Adaptive Smart Cat Feeding System Based on ESP32 Using Fuzzy Logic and IoT Monitoring

Smog hanging over cities is the most familiar and obvious form of air pollution. The effects of inhaling particulate matter have been studied in humans and animals and include asthma, lung cancer, cardiovascular issues, and premature death. There are, however, some additional products of the combustion process that include nitrogen oxides and sulfur and some un-combusted hydrocarbons, depending on the operating conditions and the fuel-air ratio. Tuning the fuel to air ratio caused to control the lung cancer. Lung cancers are tumors arising from cells lining the airways of the respiratory system. Design of a robust nonlinear controller for automotive engine can be a challenging work. This research paper focuses on the design and analysis of a high performance PID like fuzzy controller for automotive engine, in certain and uncertain condition. The proposed approach effectively combines of design methods from linear Proportional-Integral-Derivative (PID) controller and fuzzy logic theory to improve the performance, stability and robustness of the automotive engine. To solve system’s dynamic nonlinearity, the PID fuzzy logic controller is used as a PID like fuzzy logic controller. The PID like fuzzy logic controller is updated based on gain updating factor. In this methodology, fuzzy logic controller is used to estimate the dynamic uncertainties. In this methodology, PID like fuzzy logic controller is evaluated. PID like fuzzy logic controller has three inputs, Proportional (P), Derivative (D), and Integrator (I), if each inputs have N linguistic variables to defined the dynamic behavior, it has N × N × N linguistic variables. To solve this challenge, parallel structure of a PD-like fuzzy controller and PI-like fuzzy controller is evaluated. In the next step, the challenge of design PI and PD fuzzy rule tables are supposed to be solved. To solve this challenge PID like fuzzy controller is replaced by PD-like fuzzy controller with the integral term in output. This method is caused to design only PD type rule table for PD like fuzzy controller and PI like fuzzy controller.

This paper presents an analysis of multi stage fuzzy logic control application for load frequency control of isolated wind-diesel hybrid power system. Due to the sudden load changes and intermittent wind power, large frequency fluctuation problem can occur. An effective controller for stabilizing frequency oscillations and maintaining the system frequency within acceptable range is significantly required. The load frequency control (LFC) deviates the frequency deviation and maintains dynamic performance of the system. As fuzzy logic control approach can be easily implemented in practical systems, the fuzzy logic control has been applied to design LFC system. In this paper, multi stage Fuzzy logic PID controller is proposed for Load Frequency Control (LFC) of an isolated wind-diesel hybrid power system. Simulations are performed for this hybrid system with the proposed multi stage Fuzzy Logic PID controller, conventional PI controller and Fuzzy logic controller with different load disturbances and wind input disturbances. The performance of the proposed approach is verified from simulations and comparisons. Simulation results explicitly show that the performance of the proposed multi stage Fuzzy Logic PID Controller is superior to the conventional PI controller and Fuzzy logic controller in terms of overshoot, settling time and steady state error against various load changes and variations of wind inputs.

Attempts are being made to enhance the drive performance by intelligent control using fuzzy logic (FL) and neural network techniques. One of the frequently discussed applications of artificial intelligence in control is the replacement of a standard proportional plus integral (PI) speed controller with an FL or artificial neural network (ANN) speed controller. Regardless of all the work, it appears that a thorough comparison of the drive behavior under PI, FL, and ANN speed control is necessary. This article attempts to compare PI, fuzzy, and ANN controllers that are implemented in an embedded system for closed-loop speed control of DC drive fed by a buck-type DC–DC power converter. The PI controller is designed based on the small signal modeling of the system. The PI-like fuzzy controller structure is considered for comparison. Two ANN controllers are designed. One controller uses training data obtained from the simulation of a fuzzy controller and the other uses training data from the simulation of a PI controller. The performance of the controllers is studied for a variety of operating conditions, such as step change in speed command and step change in load torque. The parameters selected for the comparison are the steady-state error and the rise time of the response. It is shown that ANN speed controllers provide a superior speed response in terms of rise time and the steady-state error compared to PI and FL controllers. This advantage arises from the fact that the neural network has the property of generalization and the control surface of the neural controller is smooth. The designed neural network controller is simple, with three neurons only, and so it is best suited for embedded system implementation. It is also found that the ANN controller trained with the training data from a PI controller has a better response compared to the ANN controller trained with data from a fuzzy controller.

This study proposes an integrated approach to developing a Microservice, Cloud Computing, and Software as a Service (SaaS)-based Real-Time Storm Sewer Simulation System (MBSS). The MBSS combined the Storm Water Management Model (SWMM) microservice running on the EC2 Amazon Web Services (AWS) cloud platform and an Internet of Things (IoT) monitoring device to prevent disasters in smart cities. The Python language and Docker container were used to develop the MBSS and Web API of the SWMM microservice. The IoT comprised a pressure water level meter, an Arduino, and a Raspberry Pi. After laboratory channel testing, the simulated and IoT-monitored water levels under different flow rates indicate that the simulated water level in MBSS was such as that monitored by the IoT. These findings suggest that MBSS is feasible and can be further used as a reference for smart urban early warning systems. The MBSS can be applied in on-site stormwater sewers during heavy rain, with the goal of issuing early warnings and reducing disaster damage. The use case can be the process by which the SWMM model parameters will be optimized based on the water level data from IoT monitoring devices in stormwater sewer systems. The predicted rainfall will then be used by the SWMM microservices of MBSS to simulate the water levels at all manholes. The status of the water levels will finally be applied to early warning.

The integration of fuzzy logic controllers in automatic vehicle navigation systems represents a significant advancement in intelligent transportation systems, especially when paired with Internet of Things (IoT) functionalities and optimized through genetic algorithms. This innovative fusion harnesses the precision of fuzzy logic, the connectivity of IoT, and the optimization capabilities of genetic algorithms to transform automatic vehicle navigation. Fuzzy logic controllers excel in managing uncertainty and imprecision, providing decision-making capabilities akin to human reasoning. By simultaneously assessing multiple inputs and determining actions based on degrees of truth, fuzzy logic enables safe and efficient navigation in dynamic driving environments with fluctuating variables like obstacle proximity and traffic flow. IoT integration enhances navigation systems by enabling real-time data collection and sharing among vehicles and infrastructure, fostering adaptive route planning and improving the overall navigation experience. Genetic algorithms further optimize system performance by iteratively adjusting fuzzy logic controller parameters, ensuring efficient decision-making tailored to specific performance criteria such as travel time and fuel consumption. This collaborative integration of fuzzy logic controllers, IoT, and genetic algorithms offers a holistic solution to the challenges of automatic vehicle navigation, enhancing safety, efficiency, and adaptability in complex driving scenarios. Beyond enhancing individual vehicle performance, this approach contributes to overall transportation system efficiency and safety by mitigating traffic congestion, reducing emissions, and minimizing accidents. Consequently, these integrated systems address crucial societal challenges and pave the way for widespread adoption of autonomous vehicles in the future.

Design of a Fuzzy Logic Controller for Regulating the Temperature in Industrial Polyethylene Fluidized Bed Reactor

Fuzzy logic control to be conventional method

Smart platform based on IoT and WSN for monitoring and control of a greenhouse in the context of precision agriculture

This paper presents a new approach of maximum power point tracking (MPPT) for partial shaded total cross tied (TCT) photovoltaic array based on fuzzy logic controller. The proposed method employs MPPT based on fuzzy logic controller comprising two inputs coming from voltage and current sensor. In addition, the photovoltaic array uses total cross tied configuration (TCT) in which it is superior to other configuration such as serial (S), parallel (P), and serial-parallel (SP) configuration. Here, TCT configuration consists of 10 photovoltaic modules by using 5×2 arrangements. Meanwhile, the fuzzy logic controller itself is used to drive boost dc-dc converter through pulse width modulation (PWM). The comparative study of two topologies, TCT (without fuzzy control) and proposed TCT (using fuzzy logic control) is carried out in MATLAB using SIMULINK , Fuzzy Logic Toolbox, and Power System Toolbox. The simulation result shows that TCT photovoltaic array using fuzzy logic controller (FLC) provides both higher power compared with TCT photovoltaic array without fuzzy logic controller.

This paper presents a novel application of fuzzy logic (FL) controller driven by an adaptive fuzzy set (AFS) for position tracking of the telescope driven by electric motor. Also, the proposed FL controller, driven by AFS, is compared with a classical FL control, driven by a static fuzzy set (SFS). Both FL controllers algorithm use the position error and its rate of change as an input vector. The mathematical model of the telescope driven by electric motor is highly nonlinear differential equations. Therefore the use of the artificial intelligent controller, such as FL is much better than the conventional controller, to cover a wide range of operating conditions. So, the output of FL control is utilized to force the electric drives, of the telescope, to satisfy a perfect matching of the predefined desired position of the telescope arms. Both of FL controllers, using AFS and SFS, are simulated and tested when the system is subjected to a step change in reference value. In addition, these simulation results are compared with the conventional Proportional-Derivative (PD) controller, driven by fixed gain. The proposed FL, using an adaptive fuzzy set, improve the dynamic response of the overall system by improving the damping coefficient and decreasing the rise time and settling time compared with other two controllers.

This paper proposes an application of the particle swarm optimization (PSO) to design the optimal fuzzy logic (FL) controller for load frequency control of isolated wind-natural gas hybrid power system. Traditionally, scale factors, membership functions and control rules of FL controller are obtained by trial and error method or experiences of designers. Moreover, the isolated wind-natural gas hybrid power system is a multi-input multi-output (MIMO) system. For that reason, it is not straightforward to design both load frequency controllers simultaneously. To overcome this problem, PSO is applied to concurrently tune scale factors, membership functions and control rules of FL controller to minimize frequency deviations of the system against load disturbances. Simulation results explicitly show that the performance of the optimum FL controller is superior to the conventional PID controller and the non-optimum FL controller in terms of the overshoot, settling time and robustness against various load changes and variations of wind inputs.

After the development of fuzzy logic, an important application of it was developed in control systems and it is known as fuzzy PID controllers. They represent interest in order to be applied in practical applications instead of the linear PID controllers, in the feedback control of a variety of processes, due to their advantages imposed by the non-linear behavior. The design of fuzzy PID controllers remains a challenging area that requires approaches in solving non-linear tuning problems while capturing the effects of noise and process variations. In the literature there are many papers treating this domain, some of them being presented as references in this chapter. Fuzzy PID controllers may be used as controllers instead of linear PID controller in all classical or modern control system applications. They are converting the error between the measured or controlled variable and the reference variable, into a command, which is applied to the actuator of a process. In practical design it is important to have information about their equivalent input-output transfer characteristics. The main purpose of research is to develop control systems for all kind of processes with a higher efficiency of the energy conversion and better values of the control quality criteria. What has been accomplished by other researchers is reviewed in some of these references, related to the chapter theme, making a short review of the related work form the last years and other papers. The applications suddenly met in practice of fuzzy logic, as PID fuzzy controllers, are resulted after the introduction of a fuzzy block into the structure of a linear PID controller (Buhler, 1994, Jantzen, 2007). A related tuning method is presented in (Buhler, 1994). That method makes the equivalence between the fuzzy PID controller and a linear control structure with state feedback. Relations for equivalence are derived. In the paper (Moon, 1995) the author proves that a fuzzy logic controller may be designed to have an identical output to a given PI controller. Also, the reciprocal case is proven that a PI controller may be obtained with identical output to a given fuzzy logic controller with specified fuzzy logic operations. A methodology for analytical and optimal design of fuzzy PID controllers based on evaluation approach is given in (Bao-Gang et all, 1999, 2001). The book (Jantzen, 2007) and other papers of the same author present a theory of fuzzy control, in which the fuzzy PID controllers are analyzed. Tuning fuzzy PID controller is starting from a tuned linear PID controller, replacing it with a linear fuzzy controller, making the fuzzy controller nonlinear and then, in the end, making a fine tuning. In the papers (Mohan & Sinha, 2006, 2008), there are presented some mathematical models for the simplest fuzzy PID controllers and an approach to design

Conventional Proportional Integral Controllers are used in many industrial applications due to their simplicity and robustness. The parameters of the various industrial processes are subjected to change due to change in the environment. These parameters may be categorized as input flow, output flow, water level of the industrial machinery in use. Various process control techniques are being developed to control these variables. In this paper, the Water Level parameters of a Tank are controlled using conventional PID controller and then optimized using fuzzy logic controller. Considering final results, the comparison between literature and the results of this paper’s method illustrates that fuzzy logic controller results are considerably striking rather than others. The measured maximum overshoot for fuzzy logic controller in comparison with the measured value for the conventional PID controller reduced effectively. Besides, the settling times for both fuzzy logic and PID controllers are measured and it shows that the efficiency of fuzzy logic controller is completely reliable than the others which shows the superiority of fuzzy logic controller.

Conventional irrigation systems often result in inefficient water use due to their inability to adapt to dynamic and uncertain environmental conditions. This study aims to design and simulate an adaptive smart irrigation system using Mamdani Fuzzy Logic Controller (FLC) in an Internet of Things (IoT) architecture. This methodology integrates four environmental parameters, namely Soil Moisture, Air Temperature, Air Humidity, and Light Intensity to calculate the appropriate watering duration, effectively reducing the risk of false positives associated with traditional two-input systems. The mathematical model was verified and simulated using MATLAB Fuzzy Logic Toolbox, with the Centroid defuzzification method. The results show that in extreme testing scenarios, the system successfully calculated the appropriate watering duration of 18 seconds. This analytical calculation perfectly aligns with the MATLAB simulation, demonstrating 100% accuracy with no error deviation. In conclusion, the proposed four-input Mamdani Fuzzy Logic controller effectively reduces data ambiguity and optimizes agricultural water consumption, establishing a solid mathematical foundation for future IoT hardware implementation in precision agriculture.

Energy consumption for room air-conditioning accounts for about half of the total energy used in the building equipment area. Under this situation, it is clear that a major contribution to energy saving can be made by achieving greater energy efficiency if the air-conditioning systems is controlled more effectively such as controlled by a 68HC12 microcontroller system using fuzzy logic approaching. With energy efficiency concept adapt to the institution power demand, great savings can be archived. Fuzzy control has been widely applied for handling the system which has uncertainty or high robust system. Since the dynamic behaviors of the systems contain complexity and uncertainty in its parameters, several fuzzy logic controllers have been implemented to control room temperature in the field of air-conditioning system. This paper describe the MC68HC12 microcontroller features that support Fuzzy Logic, introduces Fuzzy Logic and intelligent control. The goals of these studies are to illustrate Fuzzy Logic theory, to apply Fuzzy Logic features of the MC68HC12, and to implement applications of fuzzy control for air-conditioning system.