Temperature field optimization control of roller kiln based on parameter estimation error

The temperature of roller kiln sintering process is the key to determine the quality of lithium-ion battery cathode materials. According to limited measurement points, it is difficult to establish a model describing the temperature field, and leads to the control failure. Parameter estimation is generally required during controller design. Most of existing methods rely on states error information of observer/predictor, which cannot directly reflect the variation of parameters to be estimated. The accuracy of these methods depends on the observer/predictor performance. This paper proposes a temperature field adaptive dynamic programming (ADP) optimal control method based on parameter estimation error. Firstly, the energy conservation equation of two-dimensional porous media is established based on heat transfer mechanism, and the boundary conditions including view actors are constructed to reflect heat radiation. Considering that the vertical direction temperature varies greatly, a one-dimensional temperature field model is established by transforming boundary conditions, and the model parameters are determined according to cathode materials. Then, policy iteration (PI) method of ADP is used to design optimal control method. A neural network (NN) is used in critic network to approximate cost function. Defining parameter estimation errors based on differences between estimated and true values of NN weights, and integrating the error information into updating process, a new weight updating algorithm is proposed to improve the convergence of parameter errors as well as the rapid convergence of NN weights in a finite time. Finally, the effectiveness of the proposed methods is verified by simulation. © 2022 South China University of Technology. All rights reserved.