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Higher Order Neural Units for Efficient Adaptive Control of Weakly Nonlinear Systems

The paper reviews the nonlinear polynomial neural architectures (HONUs) and their fundamental supervised batch learning algorithms for both plant identification and neuronal controller training. As a novel contribution to adaptive control with HONUs, Conjugate Gradient batch learning for weakly nonlinear plant identification with HONUs is presented as efficient learning improvement. Further, a straightforward MRAC strategy with efficient controller learning for linear and weakly nonlinear plants is proposed with static HONUs that avoids recurrent computations, and its potentials and limitations with respect to plant nonlinearity are discussed.