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The normalized input neural networks are considered for reliable computation of the adaptive input.
Finally, control reconfiguration is performed exploiting the inherent redundancy of actuators with the implementation of an adaptive input allocation architecture.
In this paper, the authors formulate and study a strategy to also have the embedding dimension vary, therefore providing full adaptive input space capabilities.
The adaptive input design (also called online redesign of experiments) for parameter estimation is very effective for the compensation of uncertainties in nonlinear processes.
Based on a Neuro-Fuzzy Controller (NFC) architecture, two approaches are presented for the design of a Power System Stabilizer (PSS) with adaptive input scaling.
This work proposes an adaptive control scheme applied to single link-flexible manipulators, which combines a feedback controller of the joint angle with an adaptive input shaper updated by an algebraic non-asymptotic identification.
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Plain stimuli may drive networks to the nearest equilibrium that accommodates this input, whereas adaptive stimulation may direct the space for exploration and force networks to a new balance, at a larger distance from the initial state.
An additional adaptive control input supported by neural networks can estimate and eliminate unknown terms adaptively.
In delayless ANC applications with a secondary path S z), the adaptive filter input vector x(m) is first filtered by an estimate of the secondary path.
Subsequently, a bank of adaptive unknown input observers activated by the detection results are designed to isolate which actuator is faulty and then estimate of the fault parameter.
More specifically, the adaptive unknown input observer is exploited to estimate state and fault simultaneously, and the adaptive output integral-type sliding mode is designed to attenuate unknown bounded uncertainty and tolerate time-varying fault.
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