzzy T-S model for HVAC systems temperature control

In heating, ventilating and air-conditioning （HVAC） systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno （T-S） fuzzy model was proposed to control HVAC systems. The T-S fuzzy model of stabilized controlled process was obtained using the least squares method, then on the basis of global linear predictive model from T-S fuzzy model, the process was controlled by the predictive functional controller. Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model. Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness. Compared with the conventional PID controller, this control strategy has the advantages of less overshoot and shorter setting time, etc.     

新型模糊PID控制及在HVAC系统的应用

为了推广模糊控制器在非线性系统中的应用,提出一种利用PID控制器的参数优化和调节模糊控制器的新型设计方法.通过模糊控制器的结构分析建立与PID控制之间的精确解析关系之后提出基于PID控制增益因子的模糊控制器设计算法,然后利用改进的变论域思想进一步优化模糊控制器设计参数.将其应用于暖通空调(HVAC)系统的节能控制中并与常规PID控制器相比较,仿真和实验结果表明这种模糊控制器具有超调量小、跟踪迅速、鲁棒性强等优越的控制性能.     

Multiple fuzzy model-based temperature predictive control for HVAC systems

In this paper, a multiple model predictive control (MMPC) strategy based on Takagi–Sugeno (T–S) fuzzy models for temperature control of air-handling unit (AHU) in heating, ventilating, and air-conditioning (HVAC) systems is presented. The overall control system is constructed by a hierarchical two-level structure. The higher level is a fuzzy partition based on AHU operating range to schedule the fuzzy weights of local models in lower level, while the lower level is composed of a set of T–S models based on the relation of manipulated inputs and system outputs correspond to the higher level. Following this divide-and-conquer strategy, the complex nonlinear AHU system is divided into a set of T–S models through a fuzzy satisfactory clustering (FSC) methodology and the global system is a fuzzy integrated linear varying parameter (LPV) model. A hierarchical MMPC strategy is developed using parallel distribution compensation (PDC) method, in which different predictive controllers are designed for different T–S fuzzy rules and the global controller output is integrated by the local controller outputs through their fuzzy weights. Simulation and real process testing results show that the proposed MMPC approach is effective in HVAC system control applications.     

A multivariable predictive fuzzy PID control system

In this paper, a novel multivariable predictive fuzzy-proportional-integral-derivative (F-PID) control system is developed by incorporating the fuzzy and PID control approaches into the predictive control framework. The developed control system has two main units referred as adaptation and application parts. The adaptation part consists of a F-PID controller and a fuzzy predictor. The incremental control actions are generated by the F-PID controller. The controller parameters are adjusted with the predictive control approach. The fuzzy predictor provides the multi-step ahead predictions of the plant outputs. Therefore, the F-PID controller parameters are adjusted by minimizing the errors between the predicted plant outputs and reference trajectories over the prediction horizon. The fuzzy predictor is trained with an on-line training procedure in order to adapt the changes in the plant dynamics and improve the prediction accuracy. The Levenberg–Marquardt (LM) optimization method with a trust region approach is used to adjust both the controller and predictor fuzzy systems parameters. In the application part, an identical F-PID controller of the adaptation part is used to control the actual plant. The adjusted parameter values are transferred to this identical controller at each time step. The performance of the proposed control system is tested for both single-input single-output (SISO) and multiple-input multiple-output (MIMO) nonlinear control problems. The adaptation, robustness to noise, disturbance rejection properties together with the tracking performances are examined in the simulations.     

Adaptive fuzzy model based inverse controller design using BB-BC optimization algorithm

The use of inverse system model as a controller might be an efficient way in controlling non-linear systems. It is also a known fact that fuzzy logic modeling is a powerful tool in representing nonlinear systems. Therefore, inverse fuzzy model can be used as a controller for controlling nonlinear plants. In this context, firstly, a new fuzzy model based inverse controller design methodology is presented in this study. The design methodology introduced here is based on a recursive optimization procedure that searches for an optimal inverse model control signal at every sampling time. Since the task of optimization should be accomplished in between two sampling periods the use of a fast optimization algorithm becomes essential. For this reason, Big Bang-Big Crunch (BB-BC) optimization algorithm is used due to its low computational time and high global convergence properties. Even though, inverse model controllers may produce perfect control while operating in an open loop fashion, this open loop control would not be sufficient in the case of modeling mismatches or disturbances that might occur over the system. In order to overcome this problem, secondly, an on-line adaptation mechanism via BB-BC optimization algorithm is introduced in addition to BB-BC optimization based fuzzy model inverse controller. The adaptation mechanism is used to update the related parameters of the model while minimizing the absolute value of the instantaneous error between the system and model outputs. In this manner, the system output is somehow fed back, the overall control form can be considered as a closed-loop system. The new fuzzy model based inverse control scheme with the new online adaptation mechanism has been implemented and tested on the two real time processes; namely, heat transfer and pH processes and very satisfactory results has been reported.     

基于改进遗传算法的模糊控制器设计

针对模糊控制器的隶属度函数和模糊控制规则的选取及优化缺乏自学习能力与知识采集的手段,以及遗传算法具有自适应、启发式、概率性、迭代式全局收敛的特点,该文章将遗传算法与模糊控制相结合,给出了一种基于改进遗传算法的模糊控制器设计策略.改进算法引入了分裂算子来避免遗传算法在寻优过程中陷入局部最优解,同时对编码方式、选择算子、交叉算子以及变异算子做了相应的调整与改进.并将此改进算法用于优化模糊控制器的隶属度函数与模糊控制规则.仿真结果表明用该改进算法优化后的模糊控制器较用普通遗传算法优化后的模糊控制器具有更好的控制性能.     

Online tuning fuzzy PID controller using robust extended Kalman filter

Fuzzy PID controllers have been developed and applied to many fields for over a period of 30 years. However, there is no systematic method to design membership functions (MFs) for inputs and outputs of a fuzzy system. Then optimizing the MFs is considered as a system identification problem for a nonlinear dynamic system which makes control challenges. This paper presents a novel online method using a robust extended Kalman filter to optimize a Mamdani fuzzy PID controller. The robust extended Kalman filter (REKF) is used to adjust the controller parameters automatically during the operation process of any system applying the controller to minimize the control error. The fuzzy PID controller is tuned about the shape of MFs and rules to adapt with the working conditions and the control performance is improved significantly. The proposed method in this research is verified by its application to the force control problem of an electro-hydraulic actuator. Simulations and experimental results show that proposed method is effective for the online optimization of the fuzzy PID controller.     

An intelligent HVAC control strategy for supplying comfortable and energy-efficient school environment

In South Korea, school buildings require significant energy inputs for heating and air-conditioning, and the majority of the occupants are adolescent students, whose health and cognitive performance are vulnerable to poor indoor air quality (IAQ) and thermal discomfort. Using field measurements, some previous studies have reported that some Korean schools have poor IAQ and thermal conditions. Thus, it is necessary to develop effective heating, ventilation, and air-conditioning (HVAC) control strategies to improve the indoor environment and reduce energy consumption. Therefore, this study proposes an intelligent HVAC integrated control strategy that can improve indoor environmental quality (IEQ) and reduce energy consumption in school buildings. The proposed strategy utilizes an integrated neural network prediction model for IEQ and a heuristic method that can optimize control objectives (i.e., the predicted mean vote [PMV], carbon dioxide [CO₂], particulate matter with diameters of 10 and 2.5 μm [PM₁₀ and PM_2.5, respectively], and HVAC energy consumption). To evaluate the control performance of the proposed strategy, the present study employs two base algorithms (i.e., a rule-based and a non-adaptive control approach) under non-disturbance and forcing disturbance scenarios. The control failure period for PMV is found to be 1.6420% and 9.4773% of the total occupancy period under the non-disturbance and forcing disturbance scenarios, respectively, while CO₂ control failure does not occur under either scenario. The control failure periods for PM₁₀ and PM_2.5 were 5.1676%, and 7.1844%, respectively, under forcing disturbance. Under the non-disturbance scenario, the proposed strategy consumed 2,467.07 kWh and 870,26 kWh for heating and cooling, respectively, representing 91.1% and 84.08% of that for the rule-based algorithm. The proposed strategy can thus effectively improve the IEQ of a building and has the potential for use in the development of integrated environmental management solutions for buildings.     

基于模糊逻辑与遗传算法的燃料电池热管理方法研究

有效的质子交换膜燃料电池(Proton Exchange Membrane Fuel Cell,PEMFC)热管理是提升氢燃 料电池汽车安全性、耐久性以及运行效率的关键因素之一。该文提出一种 PEMFC 电堆热管理控制方法,使电堆出入口温度保持在设定值。该方法以 PEMFC 热管理系统模型中电堆出入口温度的变化为依据,设计一种二维模糊控制器,并应用遗传算法优化模糊控制器的隶属度函数,从而使模糊控制器的控制精度更高。为验证所提出方法的有效性,该文选用 Autonomie 软件中的一款氢燃料电池汽车,在两种标准工况上进行 PEMFC 热管理方法验证。仿真结果显示,经过遗传算法优化后的模糊控制器相对于未优化的模糊控制器具有更高的控制精度,电堆出入口温度与设定值的偏差更低。     

Optimized fuzzy PD cascade controller: A comparative analysis and design

In this study, we develop a design methodology for a fuzzy PD cascade controller for a ball & beam system by using particle swarm optimization (PSO). The ball & beam system is a well-known control engineering experimental setup, which consists of servo motor, beam, and ball. This system exhibits a number of interesting and challenging properties when being considered from the control perspective. The ball & beam system determines the position of ball through the control of a servo motor. The displacement change of the position of ball leads to the change of the angle of the beam which determines the position angle of a servo motor. Consequently the variation of the position of the moving ball and the ensuing change of the angle of the beam results in the change of the position angle of a servo motor. We introduce a fuzzy PD cascade controller scheme which consists of the outer (1st) controller and the inner (2nd) controller arranged in a cascaded architecture. Auto-tuning of the parameters of the controller (scaling factors) as well as fuzzy rules of each fuzzy PD controller is realized with the use of the PSO. Moreover the comparative analysis of results of optimization realized by PSO and GA based on SGA (Serial Genetic Algorithms) is discussed from the viewpoint of control performance. The set-point value of the inner controller (the 2nd controller) corresponds to the position angle of a servo motor, and is given as reference value, which enters into the inner controller as the 2nd controller of the two cascaded controllers. The optimization process takes advantage of a rapid convergence of PSO being used here as a generic search mechanism. A detailed comparative analysis carried out from the viewpoint of the performance and the design methodology, is provided for the fuzzy PD cascade controller and the conventional PD cascade controller whose design exploited serial genetic algorithms.     

Effective digital implementation of fuzzy control systems based on approximate discrete-time models

This paper addresses an effective digital implementation of fuzzy control systems via an intelligent digital redesign (IDR) approach. The purpose of IDR is to effectively convert an existing continuous-time fuzzy controller to an equivalent sampled-data fuzzy controller in the sense of the state-matching. The authors show that, under reasonable assumptions, the IDR based on the exact discrete-time models can be reduced to the IDR based on the approximate discrete-time models. The state-matching error between the closed-loop trajectories is carefully analyzed using the integral quadratic functional approach. The estimation of the state-matching error is presented using the linear matrix inequality (LMI) techniques. The problem of designing the sampled-data fuzzy controller to minimize the estimation as well as to guarantee the stability is formulated and solved as the convex optimization problem with LMI constraints. It is also shown that the resulting sampled-data fuzzy controller recovers the performance of the continuous-time fuzzy controller as the sampling period approaches zero. A numerical example is used to demonstrate the effectiveness of the proposed design technique.     

基于改进量子粒子群算法的NCS模糊控制器参数优化

针对网络化控制系统中模糊控制器的量化因子和比例因子采用传统经验方法难以整定的问题, 提出了一种改进量子粒子群（IQPSO）算法对模糊控制器量化因子和比例因子进行优化。该方法将ABC算法中的搜索算子作为变异算子引入到QPSO算法中, 使得IQPSO算法较好地克服了QPSO算法保持种群多样性差容易早熟收敛的缺陷, 并以ITAE指标作为IQPSO算法的适应度函数对模糊控制器进行优化。典型工业过程仿真结果表明, IQPSO优化的模糊控制器具有比PID控制器和标准QPSO优化的模糊控制器更好的控制性能和适用性。     

基于T-S模型的倒立摆最优保性能模糊控制

对一类具有范数有界参数不确定性T-S模糊模型系统,采用状态反馈的并行分布补偿器(PDC)结构,基于线性矩阵不等式处理方法,研究了其最优保性能模糊控制律的设计问题.导出了保性能模糊控制律存在的条件,通过求解一个凸优化问题给出了最优保性能模糊控制律的设计方法,并用此方法设计了倒立摆系统的最优保性能模糊控制器.仿真实验验证了该设计方法的有效性.     

Single-chip fuzzy logic controller design and an application on a permanent magnet dc motor

This paper describes a low-cost single-chip PI-type fuzzy logic controller design and an application on a permanent magnet dc motor drive. The presented controller application calculates the duty cycle of the PWM chopper drive and can be used to dc–dc converters as well. The self-tuning capability makes the controller robust and all the tasks are carried out by a single chip reducing the cost of the system and so program code optimization is achieved. A simple, but effective algorithm is developed to calculate numerical values instead of linguistic rules. In this way, external memory usage is eliminated. The contribution of this paper is to present the feasibility of a high-performance non-linear fuzzy logic controller which can be implemented by using a general purpose microcontroller without modified fuzzy methods. The developed fuzzy logic controller was simulated in MATLAB/SIMULINK. The theoretical and experimental results indicate that the implemented fuzzy logic controller has a high performance for real-time control over a wide range of operating conditions.