The inverse controller is traditionally assumed to be a deterministic function. This paper presents a pedagogical methodology for estimating the stochastic model of the inverse controller. The proposed method is based on Bayes' theorem. Using Bayes' rule to obtain the stochastic model of the inverse controller allows the use of knowledge of uncertainty from both the inverse and the forward model in estimating the optimal control signal. The paper presents the methodology for general nonlinear systems and is demonstrated on nonlinear single-input-single-output (SISO) and multiple-input-multiple-output (MIMO) examples.
|Number of pages||8|
|Journal||IEEE Transactions on Neural Networks and Learning Systems|
|Publication status||Published - Jan 2007|
- distribution modelling
- inverse controller
- neural networks
- stochastic systems