'Institute of Electrical and Electronics Engineers (IEEE)'
Abstract
The paper proposes a multilayer perceptron neural network controller for dual adaptive control of a class
of stochastic MIMO nonlinear systems subject to functional
uncertainty. The neural network parameters are adjusted in
real-time using the Unscented Kalman filter algorithm and
no pre-operational training phase is required. Dual adaptive control aims to strike a compromise between the two
control characteristics of caution and probing, leading to
an improved overall performance. The system is evaluated
through numerical simulations and Monte Carlo analysis. The
resulting performance of the dual adaptive controller is not only
consistently superior to non-dual adaptive control schemes, but
also surpasses the performance of similar controllers that are
based on Extended Kalman filter estimators. This reflects the
enhanced accuracy of the Unscented Kalman filter estimator,
despite being a local estimation method. In addition, unlike use
of other estimators, the proposed approach neither requires the
computation of complex Jacobian matrices as part of the design,
nor the evaluation of such matrices in real-time. This renders
the proposed controller inherently amenable and practical for
real-time implementation.peer-reviewe
