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Fractional Order Fuzzy PID Control of Automotive PEM Fuel Cell Air Feed System Using Neural Network Optimization Algorithm
Abstract and Figures
The air feeding system is one of the most important systems in the proton exchange membrane fuel cell (PEMFC) stack, which has a great impact on the stack performance. The main control objective is to design an optimal controller for the air feeding system to regulate oxygen excess at the required level to prevent oxygen starvation and obtain the maximum net power output from the PEMFC stack at different disturbance conditions. This paper proposes a fractional order fuzzy PID controller as an efficient controller for the PEMFC air feed system. The proposed controller was then employed to achieve maximum power point tracking for the PEMFC stack. The proposed controller was optimized using the neural network algorithm (NNA), which is a new metaheuristic optimization algorithm inspired by the structure and operations of the artificial neural networks (ANNs). This paper is the first application of the fractional order fuzzy PID controller to the PEMFC air feed system. The NNA algorithm was also applied for the first time for the optimization of the controllers tested in this paper. Simulation results showed the effectiveness of the proposed controller by improving the transient response providing a better set point tracking and disturbance rejection with better time domain performance indices. Sensitivity analyses were carried-out to test the robustness of the proposed controller under different uncertainty conditions. Simulation results showed that the proposed controller had good robustness against parameter uncertainty in the system.
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... The results indicated that the hybrid controller had better performance than PID controller. AbouOmar et al. 8 built a fractional order fuzzy PID controller for the OER control, and the neural network was employed to optimize the algorithm. The proposed method showed good set point tracking ability and robustness. ...
... The control objectives: OER and p ca , are unmeasurable. And the OER is directly related to p ca from equations (6) to (8). Therefore, in order to observe the values of OER and p ca , an ESO is designed to estimate the cathode pressure in this part. ...
The proton exchange membrane fuel cell gas control has been one key point in fuel cell management systems. The complexity and coupling of the air management system make it difficult to achieve precise air intake adjustment. In this paper, an accurate joint control method for the air flow and pressure regulation is proposed. The nonlinear mathematical model of the air management system is developed to describe the output characteristic and state change. Based on this, the feedback linearization method is proposed to obtain the direct correspondence between control variables and controlled variables. In addition, to solve the problem that the controlled variables cannot be measured directly, an extended state observer is applied to estimate the stack cathode pressure. The sliding mode predictive control method is proposed to control the oxygen excess ratio and cathode pressure simultaneously. The relative order of the system is used to design the sliding mode surface, and the corresponding predictive model is proposed. The results obtained by simulation experiments show that pressure and mass flow have little effect on each other through decoupling. The proposed algorithm has been verified to have high precision, fast response, and robustness through comparative experiments.
... As a result, the choice of an optimization technique raises many challenges. Various optimization methods like Genetic Algorithms (GA), Harmony Search (HS), Particle Swarm Optimization (PSO), Ant Colony Optimizer (ACO), and Gravitational Search Algorithm (GSA)have been successfully used to drive the most suitable gains for FO Fuzzy PID regulator and yield effective dynamic performance improvement in the system [36][37][38][39][40]. Similarly, the Differential Evolution Optimizer (DEO) was used to optimize the FO Fuzzy PID gains in [41]. ...
At present, there are different types of Renewable Energy Resources (RESs) available in nature which are wind, tidal, fuel cell, and solar. The wind, tidal, and solar power systems give discontinuous power supply which is not suitable for the present automotive systems. Here, the Proton Exchange Membrane Fuel Stack (PEMFS) is used for supplying the power to the electrical vehicle systems. The features of fuel stack networks are very quick static response, plus low atmospheric pollution. Also, this type of power supply system consists of high flexibility and more reliability. However, the fuel stack drawback is a nonlinear power supply nature. As a result, the functioning point of the fuel stack varies from one position to another position on the V-I curve of the fuel stack. Here, the first objective of the work is the development of the Grey Wolf Optimization Technique (GWOT) involving a Fuzzy Logic Controller (FLC) for finding the Maximum Power Point (MPP) of the fuel stack. This hybrid GWOT-FLC controller stabilizes the source power under various operating temperature conditions of the fuel stack. However, the fuel stack supplies very little output voltage which is improved by introducing the Single Switch Universal Supply Voltage Boost Converter (SSUSVBC) in the second objective. The features of this proposed DC-DC converter are fewer voltage distortions of the fuel stack output voltage, high voltage conversion ratio, and low-level voltage stress on switches. The fuel stack integrated SSUSVBC is analyzed by selecting the MATLAB/Simulink window. Also, the proposed DC-DC converter is tested by utilizing the programmable DC source.
div class="section abstract"> The pressure, flow, and temperature of reactants play a crucial role in the operation of the proton exchange membrane fuel cell (PEMFC), directly impacting its performance. To accurately assess the stack output characteristics, precise regulation of the inlet gas temperature (air and hydrogen) is essential. This study proposes a control structure for maintaining the temperature of the inlet-stack gas. The primary actuators employed in this control structure are the heating belt and solid-state relay. An adaptive PI controller is designed based on self-regulation of the temperature error. The controller's output is mathematically converted into a PWM signal, enabling it to act on the actuators. To validate the feasibility of the control structure and controller, mathematical simulations are performed using MATLAB/Simulink®. Subsequently, experimental validations are conducted on a PEMFC stack test bench. These validations encompass step test, robustness test, and operational stability test. The step test results reveal that the average rise rate of the inlet air temperature is approximately 6.78°C/min@265NLPM, with the temperature increasing from 19.1°C to 75.6°C in 500 seconds. The inlet air temperature exhibits no overshoot, and the maximum steady-state fluctuation is approximately ±0.6°C. The robustness test demonstrates that the designed controller exhibits good resilience to large step changes in flow rate (70-140-350NLPM) and set temperature (40-70°C). Additionally, an 8hour and 20-minute continuous experiment is conducted to assess the reliability of the control structure during long-term operation. The maximum absolute error value observed in the inlet air temperature during this test is 0.7°C, highlighting the excellent reliability and accuracy of the control structure and controller.
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In this research, a new metaheuristic optimization algorithm, inspired by biological nervous systems and artificial neural networks (ANNs) is proposed for solving complex optimization problems. The proposed method, named as neural network algorithm (NNA), is developed based on the unique structure of ANNs. The NNA benefits from complicated structure of the ANNs and its operators in order to generate new candidate solutions. In terms of convergence proof, the relationship between improvised exploitation and each parameter under asymmetric interval is derived and an iterative convergence of NNA is proved theoretically. In this paper, the NNA with its interconnected computing unit is examined for 21 well-known unconstrained benchmarks with dimensions 50–200 for evaluating its performance compared with the state-of-the-art algorithms and recent optimization methods. Besides, several constrained engineering design problems have been investigated to validate the efficiency of NNA for searching in feasible region in constrained optimization problems. Being an algorithm without any effort for fine tuning initial parameters and statistically superior can distinguish the NNA over other reported optimizers. It can be concluded that, the ANNs and its particular structure can be successfully utilized and modeled as metaheuristic optimization method for handling optimization problems.
The output power of a photovoltaic (PV) module depends on the solar irradiance and the operating temperature; therefore, it is necessary to implement maximum power point tracking controllers (MPPT) to obtain the maximum power of a PV system regardless of variations in climatic conditions. The traditional solution for MPPT controllers is the perturbation and observation (P&O) algorithm, which presents oscillation problems around the operating point; the reason why improving the results obtained with this algorithm has become an important goal to reach for researchers. This paper presents the design and modeling of a fuzzy controller for tracking the maximum power point of a PV System. Matlab/Simulink (MathWorks, Natick, MA, USA) was used for the modeling of the components of a 65 W PV system: PV module, buck converter and fuzzy controller; highlighting as main novelty the use of a mathematical model for the PV module, which, unlike diode based models, only needs to calculate the curve fitting parameter. A P&O controller to compare the results obtained with the fuzzy control was designed. The simulation results demonstrated the superiority of the fuzzy controller in terms of settling time, power loss and oscillations at the operating point.
Self-consumption of household photovoltaic (PV) storage systems has become profitable for residential owners under the trends of limited feed-in power and decreasing PV feed-in tariffs. For individual PV-storage systems, the challenge mainly lies in managing surplus generation of battery and grid power flow, ideally without relying on error-prone forecasts for both generation and consumption. Considering the large variation in power profiles of different houses in a neighborhood, the strategy is also supposed to be beneficial and applicable for the entire community. In this study, an adaptable battery charging control strategy is designed in order to obtain minimum costs for houses without any meteorological or load forecasts. Based on fuzzy logic control (FLC), battery state-of-charge (SOC) and the variation of SOC (∆SOC) are taken as input variables to dynamically determine output charging power with minimum costs. The proposed FLC-based algorithm benefits from the charging battery as much as possible during the daytime, and meanwhile properly preserves the capacity at midday when there is high possibility of curtailment loss. In addition, due to distinct power profiles in each individual house, input membership functions of FLC are improved by particle swarm optimization (PSO) to achieve better overall performance. A neighborhood with 74 houses in Germany is set up as a scenario for comparison to prior studies. Without forecasts of generation and consumption power, the proposed method leads to minimum costs in 98.6% of houses in the community, and attains the lowest average expenses for a single house each year.
In this paper, a fuzzy logic controller is proposed to fulfill the objective of maximum power extraction based on a two-mass model. One of the crucial problems is that the effective wind speed cannot be measured directly due to the high disturbance of the wind speed and the high cost of sensors. Three algorithms are used to estimate the effective wind speed by solving power balance equations, and the estimated wind speed is used to determine the optimal speed reference for a generator control system. The control performance of the fuzzy logic controller is verified in the whole system and compared with a conventional PI controller. Simulation results are presented to illustrate the effectiveness and robustness against parameter variations of the proposed control design.
Optimized robust control for proton exchange membrane (PEM) fuel cell air supply systems is now a hot topic in improving the performance of oxygen excess ratio (OER) and the net power. In this paper, a cascade adaptive sliding mode control method is proposed to regulate oxygen excess ratio (OER) for proton exchange membrane (PEM) fuel cell air supply systems. Based on a simplified sixth-order nonlinear dynamic model, which takes parametric uncertainties, external disturbances and measurement noises into consideration, the nonlinear controller based on cascade adaptive sliding mode (NC-ASM) control is proposed. The method combines the nonlinear terms of super twisting algorithm and two added linear terms, and the modified second order sliding mode (SOSM) algorithm based on an observer is employed to form a cascade structure. Besides, an adaptive law is also utilized to regulate the parameters of the NC-ASM controller online. The performance of the controller is implemented on a real-time emulator. The results show that the proposed strategy performs better than the conventional constant sliding mode (CSM) control and PID method. Though during large range of load current and in the presence of various uncertainties, disturbances and noises, the NC-ASM controller can always converge rapidly, the feasibility and effectiveness are validated.
In this paper, a model-based robust control is proposed for the polymer electrolyte membrane fuel cell air-feed system, based on the second-order sliding mode algorithm. The control objective is to maximize the fuel cell net power and avoid the oxygen starvation by regulating the oxygen excess ratio to its desired value during fast load variations. The oxygen excess ratio is estimated via an extended state observer (ESO) from the measurements of the compressor flow rate, the load current, and the supply manifold pressure. A hardware-in-loop test bench, which consists of a commercial twin screw air compressor and a real-time fuel cell emulation system, is used to validate the performance of the proposed ESO-based controller. The experimental results show that the controller is robust and has a good transient performance in the presence of load variations and parametric uncertainties.
The robotic manipulator is an extremely nonlinear multi-input multi-output (MIMO), highly coupled, and complex system wherein the parameter uncertainties and external disturbance adversely affect the performance of this system. From this, it necessitates that the controllers designed for such system must overcome these complexities. In this paper, we develop a novel fractional order fuzzy pre-compensated fractional order PID (FOFP-FOPID) controller for 2-degree of freedom (2-DOF) manipulator dealing with trajectory tracking problem. In order to optimize the controller's parameters while minimizing integral of time absolute error (ITAE), a metaheuristic optimization technique, viz., artificial bee colony-genetic algorithm (ABC-GA) is presented. The efficacy of our proposed controller is demonstrated by comparing it with some existing controllers, such as integer order fuzzy pre-compensated PID (IOFP-PID), fuzzy PID (FPID), and conventional PID controllers. Furthermore, the robustness analysis for proposed controllers is also investigated for parameter variations and external disturbances. The simulation results indicate that FOFP-FOPID controller can not only guarantee the best trajectory tracking but also ameliorate the system robustness for parameter variations as well as external disturbances.
In this study, a novel fuzzy logic control scheme is investigated for the frequency and power control of an isolated hybrid power system (HPS) (IHPS) and HPS-based two-area power system. The studied IHPS comprises of various autonomous generation systems such as wind energy, diesel engine generator and an energy storage device (i.e. capacitor energy storage device). A novel fractional order (FO) fuzzy proportional-integral-derivative (PID) (FO-F-PID) controller scheme is deployed and the various tunable parameters of the studied model are tuned by quasi-oppositional harmony search (HS) (QOHS) algorithm for improved performance. The QOHS algorithm (based on standard HS algorithm) accelerates the convergence speed by combining the concept of quasi-opposition in the basic HS framework. This FO-F-PID controller shows better performance over the integer order-PID and F-PID controller in both linear and non-linear operating regimes. The proposed FO-F-PID controller also shows stronger robustness properties against loading mismatch and different rate constraint non-linarites than other controller structures.
This paper proposes a novel hybrid fuzzy-PID controller for air supply on Proton Exchange Membrane fuel cell (PEMFC) systems. The control objective is to adjust the oxygen excess ratio at a given setpoint in order to prevent oxygen starvation and damage of the fuel-cell stack. The proposed control scheme consists of three parts: a fuzzy logic controller (FLC), a fuzzy-based self-tuned PID (FSTPID) controller and a fuzzy selector. Depending on the value of the error between the current value of oxygen excess ratio and its setpoint value, the fuzzy selector decides which controller should play the greatest effect on the control system. The performance of the proposed control strategy is analysed through simulations for different load variations and for parameter uncertainties. The results show that the novel hybrid fuzzy-PID controller performs significantly better than the classical PID controller and the FLC in terms of several key performance indices such as the Integral Squared Error (ISE), the Integral Absolute Error (IAE) and the Integral Time-weighted Absolute Error (ITAE), as well as the overshoot, settling and rise time for the closed-loop control system.
Surge control in a centrifugal air compressor is crucial to ensure the reliable operation of a fuel cell system, but few studies have reported on compressor surges in fuel cell systems. This study presents an effective surge control algorithm with a surge predictive compressor model under various operating conditions of an automotive fuel cell system. Unlike previous studies on the air management systems of an automotive fuel cell, this study presents an analytic compressor model which is a nonlinear dynamic model with surge prediction capability. In this study, a model reference adaptive control (MRAC) is introduced, to avoid compressor surge during the dynamic operation of a fuel cell system. The adaptive control was compared with the nominal feedback control in the air management system of an automotive fuel cell operating within normal conditions, under transient and steady-state responses. Also, when a surge was detected in the system, the adaptive control algorithm was fast enough to recover the air mass flow rate to normal ranges. Based on these results, it can be concluded that the MRAC algorithm shows better performance than the nominal feedback control algorithm with respect to the transient behaviors and surge recovery of an automotive fuel cell system.