Abstract
The paper deals with the implementation of optimized neural networks (NNs) for state variable estimation of the drive system with an elastic joint. The signals estimated by NNs are used in the control structure with a state-space controller and additional feedbacks from the shaft torque and the load speed. High estimation quality is very important for the correct operation of a closed-loop system. The precision of state variables estimation depends on the generalization properties of NNs. A short review of optimization methods of the NN is presented. Two techniques typical for regularization and pruning methods are described and tested in detail: the Bayesian regularization and the Optimal Brain Damage methods. Simulation results show good precision of both optimized neural estimators for a wide range of changes of the load speed and the load torque, not only for nominal but also changed parameters of the drive system. The simulation results are verified in a laboratory setup.
Highlights
In most electrical drives, the elasticity of the shaft between a driving motor and a load machine must be taken intoIn many applications connected with electrical drives, algorithmic methods are applied for the non-measurable state variables estimation, for example, the Kalman filters [4, 5] and the Luenberger observers [6]
This paper presents neural estimators of the torsional torque and the load machine speed for a drive system with elastic joints
The tested drive system with an elastic joint is emulated with two DC machines (0.5 kW each) connected by an
Summary
The elasticity of the shaft between a driving motor and a load machine must be taken intoIn many applications connected with electrical drives, algorithmic methods are applied for the non-measurable state variables estimation, for example, the Kalman filters [4, 5] and the Luenberger observers [6]. Alternative ways of solving this problem are estimators based on neural networks (NNs) Such estimators do not need a mathematical model and parameters of the system, only the training data are required [7,8,9] for the estimator design. In the case of NN applications in state variable estimation, the determination of NN structure for a specific task is one of the most important problems This structure should be carefully chosen to obtain good estimation quality in the case of NN input data different than those used in the training procedure. It means that a suitable generalization ability is required. It is possible to distinguish three main trends [7]:
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