Multiobjective algebraic synthesis of neural control systems by implicit model following
IEEE Transactions on Neural Networks
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We derive a synthesis result for robust linear parameter varying (LPV) output feedback controllers for nonlinear systems modeled by neural state-space models. This result is achieved by writing the neural state-space model on a linear fractional transformation (LFT) form in a nonconservative way, separating the system description into a linear part and a nonlinear part. Linear parameter-varying control synthesis methods are then applied to design a nonlinear control law for this system. Since the model is assumed to have been identified from input-output measurement data only, it must be expected that there is some uncertainty on the identified nonlinearities. The control law is therefore made robust to noise perturbations. After formulating the controller synthesis as a set of linear matrix inequalities (LMIs) with added constraints, some implementation issues are addressed and a simulation example is presented