Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> A. Air-to-Fuel Ratio Control <s> In this paper, we present our work on self-learning engine control using "derivative" adaptive critics. In the derivative version of adaptive critic designs, the derivative of the cost function of dynamic programming with respect to the state of the process is estimated. As the goal of dynamic programming is to maximize the overall cost function, direct estimation of the cost function derivatives will naturally lead to better algorithm performance. In our previous studies of the regular version of adaptive critic techniques, a model of the process for controller training was not necessary. However, for the implementation of the "derivative" version of adaptive critic designs, a process model in the form of neural network is required. In this way, the derivative information can be propagated back to the controller in order to update the controller parameters. The objectives of the present learning control design for automotive engines are to improve performance, reduce emissions and maintain optimum performance under various operating conditions. Using data from the test vehicle equipped with a 5.3 L V8 engine, we built a neural network model of the engine. The model is then used in the development of self-learning neural network controllers based on the idea of approximate dynamic programming to achieve optimal control for both engine torque and exhaust air-fuel ratio control. The goals of the engine torque and air-fuel ratio control are to track the commanded torque and to regulate the air-fuel ratio at specified set-points, respectively. For both control problems, reasonably good neural network controller transient performance under a wide range of operating conditions have been achieved and reported. <s> BIB001 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> A. Air-to-Fuel Ratio Control <s> Because the oxygen sensor is installed into the vent-pipe of gasoline engine, the air fuel ratio signal of gasoline engine exists transmission delay, which affects the control accuracy of air fuel ratio if using directive air fuel ratio sensor signal. To overcome air fuel ratio transmission delay affection, a new air fuel ratio predictive control method was provided using adaptive expanded particle optimization in this paper. Particle is refreshed using individual and local extremum in the basic PSO algorithm. To improve the global convergence, particle is refreshed by multi-particle strategy; at the same time, parameter c0 is adaptive adjusted for fast the convergence of PSO algorithm. Applying adaptive expand PSO algorithm optimize the control serial of air fuel ratio in the finite time field, and control system stability proof is presented. The simulation was accomplished using experiment data of HQ495 gasoline engine, and the results show that the predictive control method has better performance and the air fuel ratio error is below 1% if slower throttle change, and the air fuel ratio error is below 2% if faster throttle change during transient condition, which will help to improve the emission of gasoline engine. <s> BIB002 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> A. Air-to-Fuel Ratio Control <s> Various mathematical models for the air to fuel ratio and control for spark ignition (SI) engines have been proposed to satisfy technical specifications. This paper reveals an improvement of the mean value model (MVEM) and a simple yet effective nonlinear control to enhance the air to fuel regulator. The regulator is designed by using a discrete fuzzy PI algorithm, which provides easy tuning, robustness, and rapid development with a simple architecture. Effects on the dead time, exhausted delay, time-varying air flow and chaotic disturbance are also included. The computer simulation results show satisfactory performance based on standard evaluation criteria. The proposed model has a great potential for practical implementations. <s> BIB003
Air-to-fuel ratio affects the power, torque, speed, emission, and catalytic efficiencies in engines. Thermal efficiency leads to low fuel consumption, while a high catalytic efficiency results in a low exhaust emission BIB003 . Although the catalyzers provide a good treatment for the exhaust gases, there still exists a need toward high performance air-to-fuel ratio controllers. In order to improve the performance and reduce emissions of a 5.3L V8 engine, a novel heuristic dynamic programming algorithm was proposed in BIB001 , which estimates cost function derivatives to design a more informed dynamic optimization algorithm. A neural network was employed in the research to estimate the derivative of the cost function. Their goal was to track the commanded torque and to regulate the air-to-fuel ratio at specified set points. As the oxygen sensor of gasoline engines was installed into the vent-pipe, there was a delay in the air-to-fuel ratio signal when reaching the control system. To overcome this, using an adaptive extended PSO a new air-to-fuel ratio predictive control method was proposed in BIB002 . To improve the global convergence of the algorithm, a multiparticle strategy and an adaptive control of the algorithm's parameters were used. Using data acquired from a HQ495 gasoline engine, the algorithm was tested on a simulation of the engine and the results suggested an improved performance when using the predictive control system.