In recent years, research on machine learning techniques that can be integrated with existing suspension control algorithms for enhanced control effects has advanced considerably. Machine learning, especially involving neural networks, often requires many samples, which makes maintaining robust performance in diverse, changing environments challenging. The present study applied reinforcement learning, which can generalize complex situations not previously encountered, to overcome this obstacle and is crucial for suspension control under varying road conditions. The effectiveness of the proposed control method was evaluated on different road conditions using the quarter-vehicle model. The impact of training data was assessed by comparing models trained under two distinct road conditions. In addition, a validation exercise on the performance of the control method that utilizes reinforcement learning demonstrated its potential for enhancing the adaptability and efficiency of suspension systems under various road conditions.

Velocity ripple in manufacturing processes reduces productivity and limits the precision of the product. In practice, the frequency and phase of velocity ripples always change minutely, which makes it impossible to compensate for the ripple by simply inserting an opposite feed-forward signal in the system. In this study, an active-phase compensation algorithm was developed to enable the velocity-ripple controller to track the phase change of the ripples in real time. The proposed controller can compensate for the velocity ripple whatever its cause, including disturbance by the torque ripple. The algorithm consists of three functional modules: the velocity-ripple extractor, the synchronized integrator, and the phase shifter. Experimental results showed that the proposed controller clearly reduces velocity ripples with phase variation.