FPGA based real-time adaptive fuzzy logic controller

This paper presents the closed-loop control of high brightness LED lamps using microcontroller. Three types of controllers, proportional-integral (PI) controller, look-up table fuzzy logic controller and real-time calculation fuzzy logic controller are implemented for performance comparison. A photosensitive resistor senses the LED brightness and an 8-bit microcontroller converts brightness signal to numerical value and performs control algorithm. The algorithm is programmed in C language. Selected inputs for fuzzy control algorithm are the brightness error and the change of brightness error. Pulse width modulation (PWM) correction is the only output of the controller and PWM current source is used to drive the LED lamp. Experimental results show that the PI controller needs the least memory and run time, and the real-time calculation fuzzy logic controller requires the most memory and run time, among the designed controllers.

Passenger ride comfort is considered to be of utmost importance in designing any suspension system. The primary objective of any suspension system in automobiles is to isolate the road disturbances experienced by the tyres from being transmitted to the passengers. ISO- 2631-1:1997(E) has provided certain guidelines to analyse ride comfort and also indicates the degree to which the vibration exposure will be acceptable. Hence active suspension systems are becoming crucial to improve ride quality and passenger comfort, in view of the established superiority over conventional systems. In this paper considering direction of vibrations acting on human body in the translational vertical direction of the coordinate system, bump analysis is carried out on a 3 DOF quarter car model. Since the system dynamics change with increase in the Degrees of freedom, fuzzy logic control is used to control active suspension of a 3-DOF quarter car model. The objective function of the fuzzy logic control has been defined to minimize the sprung mass acceleration and seat acceleration, and is evaluated by simulating the transient response to road perturbations. In this paper the study is extended to a quarter car (3-DOF) seat-suspension model instead of 2 DOF suspension systems, since the vibrations are transmitted to human body through broad contact area like buttocks when sitting. The performance parameters, chassis acceleration, seat acceleration, seat displacement and chassis displacements are analyzed and also the ride comfort levels are analyzed according to ISO-2631-1:1997(E). Results indicate more ride comfort to the occupants through the active suspension system with the proposed fuzzy controller rather than the passive suspension system.

This study designs a fuzzy logic controller (FLC) for an active automobile suspension system in which the membership functions and control rules are optimized using a genetic algorithm (GA). The objective of the FLC is to strike an optimal balance between the ride comfort and the vehicle stability. The values of the crossover and mutation parameters in the GA are adapted dynamically during the convergence procedure using a fuzzy control scheme. The convergence state of the GA is determined by using a support vector machine (SVM) method to identify the variation in each of the genes of the best-fit GA chromosome following each iteration loop. The feasibility of the proposed GA-assisted FLC scheme is verified by performing a series of numerical simulations in which the characteristics of the controlled plant are compared with those observed in a passive suspension system and obtained under an optimal linear feedback controller. The results demonstrate that the GA-assisted FLC results in a lower suspension deflection, a reduced sprung mass acceleration and a lower bouncing distance between the tire and the ground.

Fuzzy logic based control system is developed for glideslope capture of an aircraft under gust. First, the design of a longitudinal control system for automatic landing is presented for a linear aircraft model. Then, the fuzzy controller parameters derived for the linear model are modified to obtain a robust control system for a six degree of freedom nonlinear aircraft model. It is shown that the simple tuning fuzzy controller, by altering scaling factors, is well suited for control of difficult aircraft maneuvers like landing in severe weather conditions which require continuous change in engine thrust and control surfaces.

This paper presents a novel real-time fuzzy logic control (FLC) configuration for energy management of a fuel cell/battery vehicle. To do so, three FLCs, in a parallel structure, have been designed and optimized by a genetic algorithm for variant driving behavior. Subsequently, a velocity classifier is employed to set the most suitable weighting factor at each moment, regarding the speed level. Finally, performance of the proposed FLC, concerning degradation and fuel consumption, has been compared with optimal results, achieved by dynamic programming, for three distinct driving cycles. The comparison outcomes prove the success of the suggested fuzzy strategy.

A fuzzy logic-based control system in household split-type air conditioners (AC) offers an alternative approach to reducing excess energy consumption without compromising thermal comfort. This study aims to test the effectiveness of three types of membership functions (MF), namely triangular, trapezoidal, and Gaussian, in improving energy efficiency and the stability of room temperature and humidity control. Simulations were performed using MATLAB software with the Mamdani fuzzy inference method and centroid defuzzification technique. The three MF were tested using 30 sets of temperature and humidity data to analyze their effect on fan speed and power consumption. The simulation results show that the trapezoidal MF provides the highest energy efficiency of 57.24%, followed by the Gaussian MF at 56.80% and the triangular MF at 53.71%. These findings indicate that fuzzy systems can significantly reduce energy consumption compared to conventional air conditioner controllers. This research is expected to serve as a reference in the development of more energy-efficient intelligent control systems.

The performance of active suspension systems is directly related to the mechanical design and control of the system. The stable operation of the controller improves driving comfort and handling. The quarter car model is frequently used in the analysis of suspension systems due to its simple structure. In this study, Matlab Simulink software was used in the modeling, control and simulation of the quarter car active suspension model. System performance was investigated for four different road profiles with PID and fuzzy logic control methods. Two of the road profiles used are in the form of impact signals consisting of pits and bumps. The other two road profiles are random road disturbances with high frequency. The effects of active and passive suspension systems on driving comfort are compared by taking into account the control methods used. As a result of the study, it has been determined that the fuzzy logic controller gives better results in pulse signals consisting of bumps and pits, and the PID controller gives better results in high-frequency random road disturbances. In addition, with the use of fuzzy logic control method in the active suspension system, a significant decrease in the actuator force has occurred. This result is very interesting in terms of minimizing energy, reducing actuator sizes and reducing costs.

FUZZY CONTROL METHODOLOGY AND APPLICATIONS TO AUTOMOTIVE SYSTEMS AND MOBILE ROBOTS

The aim of this paper is to show how to build a fuzzy controller and its membership functions automatically. In a fuzzy logic controller (FLC), the proposed method allows one easily to construct a set of membership functions, called shrinking-span membership functions (SSMFs). The FLC uses Mamdani-type fuzzy controllers for the defuzzification strategy and inference operators. The FLC hardware implementation is performed on an 8-bit microcontroller. Simulation results and experimental results demonstrate that the converter can be regulated with good performance even when subjected to input disturbance and load variation. The presented approach is generally valid for the design of an FLC, and can be applied to any dc–dc converter topologies.

This study details an efficient fuzzy logic controller (FLC) to improve the performance of active automotive suspension system. A comparison between passive and FLC active suspensions is performed. A mathematical model of automotive active suspension has six degrees of freedom and two input forces generated by two separate actuators are solved using Matlab Simulink. In order to evaluate the effectiveness of the proposed controller under random road disturbance, several performance criteria are assessed based on the dynamic response of the half automotive suspension system. Simulation results of the active suspension system based on the fuzzy logic clearly have been provided to illustrate the effectiveness of the FLC under different road conditions and confirmed that fuzzy logic is very effective for enhancing ride comfort and stability of the vehicle.

Fuzzy logic controllers are designed for spacecraft hovering flight in a binary asteroid system. The binary asteroid system is modeled as an ellipsoid-sphere system where the spherical harmonics method is adopted to represent the gravitational field of the asteroids. The method to design the fuzzy logic controllers is introduced in detail. Hovering flight about the inner collinear equilibrium point L 1 of binary asteroid Didymos is numerically presented. Detailed steps on roughly estimating the scaling gains of fuzzy logic controllers are also summarized. Numerical simulations are performed to show the effectiveness of the proposed method. Moreover, the aforementioned fuzzy logic controllers can be used for both continuous and impulsive thrusters. The fuzzy controllers with continuous thrust, the fuzzy controllers with fixed thrust, and the other two kinds of sliding-mode controllers are optimized in the same way and compared in the same simulation environment together, to show tremendous advantages of fuzzy logic controllers and continuous thrusters on both control performance and propellant consumption.

In this paper, a fuzzy logic based control system for an undersea vehicle is proposed, in order to improve the response of the vehicle to commands as well as to make it robust against speed changes and environmental disturbances. After a brief introduction of the dynamical model of the undersea vehicle and the fundamentals of fuzzy logic control, the construction of the fuzzy logic controller for the vehicle is set up, in which depth and pitch rate are chosen as input linguistic variables, and the deflection of the sternplane is the control variable. Performance evaluation of the fuzzy logic controller for the vehicle is carried out at three different speeds chosen from the whole speed region. The evaluation includes: 1) response of the vehicle with the fuzzy logic controller to a depth command, 2) robustness of the fuzzy logic controller to speed variation and 3) robustness of the fuzzy logic controller to wave induced disturbances. The calculation results show that the depth response reaches the command's value without oscillation and final error at all speeds, and the designed controller demonstrates good robustness against parameter changes and provides effective disturbance rejection from waves.

This paper developed a fuzzy logic controller with a genetic algorithm control system for sensor based pipeline robot navigation in pipe environment. The fuzzy controller creates initial membership functions and fuzzy logic rules. the genetic algorithm, which automatically designs a fuzzy logic controller by modifying the position of controller membership functions and the commands given to the pipeline robot, optimizes the fuzzy controller. This GA-fuzzy system is used to control the mobile pipeline robot, which is equipped with an array of ultrasonic sensors to acquire the distance between the pipeline robot and obstacle. On the basis of this system, a strategy for combining local behavior control with global behavior control planning was proposed in this paper. Finally a pipeline robot runs in simulation environment. The experimental results show that appropriate control mechanisms of the fuzzy controller are obtained by evolution. The controller has evolved well enough to smoothly drive the pipeline robot in different pipe environment.

<div>A hybrid fuzzy and proportional-integral-derivative (PID) controller is proposed for roll angle handling of a three-axle truck with an active air suspension system. The conventional truck suspension system has four air springs for the rear wheels and two leaf springs for the front wheels, which cannot properly control the pitch angle, and here in this study is upgraded into front air springs. Therefore in the full air suspension system, the pitch angle is controlled by the active suspension system. Roll reduction of a heavy vehicle can improve the ride comfort and rollover tendency of the truck, simultaneously. The relation of air spring pressures and vehicle dynamics is developed in a simple and accurate model. Using this comprehensive model, it is possible to control the variables of vehicle dynamics such as roll, pitch, and height of the truck. The truck air suspension system is examined in step steering, fishhook, and asymmetric rough road (types E and G power spectral density [PSD] road) tests. The fuzzy input is a normalized roll angle and the output is the normalized mass flow rate (of the air springs). Both of the fuzzy input and output have nine membership functions (MFs), which have optimized with the genetic algorithm (GA) method. The optimization cost function is a combination of maximum and integral of the absolute roll angle of the truck sprung mass. Besides, the PID controller is tuned by the Ziegler-Nichols method at the first stage and optimized by the GA method. The results show that the optimized fuzzy controller has good roll performance in a different test; however, the simple PID addition to the fuzzy controller can improve vehicle comfort and stability.</div>

The use of fuzzy logic-based automatic control systems in aeroponic systems is expected to facilitate farming. This paper proposes a novel integrated fuzzy control system for an aeroponic greenhouse. The real-time method considers temperature and carbon dioxide (CO2) effects on red chili cultivation, and the controller is based on control performance or an evaluation of the correlation of parameters. The design a fuzzy control system to control red chili plant temperature and CO2 in an aeroponic system cultivation in a greenhouse based on control performance. The fuzzy control system was developed by using a fuzzy zet, a member function, fuzzification, a set of rules, and defuzzification on a personal computer. Sensors include a DHT11 temperature sensor, a CDM4160 CO2 sensor and 4 actuators: a 12 V pump fan, a gas valve and a lamp that is connected to the Arduino Mega 2560 microcontroller by a relay, and an L298N motor driver for control, as well as an additional Arduino 16x2 LCD screen that displays the temperature and CO2 values read by the sensors. The performance results reported for the temperature and CO2 fuzzy logic control parameters for red chilis cutivation in the aeroponic on greenhouse include the time delay (Td), time rise (Tr), time peak (Tp) and Error steady state (Ess) values. The red chili plants grow well using the proposed method