基于人工鱼群算法的车辆平顺性优化分析.

Ride comfort is of great importance feature for the heavy truck, its optimization can improve the driver's driving conditions to reduce fatigue, and make the goods safer. Primary factors that can influence ride comfort are form and parameters of suspension, which are suspension stiffness, suspension damp and their combination. When the form of suspension is confirmed, more reasonable parameters can be selected by optimization method to improve ride comfort. Ride comfort optimization analysis belongs to the combinatorial optimization problem, at the same time, the nonlinear characteristics in optimization is essentially a nonlinear multimodal optimization problem. In this paper, at first, a nine degree of freedom vehicle vibration model was established; Vehicle driver seat acceleration simulation tests were conducted with different vehicle speed. Also, both time and frequency domain analysis was implemented with MATLAB software development platform. On the whole, with the increase of the speed of the vehicle, the acceleration root mean square of vehicle driver seat became larger, so the vehicle ride comfort performance reduced. Especially at low speed and high acceleration change is more obvious. But in 40~80 km/h, the acceleration change quite gentle. That means to achieve the better economy and the vehicle ride comfort performance, the vehicle speed keeping in a medium speed is better. Based on C level road and the speed of 70 km/h, with an eight by four dump truck as experimental object, the ride comforttests were conducted, moreover the test results compared with the results of simulation. The compared results showed that the simulation and the test were very close. And then, today technology was coming to a stage of intersection, infiltration, and interaction with multi subjects. More and more issues on complexity, non linearity, and system have come to us. To deal with such complexity of system, conventional techniques have become incapable, and to seek an optimization algorithm, which adapt to large scale parallel with intelligent characteristics, has been a primary research target of related subjects. The artificial fish algorithm was proposed to optimize ride comfort. The artificial fish swarm algorithm (AFSA), anew method based on animal behaviors and the typical application of behaviorism artificial intelligence, was proposed byan internal scholar in recent years. It used the operators such as prey, swarm, follow and random behavior. The algorithm parameters, such as population, step size, sense of distance, the largest try number, crowded degree coefficient and the largest number of iterations, has a great impact on the performance of the convergence. At the end, the artificial fish algorithm was used to optimize ride comfort by reasonable selection of the suspension parameters. The objective function was the acceleration root mean square of vehicle driver seat to be minimized. The decision variables were front suspension stiffness and damp. Moreover AFSA need to set up the appropriate algorithm parameters. For example, population scale, step size, sense of distance, the largest try number, crowded degree coefficient and the largest number of iterations was 100, 100, 20 000, 100, 9 and 50. Where the population scale N was called the number of possible values of suspension parameters within the value range, step size was suspension parameters increasing or decreasing the amount of each iteration, and sense of distance visual was variables scope of each iteration. Optimization results show that the acceleration root mean square average fell by 16.82%, the biggest fell by 21.24% in 60 km/h, so it effectively improves the ride comfort h performance of heavy vehicles. [ABSTRACT FROM AUTHOR]