This paper presents multi-input multi-output (MIMO) generalized predictive control (GPC) law and its application to reconfigurable control. A method to compute the desired end-point state from the desired output and end-point state weighting matrix is given. In particular, an application to flight control with actuator saturation failure is presented. A numerical simulation using a short-period approximation model of a civil transport aircraft is presented to demonstrate the reconfigurable control architecture.

A recurrent backpropagation neural network suitable for modelling dynamical systems is analyzed. It is shown that the weight matrices of the neural network model determine, with reasonable accuracy, the impulse response of the modelled dynamical system. By analyzing this impulse response with the eigensystem realization algorithm (ERA), one can obtain state-space representation of the original system. Simulation results are presented

This paper presents a status report on the experimental results of the transonic wind-tunnel test conducted to demonstrate the use of generalized predictive control for flutter control of a subsonic airfoil. The generalized predictive control algorithm is based on the minimization of a suitable cost function over a finite prediction horizon. The cost function minimizes the sum of the mean square output of the plant predictions using a suitable plant model, weighted square of control increments, and the term which incorporates the input constraints. The characteristics of the subsonic airfoil are such that its dynamics are invariant to low input frequencies. This results in a control surface that drifts within the specified input constraints. An augmentation to the cost function that penalizes this low frequency drift is derived and demonstrated. The initial validation of the controller uses a linear plant predictor model for the computation of the control inputs. The generalized predictive controller based on this model could successfully suppress the flutter for all testable mach numbers and dynamic pressures in the transonic region. The wind-tunnel test results confirmed that the generalized predictive controller is robust to modeling errors. The simulation results that were used to determine the nominal ranges for control parameters before wind-tunnel testing are also included. The wind-tunnel test results were in good agreement with the results of the simulation.