- This paper attempts to present a neural inverse
control design framework for a class of nonlinear
multiple-input multiple-output (MIMO) system with
uncertainties. This research effort is motivated by the following considerations: (a) An appropriate reference model that accurately represents the desired system dynamics is usually assumed to exist and to be available, and yet in reality this is not the case often times; (b) In real world applications, there are many cases where controls are constrained within a physically allowable range, which presents another layer of difficulties to directly apply the reference model based inverse control; (c) It is difficult to
consider optimal control even for the reference model as in general the analytic solution to the optimal control problem is not available. The simulation study has been focused on the identification of Multiple Input, Single Output (MISO) non-linear complex systems. This paper concentrates on the identification of Multiple Input Multiple Output (MIMO) system by means of a hybrid-learning rule, which combines the back propagation and the Least Mean Squared (LMS) to identify parameters. We construct a neuro fuzzy model
structure, and generate the membership function from the measured data. The MIMO system model is represented as a set of coupled input-output MISO models of the Takagi- Sugeno type. Neuro fuzzy model of the system structure is incorporated easily in the structure of the model. The simulation is used to implement a MIMO spacecraft system using Matlab for moment_yaw, moment_pitch, and moment_roll as input, and velocity in inertial axis as output.
Experimental results are given to show the effectiveness of this Adaptive Neuro Fuzzy System (ANFIS) model.