Single Fuzzy Controller Research Articles

Simulation and experimental research on energy management control strategy for fuel cell heavy-duty truck

Energy Management Control Strategies (EMCS) play a significant role in improving the economy of Fuel Cell Heavy-duty Truck (FCHT). In this paper, a Single Fuzzy (S-Fuzzy) control strategy is introduced for the FCHT by considering the output characteristics of the fuel cell as well as the charging and discharging power characteristics of the power battery at the current state of charge (SOC). On this basis, the real-time difference between the current SOC and the target SOC of the power battery during the actual operation of the vehicle was further considered, the real-time correction coefficient of the fuel cell output power was introduced, and a Compound Fuzzy (C-Fuzzy) control strategy suitable for FCHT was developed. Meanwhile, the established vehicle model was verified dynamically and economically to ensure its accuracy, with the maximum speed error lower than 2%, the SOC error within 4.9%, and the relative dynamic simulation error of 1.3%. Utilizing co-simulation platforms Cruise and Matlab, the research demonstrates that the C-Fuzzy control strategy significantly outperforms the S-Fuzzy strategy, reducing Equivalent Hydrogen Consumption (EHC) by 1.63% under C-WTVC conditions and by 3.12% under CHTC-TT conditions. To counteract efficiency loss from fuel cell load fluctuations, a real-time EMCS based on C-Fuzzy control and multi-level power output was developed. Validated over a 122-km route, the real-time EMCS reduced the equivalent hydrogen consumption per 100 km of the FCHT to 10.14 kg, maintained the average fuel cell efficiency at 51.02%, and ensured stable SOC of the power battery within the predetermined target range.

Dual Fuzzy Energy Control Study of Automotive Fuel Cell Hybrid Power System with Three Energy Sources

A dual fuzzy control strategy is proposed for the complex energy management problem in a multi-energy-source hybrid power system vehicle with fuel cell + Li-ion battery + super capacitor. According to the efficiency characteristic of each energy source in a fuel cell hybrid power system (referred to as FCHPS), a control scheme with a dual fuzzy control strategy is devised, in which the main fuzzy controller controls the fuel cell to ensure its power output. It is controlled by a sub-fuzzy controller that regulates the power output and braking energy recovery of the Li-ion battery and super capacitor. Simulation verification and comparative analysis were carried out under a world light vehicle test cycle (referred to as WLTC) conditions using MATLAB+ADVISOR. With a dual fuzzy control strategy, fuel cell hybrid vehicles meet dynamics requirements and the fuel economy of these vehicles is generally 6.7% and 6.4% better than power following control and single fuzzy control, respectively. Li-ion batteries are also capable of handling reduced average currents.

Adaptive Fuzzy Sliding Mode Control of a Pressure-Controlled Artificial Ventilator.

This paper presents the application of adaptive fuzzy sliding mode control (AFSMC) for the respiratory system to assist the patients facing difficulty in breathing. The ventilator system consists of a blower-hose-patient system and patient's lung model with nonlinear lung compliance. The AFSMC is based on two components: singleton control action and a discontinuous term. The singleton control action is based on fuzzy logic with adjustable tuning parameters to approximate the perfect feedback linearization control. The switching control law based on the sliding mode principle aims to minimize the estimation error between approximated single fuzzy control action and perfect feedback linearization control. The proposed control strategy manipulated the airway flow delivered by the ventilator such that the peak pressure will remain under critical values in presence of unknown patient-hose-leak parameters and patient breathing effort. The closed-loop stability of AFSMC will be proven in the sense of Lyapunov. For comparative analysis, classical PID and sliding mode controllers are also designed and implemented for mechanical ventilation problems. For performance analysis, numerical simulations were performed on a mechanical ventilator simulator. Simulation results reveal that the proposed controller demonstrates better tracking of targeted airway pressure compared with its counterparts in terms of faster convergence, less overshoot, and small tracking error. Hence, the proposed controller provides useful insight for its application to real-world scenarios.

Power distribution strategy based on fuzzy controller and Savitzky-Golay selective filtering in hybrid energy storage system

Lithiumion batteries, as the power of electric vehicles, are facing challenges due to their intrinsic defects. The hybrid energy storage system with the combination of high power supercapacitor and lithium-ion battery are better promising solution. The peak current output by the lithium battery would be reduced greatly, which would result in great heat reduce and further enhancement of life cycle and safety of the lithium-ion battery. In considering their different charge and discharge behaviors, a high efficient control strategy is necessary for better performances. Here, an intelligent energy control strategy that combines fuzzy controller and Savitzky-Golay selective filtering is studied for HESS controlling. The fuzzy controller has good robustness and is suitable for nonlinear control, while the Savitzky-Golay filter can effectively smooth the waveform, and by selecting the output of both can effectively improve cell performance index. The simulation results show that compared with ESS under the tested cycle conditions, the proposed control strategy reduces the maximum current of the battery by 8.5% and the maximum temperature is reduced by 7.98%; when compared with a single fuzzy controller used by HESS, the proposed strategy can reduce the maximum battery current and maximum temperature by 7.02% and 1.50%, respectively.

Analysis and Design of a Double Fuzzy PI Controller of a Voltage Outer Loop in a Reversible Three-Phase PWM Converter

Aiming at the application of a reversible three-phase pulse width modulation (PWM) converter with a wide range of AC side voltage and DC side voltage, a double fuzzy proportional integral (PI) controller for voltage outer loop was proposed. The structures of the proposed controller were motivated by the problems that either the traditional PI controller or single fuzzy PI controller cannot achieve high performance in a wide range of AC and DC voltage conditions. The presented double fuzzy controller studied in this paper is a sub fuzzy controller in addition to the traditional fuzzy PI controller; in particular, the sub fuzzy controller can get the auxiliary correction of PI control parameters according to the AC side voltage and the DC side given voltage variation of PWM converter after the reasoning of the sub fuzzy controller, while the traditional fuzzy PI controller outputs the correction of PI control parameters according to the DC voltage error and its error change rate. In this paper, the traditional fuzzy PI controller can be called the main fuzzy controller, and the adaptive adjustment of PI control parameters of the voltage outer loop is the sum of the PI parameter correction output by the main fuzzy controller and the auxiliary PI parameter correction output by the sub fuzzy controller. Finally, the experimental results show that the reversible three-phase PWM converter can achieve excellent dynamic and static performance in a wide range of AC voltage and DC voltage applications by using the proposed double fuzzy PI controller.

Adaptive Current Control Method for Hybrid Active Power Filter

Abstract This paper proposes an adaptive current control method for Hybrid Active Power Filter (HAPF). It consists of a fuzzy-neural controller, identification and prediction model and cost function. The fuzzy-neural controller parameters are adjusted according to the cost function minimum criteria. For this reason, the proposed control method has a capability on-line control clings to variation of the load harmonic currents. Compared to the single fuzzy logic control method, the proposed control method shows the advantages of better dynamic response, compensation error in steady-state is smaller, able to online control is better and harmonics cancelling is more effective. Simulation and experimental results have demonstrated the effectiveness of the proposed control method.

Multiple Models Fuzzy PID Controller Design in Ship Power System

The ship power system is a complex system with time varying, nonlinear and strong coupling characters. The traditional PID controller is difficult to meet the requirements of the system.In this paper, a multiple models fuzzy PID controller is designed. Considering the different operating conditions of on-land ship power system, two excitation regulation model which respectively corresponds to a fuzzy PID controller are set up. Each time, based on the input operation condition tested, only one fuzzy PID Controller is chosen to improve the control precision. At the end, simulation results show that multiple model fuzzy PID controller is superior to the single fuzzy PID controller, which effectively improves the transient response of the system, the steady state accuracy and robustness, having good prospects for engineering applications.

An Intelligent Traffic Signal Controller Based on CAN Bus

According to the characteristics of mixed traffic flow of China, An intelligent traffic signal controller is introduced. STM32F207 and w5500 are used to design hardware structure. The software design is completed based on CoOS. Single point fuzzy control is used to improve dynamic traffic flow. Experimental results show that the intelligent traffic controller has high level of integration, powerful functions, and reliable performance.

Design and Optimization of Urban Signal Fuzzy Controller Based on IQPSO

In order to overcome the shortcomings of traffic signal fixed-time control method, a fuzzy control algorithm for urban traffic signal is proposed. The signal phase switching order is adjustable. The improved quantum particle swarm optimization(QPSO) is also introduced to optimize fuzzy control rules of traffic signal controller. Take four-phase traffic signal commonly used in current practice for example. Compared with traffic signal fixed-time control and single fuzzy control method, the control method put forward in this paper can reduce the vehicles’ average delay time in junction. The simulation results show that the proposed algorithm is proved to be an effective and practicable method for urban traffic self-adaptive control.

Multiple Models Fuzzy PID Controller Design in Air-Condition Temperature Control System

This paper deals with the study of air-condition temperature control, which is a complex system of time varying, nonlinear, imprecise model and uncertain work environment. In this paper, a multiple models fuzzy PID controller is designed. Considering the air-condition temperature adjustment system to combat aircraft and the frequently changing external environment the multiple fuzzy PID controllers are estimated according to the temperature changing area. Each time, based on temperature tested, only one fuzzy PID Controller is chosen to improve the control precision. At the end, simulation results show that multiple model fuzzy PID control is superior to the single fuzzy PID control, which effectively improve the transient response of the system, the steady state accuracy and robustness, having good prospects for engineering applications.

Design of Fuzzy Logic Controller for Lateral Dynamics Control of Aircraft by Considering the Cross-Coupling Effect of Yaw and Roll on Each Other

In this paper, we explore fuzzy logic approach for lateral control of aircrafts. Here the effect of roll on yaw and that of yaw on roll is taken into account. The dynamic modeling of lateral control system that controls the lateral dynamics of an aircraft.is presented. The aircraft being considered in this work is a standard NAVION Transport aircraft [1]. A suitable mathematical model to describe the dynamics of an aircraft is derived. A single fuzzy controller is designed which will control both yaw and roll of the aircraft simultaneously. In this paper the transfer functions for yaw and roll is derived and their effects on each other is taken into account so that the system will have greater accuracy and efficiency than the application of two separate controllers. This design is very cost effective and will significantly reduce instability and increases maneuverability. Simulation results for the response of lateral controller are presented in time domain. Finally, the performance of lateral control system is analyzed based on common criteria of step’s response

On the Basis of Fuzzy and PID Double Control of Honing Machine Reciprocating Motion

As the improvement of honing velocity, acceleration and accuracy, corresponding requirements about control technology are put forward to meet the improvement of high machining efficiency. While the honing machine spindle reciprocating reversing control is the key control link, directly affects the ability of the machine abilities and quality. Aim at the traditional honing machine reciprocating motion cannot satisfy the requirement of high speed, high acceleration and high reversing accuracy. According to the characteristics of honing machine mechanism of reciprocating movement, establish its mathematic model and the system of fuzzy and PID double control model, and then the control system was conducted by using the tool of Simulink. The simulation result showed that using fuzzy and PID double control can make overshoots reduced compared with conventional single fuzzy control and PID control, and can well satisfied the system performance index.

Semi-active control of ship mast vibrations using magneto-rheological dampers

On marine vessels, delicate instruments such as navigation radars are normally mounted on ship masts. However the vibrations at the top of mast where the radar is mounted often cause serious deterioration in radar-tracking resolution. The most serious problem is caused by the rotational vibrations at the top of mast that may be due to wind loading, inertial loading from ship rolling and base excitations induced by the running propeller. This paper presents a method of semi-active vibration control using magneto-rheological (MR) dampers to reduce the rotational vibration of the mast. In the study, the classical optimal control algorithm, the independent modal space control algorithm and the double input - single output fuzzy control algorithm are employed for the vibration control. As the phenomenological model of an MR damper is highly nonlinear, which is difficult to analyse, a back- propagation neural network is trained to emulate the inverse dynamic characteristics of the MR damper in the analysis. The trained neural network gives the required voltage for each MR damper based on the displacement, velocity and control force of the MR damper quickly. Numerical simulations show that the proposed control methods can effectively suppress the rotational vibrations at the top of mast.

Hierarchical neuro-fuzzy current control for a shunt active power filter

This paper presents the design of a hierarchical neuro-fuzzy current control scheme for a shunt active power filter compared with a single fuzzy controller scheme. A single fuzzy controller scheme is presented first and an ANFIS based neuro-fuzzy controller is connected hierarchically to the first one to improve the performance. Simulation results show that harmonic compensation is achieved with both schemes, however switching performance is superior for hierarchical scheme. The method of switching controller development in this paper is new and can be applied to other power electronic converter applications for improved performance.

Towards a methodology for selecting good scaling factors for a fuzzy controller

The work presented concerns an approach for selecting the scaling factors of a single fuzzy controller. This approach has been done using as basic systems the ones presented in an advanced showcase of adaptative controller designs (Masten and Cohen, 1990). The paper presents results through an optimisation technique done on these systems by data analysis method.

An efficient single output fuzzy control algorithm for adaptive applications

A new single output fuzzy control algorithm which is simple and efficient is developed in the paper. Assuming standard type membership functions, a fuzzy set is characterized in this approach by that element in the fuzzy set with maximal membership. Fuzzy rules are then represented by a set of numbers. The algorithm uses a matrix representation of the rules in which the input part of the rule is used as an index, and the indexed element is the corresponding output. This is shown to allow for easy expansion of the output universe. Modification and introduction of new rules are further shown to be very simple and as a salient feature the efficiency of the algorithm is found to increase, when the number of rules increases. This new algorithm is therefore particularly useful and efficient when combined with a rule adaption mechanism. Because of simplicity and efficiency, this algorithm is very suitable for implementation even on small micro processor systems, making adaptive fuzzy control feasible for industrial applications, where a fast and nonlinear control is required.