An Intelligent Fuzzy-PID Controller for Supporting Comfort Microclimate in Smart Homes

Fuzzy PID control is a process control technique combining the advantages of fuzzy logic and PID control. It maintains precise control over processes by combining fuzzy logic and proportional-integral-derivative (PID) controllers. With this method, it is possible to achieve higher levels of accuracy and stability in controlling process variables such as temperature, pressure, flow rate, and position. This makes Fuzzy PID control an ideal cordial application requiring precise process control. Fuzzy PID control uses the PID parameter tuning experience to make the fuzzy controller automatically tune its parameters, giving PID control a better control effect. In this paper, a fuzzy PID controller is designed to control the temperature of an electric boiler. The control effects of PID control, fuzzy control, and fuzzy PID control are compared by simulation analysis. The simulation results show that the unclear PID control effect is ideal and has a good application prospect.

This paper investigates the integration of Fuzzy Logic with PID (Proportional, Integral, Derivative) controllers to enhance the performance of temperature control systems. Traditional PID controllers, widely used in various applications, often encounter difficulties with nonlinear, time-varying, and uncertain systems, resulting in performance issues like oscillations and overshoots. To mitigate these challenges, the adoption of Fuzzy PID control, which improves adaptability, robustness, and responsiveness by incorporating fuzzy logic into the control mechanism. The study involves simulations using Simulink to compare three distinct control models: conventional PID, standalone Fuzzy Control, and the hybrid Fuzzy PID Control across standardized control scenarios. Our results show that Fuzzy PID controllers significantly outperform traditional methods, especially in complex environments lacking precise mathematical modeling. The enhanced system response times and stability demonstrated by Fuzzy PID controllers suggest substantial potential for broader application across diverse industries, promising considerable improvements in operational efficiency and reliability.

In industrial production, temperature and humidity are common main manipulated parameters, especially in heat treatment industry, the temperature control is becoming more and more important. This paper from two aspects that hardware and software introduced single-chip microcomputer (SCM) C8051F intelligent temperature and humidity control system, and described schematic diagram of hardware and software. The design adds the integration of carbon dioxide concentration and light intensity detection and necessary communication function. It is a more humanized and more practical intelligent temperature and humidity measurement and control system.

ABSTRACTFuzzy logic control is the most common method utilized to tune proportional integral derivative (PID) controller parameters online. However, proportional integral derivative controllers often perform poorly in the control of nonlinear and/or complicated systems, such as direct current motors, where the model parameters are not exactly known if the scaling factors are not properly selected besides the membership function and rule sets in a fuzzy logic controller design. Finding the most suitable scaling factors for complex systems where the model parameters are not exactly known or nonlinear systems is a challenging task. Furthermore, traditional trial and error techniques of determining appropriate scaling factors are experience based, time consuming, and may not always provide optimal response. In this paper, a particle swarm optimization algorithm is suggested for optimizing the input and output gains of the fuzzy PID controller. The robustness and effectiveness of the suggested controller was validated using MATLAB/Simulink. The performance of the suggested controller is compared with the Ziegler Nichols and Particle Swarm Optimization Algorithm tuned PIDs, and fuzzy PID controllers. The simulation result show that the fuzzy PID controller whose scaling factor was tuned using particle swarm optimization outperforms the other controllers in avoiding disturbance and has a better trajectory tracking capability.

The paper deals with technical problems of automated decentralised indoor climate control in residential buildings. The lead value for the realisation of the control system is the objective thermal comfort. The measurement system to be designed works as a network between a large variety of different sensors and actuators. A comprehensive and flexible EIB networked instrumentation using cheap commercial and prototype sensors and actuators has been created and tested in a realistic test environment. In the paper, the conditions for a modern room climate control are discussed and the technical base for data acquisition system containing several different data collection channels is presented. Extensive measurements with the reliable, fully redundant data acquisition system showed its applicability in residential buildings. Based on the presented concept, different systems configurations can be realised. Following the results, a low­cost decentralised automated room climate control system is able to become a new standard in low­energy buildings, opening a huge market potential.

PID and fuzzy PID controller are applied into the wind turbine with nonlinear mathematical model. PID control has simple principle and its parameters setting are rather easy. Fuzzy control need not to establish the mathematical of the control system and has strong robustness. Fuzzy PID control is combined with PID control and fuzzy control. The advantages of fuzzy PID control are simple, easy in setting parameters and strong robustness. Fuzzy PID controller combines with closed-loop optimal fuzzy reasoning (COFR), which can effectively improve the robustness, when the robustness is special requirement. MATLAB software is used for simulations, results display that fuzzy PID controller which combines with COFR has better performances than PID controller when errors exist, especially robustness performance.

Net-zero Nation: HVAC and PV Systems for Residential Net-Zero Energy Buildings across the United States

This paper presents evaluation of the energy consumption and tracking performance associated with the use of a recently introduced dual-mode model predictive controller (DMMPC) for control of a heating, ventilation, and air conditioning (HVAC) system. The study was conducted using detailed simulations of an HVAC system, which included a multizone air loop, a water loop, and a chiller. Energy consumption and tracking performance are computed from the simulations and evaluated in the presence of different types and magnitudes of noise and disturbances. Performance of the DMMPC is compared with a baseline proportional-integral-derivative (PID) control structure commonly used for HVAC system control, and this comparison showed clear and consistent superiority of the DMMPC.

<div class="section abstract"><div class="htmlview paragraph">Electric Vehicles (EVs) are rapidly transforming the automotive landscape, offering a cleaner and more sustainable alternative to internal combustion engine vehicles. As EV adoption grows, optimizing energy consumption becomes critical to enhancing vehicle efficiency and extending driving range. One of the most significant auxiliary loads in EVs is the climate control system, commonly referred to as HVAC (Heating, Ventilation, and Air Conditioning). HVAC systems can consume a substantial portion of the battery's energy—especially under extreme weather conditions—leading to a noticeable reduction in vehicle range.</div><div class="htmlview paragraph">This energy demand poses a challenge for EV manufacturers and users alike, as range anxiety remains a key barrier to widespread EV acceptance. Consequently, developing intelligent climate control strategies is essential to minimize HVAC power consumption without compromising passenger comfort. These strategies may include predictive thermal management, cabin pre-conditioning, zonal climate control, and integration with renewable energy sources. By implementing such energy-efficient solutions, EVs can achieve better range performance, improved user satisfaction, and greater environmental benefits.</div><div class="htmlview paragraph">Modern EV climate control systems increasingly rely on intelligent features such as Auto mode, which dynamically adjusts fan speed, airflow direction, and temperature settings based on real-time cabin and ambient conditions. By leveraging sensor data and adaptive control algorithms, Auto mode optimizes thermal comfort while minimizing unnecessary energy expenditure. This automated regulation plays a crucial role in reducing HVAC-related power consumption, thereby contributing to overall range improvement and enhancing system efficiency without compromising passenger comfort.</div><div class="htmlview paragraph">This study focuses on the development of three distinct Auto mode calibration levels for each set condition, designed to achieve the same cabin temperature with varying dynamic responses and energy consumption profiles. In Auto mode, the cabin temperature is regulated through intelligent control of compressor speed, blower speed, and evaporator temperature. While all Auto levels can maintain the desired setpoint, the time required to reach this temperature and the system’s responsiveness to sudden thermal loads can vary significantly. This study introduces three distinct calibration profiles, each engineered to achieve the same cabin temperature under different dynamic conditions and energy consumption levels. These profiles allow users to choose between faster thermal response or reduced power usage, effectively enabling a trade-off between immediate comfort and extended driving range</div></div>

Managing thermal comfort in contemporary high-rise residential buildings: Using smart thermostats and surveys to identify energy efficiency and comfort opportunities

The U.S. energy Information Administration (EIA) estimates that about 94.8 quadrillion Btu of energy is produced from various energy sources like petroleum, natural gas, coal, renewable, and nuclear power plants. Out of this total generated energy, mostly from non-renewable sources, about 12 quadrillion Btu energy is consumed by residential buildings and about 9.50 quadrillion Btu of energy is consumed by commercial building. Some estimates go further to state that about 40% of all energy generated are used by the building sector. Out of all building system components, HVAC (Heating, Ventilation and Air Conditioning) equipment consume significant portion of this energy. Some studies estimate that about 30-50% of the energy consumed by a building is used by the HVAC system. The data obtained from energy simulation during preliminary design phase of the building is used for the HVAC system sizing. Some studies estimate about 50-150% discrepancy between actual and predicted energy usage is observed because most of the building simulation do not take dynamic Occupant Behavior (OB) changes into consideration. In this study, we propose to integrate the OB predicting Q-Lattice model into EnergyPlus. The Q-Lattice model is trained using the newly introduced python package called ‘feyn’. Unlike other black box models like DNN and LSTM, the Q-Lattice model is fully interpretable as it describes the relationship between dependent and independent variables using the basic mathematical operations. To train the model, we collected data from 16 residential dorms located at Upstate New York for two years. We propose to train two Q-Lattice models; the first model estimates the HVAC energy usage independent of EnergyPlus. The second model estimates the window opening behavior of the students. The second model will be integrated into EnergyPlus using EnergyPlus API to simulate the HVAC energy usage during the winter months in residential dormitories. The second model was also integrated with the infiltration and window airflow prediction DNN model developed in past study. After that, the HVAC energy usage will be evaluated using EnergyPlus with Q-Lattice embedded inside it.

MF^2: Model-free reinforcement learning for modeling-free building HVAC control with data-driven environment construction in a residential building

<div class="htmlview paragraph">Unique challenges arise during the design of temperature and humidity control systems (THCS) for use in microgravity. The design of the Plant Research Unit’s (PRU) THCS builds on the experience gained during the Biomass Production System (BPS) project and extends the understanding of the critical design variables and necessary technical advancements to allow for longer on-orbit operation. Previous systems have been limited by loss of prime, clogging in the porous plates and component reliability. Design of THCSs for long-duration space flight experiments requires the mitigation of these issues as well as a complete understanding of the relevant design variables. In addition to the normal design variables (e.g. mass, power, volume), a complex and interdependent relationship exists between the THCS variables including operational temperature range, operational humidity range, required humidity condensation rate and system air flow.</div>

DSTATCOM is a shunt compensation device connected at Point of Common Coupling (PCC) of Distribution System to deliver quality power. The proportional & integral controller (PI) of DSTATCOM needs tuning at the time of installation as well as due to change of network parameters and working conditions, can provide a slower response. To ensure plug and play feature to the device with faster response Proportional Integral Derivative (PID) action with fuzzy interference system (FIS) is implemented in this paper. With respect to SRF theory based simple PI controller a better response is obtained with fuzzy PID controller in terms of dynamic voltage profile and Total Harmonic Distortion (THD).

Oyster mushroom is one type of mushroom that is commonly consumed by Indonesian people which is relatively easy to cultivate. It can produce the optimum fruit body at temperatures below 30°C, the range of humidity is 60–90% with altitudes between 500–1000 meters above sea level. This paper describes on the development of the prototype of temperature and humidity control system in oyster mushroom cultivation using a fuzzy logic controller. The prototype comprises of DHT11 sensor for detecting temperature and humidity, Arduino microcontroller, mistmaker, and cooling fan. Fuzzy logic controller is installed in the Arduino microcontroller to generate an actuating signal for the mistmaker and cooling fan based on fuzzy inputs. The whole fuzzy system is designed using two inputs namely temperature and humidity with each input consist of three fuzzy sets with the triangular membership function. Meanwhile, the output variable is the speed of cooling fan with three fuzzy sets and triangular membership functions. A Mamdani inference system is adopted to achieve the control objective in the whole system. Based on the appropriate rule bases, fuzzy logic controllers are designed to produce the temperature and humidity setpoint around 27°C and 60–90 % respectively. Experimental results show the time response to achieve the setpoint span from 1 minute 27 seconds until 8 minutes 10 seconds.