Agile Tyre Mobility: Observation and Control in Severe Terrain Environments

Abstract

This research study develops fundamentals for a new ground vehicle technology to radically improve and protect off-road vehicle mobility by providing agile (fast, exact and pre-emptive) responses and advanced mobility controls in severe terrain conditions. The current framework of terrain vehicle mobility that estimates a vehicle capability “to go through” or “not to go through” the given terrain conditions cannot provide an analytical basis for novel system design solutions. Indeed, modern traction control and other mobility related electronic control systems possess control response time within the range of 100–120 milliseconds and greater. With this response time, the actual control occurs after the vehicle has reached a critical motion situation, e.g., a wheel(s) is/are spinning and the vehicle is already losing its mobility. In this study, the developed methods allowed for estimating tyre mobility and controlling tyre motion before the tyre starts spinning. As shown in the conducted analysis, the response time, which occurs within the longitudinal tyre relaxation time constant of 40–60 ms, is sufficient for a tyre to avoid spinning and to maintain its required mobility. Most common traditional approaches to observation of data supplied by virtual sensors were simulated and improved by means of machine learning algorithms. Computational simulations of an one-wheel-locomotion module driven by an electric driveline system demonstrated a sufficient performance of the proposed observation method to estimate mobility margins of the module in real time. A hybrid intelligent control algorithm was designed, in which reinforcement learning was used to fine-tune the parameters of a fuzzy logic controller. A new wheel mobility index was utilized as a cost function to guarantee a designed behavior of the locomotion module. A fuzzy corrector was additionally designed to take into account both the dynamic state of the system and the dynamics of the tyre-terrain interaction. The fuzzy corrector supports upper level controls of autonomous vehicle dynamics by decreasing tyre slippage on severe terrains. Computer simulations testified both stability of the controller (due to utilization of fuzzy logic polynomial control) and its desired performance (due to application of reinforcement learning). The fine-tuned controller requires minimal online computations. This paper provides an extended summary of the above-listed research studies. Further details can be found in publications referenced in the paper.