Abstract
Electro-pneumatic actuators play an essential role in various areas of the industry, including heavy-duty vehicles. This article deals with the control problem of an Automatic Manual Transmission, where the actuator of the system is a double-acting floating-piston cylinder, with dedicated inner-position. During the control design of electro-pneumatic cylinders, one must implement a set-valued control on a nonlinear system, when, as in the present case, non-proportional valves provide the airflow. As both the system model itself and the qualitative control goals can be formulated as a Partially Observable Markov Decision Process, Machine learning frameworks are a conspicuous choice for handling such control problems. To this end, six different solutions are compared in the article, of which a new agent named PG-MCTS, using a modified version of the “Upper Confidence bound for Trees” algorithm, is also presented. The performance and strategic choice comparison of the six methods are carried out in a simulation environment. Validation tests performed on an actual transmission system and implemented on an automotive control unit to prove the applicability of the concept. In this case, a Policy Gradient agent, selected by implementation and computation capacity restrictions. The results show that the presented methods are suitable for the control of floating-piston cylinders and can be extended to other fluid mechanical actuators, or even different set-valued nonlinear control problems.
Highlights
The popularity of autonomous functions is continuously increasing in the heavy-duty vehicle industry
SIMULATION RESULTS This section presents further training details and the performance and strategy choice comparison of the different methods. This requires that all data for the performance figures are generated by applying all methods for the same seed of environmental parameters, for 10000 simulations, to ensure representativity and to enable in-depth comparison
Data preparation and hyperparameter optimization are crucial parts of supervised learning; the training samples generated by the Monte-Carlo Tree Search (MCTS) algorithm are normalized, filtered, and shuffled
Summary
The popularity of autonomous functions is continuously increasing in the heavy-duty vehicle industry. Along with advanced driver assistance systems (ADAS), they are expected to increase fuel efficiency and decrease emissions while enhancing safety [1] Typical examples of these systems are platooning [2], automated highway driving [3], and autonomous yard maneuvering [4], having large influence on the future of transportation systems [5]. In the case of electro-pneumatic actuators, the primary sources of nonlinearities come from the air’s friction and compressibility. Despite these, they are widely used in robotics and heavy-duty vehicles, as they have high power density, simple maintainability, and high operational safety.
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