Fractional Order Fuzzy PID Control of Automotive PEM Fuel Cell Air Feed System Using Neural Network Optimization Algorithm
TL;DR: In this paper, a fractional order fuzzy PID controller was proposed to achieve maximum power point tracking for the PEMFC stack, which 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).
Abstract: 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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TL;DR: In this paper, the use of a modified neural network algorithm (MNNA) is proposed as a novel adaptive tuning algorithm to optimize the controller gains and a new mathematical modulation is introduced to promote the exploration manner of NNA without initial parameters.
Abstract: The tuning of the robot actuator represents many challenges to follow a predefined trajectory on account of the uncertainties of parameters and the model nonlinearity. Furthermore, the controller gains require proper optimization to achieve good performance. In this paper, the use of a modified neural network algorithm (MNNA) is proposed as a novel adaptive tuning algorithm to optimize the controller gains. Furthermore, a new mathematical modulation is introduced to promote the exploration manner of the NNA without initial parameters. Specifically, the modulation is formed by using a polynomial mutation. The proposed algorithm is applied to select the proportional integral derivative (PID) controller gains of a robot manipulator arms in lieu of conventional procedures of designer expertise. Another vital contribution is formulating a new performance index that guarantees to improve the settling time and the overshoot of every arm output simultaneously. The proposed algorithm is evaluated with different intelligent techniques in the literature, including the genetic algorithm (GA) and the cuckoo search algorithm (CSA) with PID controllers, where its superiority to follow various trajectories is demonstrated. To affirm the robustness and efficiency of the proposed algorithm, several trajectories and uncertainties of parameters are considered for assessing the response of a robotic manipulator.
48 citations
11 Jun 2019
TL;DR: In this article, the authors explored the new meaning of integral and derivative actions, and gains, derived by the consideration of non-integer integration and differentiation orders, i.e., for fractional order PID controllers.
Abstract: The beauty of the proportional-integral-derivative (PID) algorithm for feedback control is its simplicity and efficiency. Those are the main reasons why PID controller is the most common form of feedback. PID combines the three natural ways of taking into account the error: the actual (proportional), the accumulated (integral), and the predicted (derivative) values; the three gains depend on the magnitude of the error, the time required to eliminate the accumulated error, and the prediction horizon of the error. This paper explores the new meaning of integral and derivative actions, and gains, derived by the consideration of non-integer integration and differentiation orders, i.e., for fractional order PID controllers. The integral term responds with selective memory to the error because of its non-integer order λ , and corresponds to the area of the projection of the error curve onto a plane (it is not the classical area under the error curve). Moreover, for a fractional proportional-integral (PI) controller scheme with automatic reset, both the velocity and the shape of reset can be modified with λ . For its part, the derivative action refers to the predicted future values of the error, but based on different prediction horizons (actually, linear and non-linear extrapolations) depending on the value of the differentiation order, μ . Likewise, in case of a proportional-derivative (PD) structure with a noise filter, the value of μ allows different filtering effects on the error signal to be attained. Similarities and differences between classical and fractional PIDs, as well as illustrative control examples, are given for a best understanding of new possibilities of control with the latter. Examples are given for illustration purposes.
47 citations
TL;DR: In this paper, the authors proposed a nonlinear model predictive control (NLMPC) controller for the nonlinear systems instead of the conventional MPC to track regular and irregular trajectories.
Abstract: The nonlinearities of the robotic manipulators and the uncertainties of their parameters represent big challenges against the controller design. Moreover, the tracking of regular and irregular trajectories with fewer overshoots, short settling time, and small steady-state error is the main target for the robotic response. The model predictive control (MPC) is an efficient controller to handle the performance requirements. However, the conventional MPC requires the linearization of the system model. The linearization of the model does not cover all dynamics of the robotic system. Thus, this paper introduces the nonlinear MPC (NLMPC) as a proper control method for the nonlinear systems instead of the conventional MPC. Specifically, this work proposes the use of NLMPC for controlling robotic manipulators. However, the NLMPC gains need proper tuning to attain good performance rather than the conventional methods. The neural network algorithm (NNA) considers a sufficient adaptive intelligent technique that can be utilized for this purpose. The restriction in a local optimum reveals the main issue versus artificial intelligence techniques. This paper suggests a new improvement to reinforce the exploration behavior of the NNA to overcome the local restriction issue. This modification is carried out by utilizing the polynomial mutation as an effective method to promise the exploration manner of the intelligence techniques. The proposed system can estimate all states from only the output to reduce the cost of the required sensors to measure all states. The results confirm the superiority of the proposed systems with the estimator with negligible change in the output response. The proposed modified NNA (MNNA) is evaluated with the main NNA, genetic algorithm-based PID control scheme, besides the cuckoo search algorithm-based PID control scheme from other works. The results confirm the robustness and effectiveness of the suggested MNNA-based NLMPC to track regular and irregular trajectories compared with other techniques.
40 citations
TL;DR: In this article , a comprehensive and systematic overview of state-of-the-art PEMFC control strategies is carried out, based on a thorough investigation of 180 literatures, these control strategies are classified into nine main categories, including proportional integral derivative (PID) control, adaptive control, fuzzy logic control (FLC), robust control, observer-based control, model predictive control (MPC), fault tolerant control (FTC), optimal control and artificial intelligence control.
37 citations
References
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TL;DR: In this article, a fractional-order PI/sup/spl lambda/D/sup /spl mu/controller with fractionalorder integrator and fractional order differentiator is proposed.
Abstract: Dynamic systems of an arbitrary real order (fractional-order systems) are considered. The concept of a fractional-order PI/sup /spl lambda//D/sup /spl mu//-controller, involving fractional-order integrator and fractional-order differentiator, is proposed. The Laplace transform formula for a new function of the Mittag-Leffler-type made it possible to obtain explicit analytical expressions for the unit-step and unit-impulse response of a linear fractional-order system with fractional-order controller for both open- and closed-loops. An example demonstrating the use of the obtained formulas and the advantages of the proposed PI/sup /spl lambda//D/sup /spl mu//-controllers is given.
2,479 citations
TL;DR: Two discretization methods for fractional-order differentiator s/sup r/ where r is a real number via continued fraction expansion (CFE) via the Al-Alaoui operator and a direct recursion of the Tustin operator are presented.
Abstract: For fractional-order differentiator s/sup r/ where r is a real number, its discretization is a key step in digital implementation. Two discretization methods are presented. The first scheme is a direct recursive discretization of the Tustin operator. The second one is a direct discretization method using the Al-Alaoui operator via continued fraction expansion (CFE). The approximate discretization is minimum phase and stable. Detailed discretization procedures and short MATLAB scripts are given. Examples are included for illustration.
543 citations
TL;DR: In this paper, the authors analyzed and designed air flow controllers that protect the fuel cell (FC) stack from oxygen starvation during step changes of current demand, and used linear optimal control design to identify the frequencies at which there is a severe tradeoff between the transient system net power performance and the stack starvation control.
Abstract: In this article we analyzed and designed air flow controllers that protect the fuel cell (FC) stack from oxygen starvation during step changes of current demand. The steady-state regulation of the oxygen excess ratio in the FCS cathode achieved by assigning an integrator to the compressor flow. Linear observability techniques were employed to demonstrate improvements in transient oxygen regulation when the FCS voltage is included as a measurement for the feedback controller. The FCS voltage signal contains high frequency information about the FC oxygen utilization, and thus, is a natural and valuable output for feedback. We used linear optimal control design to identify the frequencies at which there is a severe tradeoff between the transient system net power performance and the stack starvation control. The limitation arises when the FCS system architecture dictates that all auxiliary equipment is powered directly from the FC with no secondary power sources. This plant configuration is preferred due to its simplicity, compactness, and low cost. The FCS impedance given the closed-loop air flow and perfect humidification and temperature regulation captures the FC current-voltage dynamic relationship. It is expected that the closed-loop FCS impedance will provide the basis for the systematic design of FC stack electronic components.
471 citations
TL;DR: In this article, the authors developed a dynamic model suitable for the control study of fuel cell systems, including the flow and inertia dynamics of the compressor, manifold filling dynamics (both anode and cathode), reactant partial pressures, and membrane humidity.
Abstract: Fuel Cells are electrochemical devices that convert the chemical energy of a gaseous fuel directly into electricity. They are widely regarded as a potential future stationary and mobile power source. The response of a fuel cell system depends on the air and hydrogen feed, flow and pressure regulation, and heat and water management. In this paper, we develop a dynamic model suitable for the control study of fuel cell systems. The transient phenomena captured in the model include the flow and inertia dynamics of the compressor, the manifold filling dynamics (both anode and cathode), reactant partial pressures, and membrane humidity. It is important to note, however, that the fuel cell stack temperature is treated as a parameter rather than a state variable of this model because of its long time constant. Limitations and several possible applications of this model are presented.
410 citations
TL;DR: In this paper, a mathematical model is developed to simulate the transient phenomena in a polymer electrolyte membrane fuel cell (PEMFC) system, which can predict the transient response of cell voltage, temperature of the cell, hydrogen/oxygen out flow rates and cathode and anode channel temperatures/pressures under sudden change in load current.
328 citations