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This paper studies a new active vehicle suspension controlled by Fuzzy-PID controller with wheel base preview. By this new algorithm, the fuzzy controller controls the parameters of the PID in time .Then the wheelbase preview is integrated to ensure the future road information is combined with the current state of the vehicle effectively. A sensor is placed on the front suspension collects and feeds forward the preview information as an input to the rear suspension system . MATLAB simulations show that using such control strategy can obtain a low noise and better robustness performance than the traditional PID control algorithm.

The current practice of gasoline engine emission examination is partially based on λ reading. For the λ value of a car over a specific standard, it indicates the car failed the examination. Although modern automobiles have built-in λ sensors to monitor the efficiency of the catalytic converter and can warn the driver when the converter breaks down, mandatory vehicle examination cannot be taken every day for each car. The concept of telemetry with traffic lights is proposed that allows λ data collected from the vehicle on road to be reported immediately at a traffic light. The data can be sent via a radio transmitter in the vehicle to a radio receiver mounted on the traffic light which is connected to a server. So, the authority can monitor the engine emissions via computer networks while the vehicles stop at red traffic lights. Experimental results show that the idea is feasible.

Engine power, brake-specific fuel consumption, and emissions relate closely to air ratio (i.e., lambda) among all the engine variables. An accurate and adaptive model for lambda prediction is essential to effective lambda control for long term. This paper utilizes an emerging technique, relevance vector machine (RVM), to build a reliable time-dependent lambda model which can be continually updated whenever a sample is added to, or removed from, the estimated lambda model. The paper also presents a new model predictive control (MPC) algorithm for air-ratio regulation based on RVM. This study shows that the accuracy, training, and updating time of the RVM model are superior to the latest modelling methods, such as diagonal recurrent neural network (DRNN) and decremental least-squares support vector machine (DLSSVM). Moreover, the control algorithm has been implemented on a real car to test. Experimental results reveal that the control performance of the proposed relevance vector machine model predictive controller (RVMMPC) is also superior to DRNNMPC, support vector machine-based MPC, and conventional proportional-integral (PI) controller in production cars. Therefore, the proposed RVMMPC is a promising scheme to replace conventional PI controller for engine air-ratio control.