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This paper proposes a new Electric Power Steering (EPS) control strategy that enables remarkable progress on steering maneuverability for stationary vehicles. Using a conventional controller, undesirable steering vibration prevented us from reducing steering torque. To eliminate this vibration, we developed a new control strategy based on damping for specified frequency using a motor angular-velocity estimator. We experimented with this proposed control algorithm using a test vehicle and confirmed that it enables reduced steering torque without any perceived vibration for drivers. Concerning the gradient of the assist-map, the proposed control strategy enabled more than three times higher compared with that of the same type vehicles on the market as the test vehicle. This proposed control strategy requires only the torque sensor signal, supply voltage and current to the motor, which are used in the conventional EPS systems, so no supplemental sensors are required.

This paper proposes a new Electric Power Steering (EPS) control strategy that enables improvement to steering-wheel returnability. Using a conventional EPS controller, frictional loss torque in the steering mechanism reduces steering-wheel returnability, which drivers occasionally perceive as unpleasant. This phenomena occurs in any EPS system regardless of motor type or mounting location. To improve steering-wheel returnability for EPS-equipped vehicles, we developed a new control strategy based on estimation of alignment torque generated by tires and road surfaces. This proposed control strategy requires no supplemental sensors like steering-wheel angle or motor-angle sensors. We experimented with this proposed control algorithm using a test vehicle and confirmed that it enables improved steering wheel returnability and also better on-center feeling.

This paper presents a new motor current control strategy for Electric Power Steering (EPS) to reduce current fluctuation. Such current fluctuation may cause undesirable steering torque ripple and acoustic noise, if an inexpensive microprocessor is used. Using a DC-motor, current fluctuation associated with change in the battery voltage, etc., may occur. We have developed a new current control strategy which effectively alleviates current fluctuations of the motor without using higher performance microprocessors. The new controller is based on the estimation of disturbance voltage and compensation for this disturbance voltage. We have bench-tested the performance of this control strategy and confirmed that current fluctuation is reduced below that using conventional PI controller. The PI gain for the proposed controller is the same as that for the conventional controller.

In this paper, an application of variable structure systems (VSS)(1) to electromagnetic-clutch (EMC) control for starting vehicles is studied. Using a conventional open-loop controller, there is the case that changes in EMC dynamics lead to undesirable vehicle vibration though it may be rare. First, to overcome the above problem, we developed a control strategy based on VSS. The VSS control is robust with respect to changes in EMC dynamics. Second, we discussed the chattering problem of this controller in application to actual vehicle control. Finally, we confirmed the validity of the proposed control strategy and the appropriateness of the conditions for reduced chattering derived by simulation. The validity of this control strategy was also confirmed experimentally.

This paper proposes a vehicle state detection method for improving the stability of vehicles equipped with electric power steering (EPS) and electronic stability control (ESC) systems. ESC is an effective vehicle stability control system that operates within a vehicle's stability limitations. Generally ESC uses a vehicle state signal such as yaw rate. To enhance the ESC function so that it can alleviate understeer, a process that is capable of detecting understeer is required. This concept motivated us to develop a vehicle state detection algorithm based on estimated self-aligning torque using EPS. It is well known that maximum self-aligning torque occurs before maximum cornering force is reached. We have confirmed that the proposed algorithm can detect understeer earlier than conventional means based on vehicle yaw rate.

In this paper, we suggest a novel algorithm to distinguish semi-steady states from various steering patterns and to estimate the stability factor. The algorithm also estimates each stability factor in left and right turns because there could be a case where they differ based on uneven tire wear and so on. The stability factor, which is the turning characteristic of a vehicle, has been treated as constant for most vehicle control systems. However, in fact, it may change in some situations, for example when a vehicle is overloaded. So there is a chance that a driver may be aware of an unusual sensation when vehicle control is designed based on a constant stability factor. We have succeeded in developing an algorithm to estimate the stability factor accurately enough to be able to compensate for it and have confirmed the effectiveness of the algorithm by simulation and vehicle testing as well.

Steady advances in autonomous vehicle development are expected to lead to improved traffic flow in terms of string stability compared with that for human-driven vehicles. Fluctuation in intervehicle distances among a group of vehicles without string stability is amplified as it propagates upstream (rearward), which may cause traffic congestion. Since it will take a few decades for autonomous vehicles to replace all human-driven vehicles, it is important to tackle the problem of traffic congestion in a mixed flow of human-driven and autonomous vehicles. Communication technologies such as fifth-generation mobile communication systems, which are improving rapidly, enable vehicle-to-vehicle communication with a sufficiently small delay. We previously reported a strategy based on vehicle-to-vehicle communication for avoiding traffic congestion by using leader–follower control, which is a distributed autonomous control strategy.