Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> B. Idle Speed Control <s> In this paper, a MIMO nonlinear discrete time model of the idle mode of an SI engine is first developed using a fuzzy logic model in the form of a nonlinear radial basis function expansion. The inputs are the throttle angle and the ignition advance and the outputs are the idle speed and manifold pressure. The model is validated using nonlinear correlation tests for MIMO systems. Phase portrait analysis is used to study the global dynamic behavior of the plant, in order to find periodic motions and equilibrium points of the system. Optimal state trajectories are used as entries to a fuzzy logic expansion to approximate the distorted control surface of the plant. The resulting FAM or fuzzy controller smoothens the control surface, and consequently, the transitions of the state trajectories from a different initial condition to another in its path to the equilibrium. Both the model and controller parameters are optimized using a modern version of an evolutionary algorithm, called covariance matrix adaptation-evolution strategies (CMA-ES). <s> BIB001 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> B. Idle Speed Control <s> Abstract This paper presents a control technique based on a directly identified non-linear inverse of a stable system. A NARMA model of the inverse of the uncontrolled system is identified using reversed input–output data collected experimentally from the plant. Linear identification is carried out on the plant which is pre-compensated by the identified inverse, sufficiently time delayed to be causal. This achieves a reduction in the uncertainty representation necessary to model the non-linearity. Subsequent robust linear controller design results in an improvement in robust performance when using the inverse pre-compensator. The method is applied to the automotive SI engine idle-speed control problem using a parameter space linear robust control design technique. The result is experimentally validated using an electric dynamometer. <s> BIB002 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> B. Idle Speed Control <s> Idle speed control in a fuel-injection engine system has focused on controlling long-term averages of engine speed, but short-term fluctuations of engine speed have been neglected. The torque differences among cylinders influence the idle stability and cause vibration of the vehicle. In this paper, we introduce two intelligent control systems to reduce the fluctuations of engine speed at idle, an evolutionary computing control based on genetic algorithms and a stochastic control based on Alopex algorithm. We first estimate the torque differences among the cylinders by observing an engine cycle of crankshaft angular speed. Then the uniformity level over the engine speed is fedback into the control system. It manipulates spark ignition timings to suppress unbalanced combustions among the cylinders. We test the two adaptive approaches with simulation of a nonlinear engine model, and compare their performances. <s> BIB003 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> B. Idle Speed Control <s> PID control systems are widely used in many fields, and many methods to tune parameters of PID controller are known. When the characteristics of the object are changed, the traditional PID control should be adjusted by empirical knowledge. It may bring a worse performance to the system. In this paper, a new method to tune PID parameters called as the modified back propagate network by particle swarm optimization is proposed. This algorithm combines the conventional PID control with the back propagate neural network (BPNN) and the particle swarm optimization (PSO). This method is demonstrated in the engine idle-speed control problem, and the proposed method provides prominent performance benefits over the traditional controller in this simulation. <s> BIB004 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> B. Idle Speed Control <s> PID control systems are widely used in many fields, and many methods to tune the parameters of PID controllers are known. When the characteristics of the object are changed, the traditional PID control should be adjusted by empirical knowledge. This may result in a worse performance by the system. In this article, a new method to tune PID parameters, called the back-propagation network modified by particle swarm optimization, is proposed. This algorithm combines conventional PID control with a back propagation neural network (BPNN) and particle swarm optimization (PSO). This method is demonstrated in the engine idling-speed control problem. The proposed method provides considerable performance benefits compared with a traditional controller in this simulation. <s> BIB005
Idle speed control of engines is to stabilize the engine speed at a desirable level. The aim is to prevent the crankshaft from oscillating, as it results in vibrations of the components of the vehicle body. A number of researchers have employed metaheuristic algorithms in idle speed control of car engines. In one of the first attempts in this regard BIB001 , a fuzzy control was proposed for the problem. They then used the covariance matrix adaptation evolution strategy to optimize the model and the controller parameters. In BIB002 , to control the idle speed of a SI engine, a directly identified nonlinear inverse of a stable system was proposed. The stable identification method was implemented by prediction error minimization using a genetic algorithm for which the model simulation was used to evaluate an root mean square (RMS) error performance function. Most research in idle speed control has been to control the long-term averages of engine speed, but short-term fluctuations of engines stemming from the torque differences among cylinders has been neglected by many researchers. One of the first research targeting this issue was BIB003 , in which two intelligent control systems were introduced. The intelligent systems were an evolutionary controller based on genetic algorithms and a stochastic controller based on the Alopex algorithm. The Alopex algorithm is a stochastic parallel algorithm which was originally suggested in  for visual receptive field mapping. In this method, first by observing the engine cycle of crankshaft angular speed, the torque differences among the cylinders was estimated. Then the uniformity level over the engine speed was feedback into the control system and the SI timing was manipulated to suppress any unbalanced combustions among the cylinders. The authors tested their system on a simulation of a nonlinear engine model and reported good performance. The proportional integral derivative (PID) controllers are widely used in many fields and a number of methods to tune the parameters of the controllers have been proposed. One of these methods was proposed in BIB004 and BIB005 , where a modified back propagate network by PSO was combined with a PID controller. The experimental results showed the superiority of their proposed method over traditional controllers.