  In this paper a new framework has been applied to the design of controllers
which encompasses nonlinearity, hysteresis and arbitrary density functions of
forward models and inverse controllers. Using mixture density networks, the
probabilistic models of both the forward and inverse dynamics are estimated
such that they are dependent on the state and the control input. The optimal
control strategy is then derived which minimizes uncertainty of the closed loop
system. In the absence of reliable plant models, the proposed control algorithm
incorporates uncertainties in model parameters, observations, and latent
processes. The local stability of the closed loop system has been established.
The efficacy of the control algorithm is demonstrated on two nonlinear
stochastic control examples with additive and multiplicative noise.
