Abstract
It is a challenge to identify the parameters of a mechanism model under real-time operating conditions disrupted by uncertain disturbances due to the deviation between the design requirement and the operational environment. In this paper, a novel approach based on reinforcement learning is proposed for forging machines to achieve the optimal model parameters by applying the raw data directly instead of observation window. This approach is an online parameter identification algorithm in one period without the need of the labelled samples as training database. It has an excellent ability against unknown distributed disturbances in a dynamic process, especially capable of adapting to a new process without historical data. The effectiveness of the algorithm is demonstrated and validated by a simulation of acquiring the parameter values of a forging machine.
Highlights
Introduction for Forging Machine UsingComplex engineering systems are with a high requirement for system reliability and control and production performance
The control algorithms have made great progress from conventional PID-based algorithms [5] to advanced model-based control algorithms, including sliding mode control [6,7], back-stepping control [8], and feedback linearization [9], in order to obtain higher performance. The effects of these control algorithms strongly depend on the accuracy of the mechanism model
In [10,11], fuzzy-based control was proposed by using fuzzy rules instead of the mechanism model, but it cannot achieve the requirement of high precision
Summary
Complex engineering systems are with a high requirement for system reliability and control and production performance. A forging machine is often working on batch processes whose parameters are different in each batch, and are even impossible to be known for new forging pieces This means the parameters of the mechanism model for a forging machine will need to be determined from as few data as possible. From the perspective of data effectiveness, the classical parameter identification methods, whether offline estimation or online correction, are based on the least squares concept with the assumption of data following a normal distribution. It is a challenge to determine the parameters of a model for a forging machine online to meet the needs of a complex environment. In the case of a forging machine, it is a feasible approach to find the optimal values of the model parameters in a new condition under disturbances.
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