## What is/are Composite Learning?

Composite Learning - Once the states run out of the domain of intelligent control, the robust item will pull the states back while inside the neural working domain, the composite learning is developed to achieve higher approximation precision by building the prediction error for the weight update.^{[1]}This paper investigates the robust adaptive neural control of nonminimum phase hypersonic flight vehicle using composite learning.

^{[2]}The proposed scheme can guarantee at least the similar control performance compared with its continuous alternatives while it importantly reduces the communication burden through the ETC and can enhance the estimation accuracy of the observer by applying the composite learning.

^{[3]}This paper investigates a fault-tolerant control of the hypersonic flight vehicle using back-stepping and composite learning.

^{[4]}Furthermore, the composite learning, which combines nonlinear disturbance observer and direct adaptive neural control, is applied to improve the approximation performance and enhance system robustness.

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## Novel Composite Learning

In addition, a novel composite learning-based controller for each turbine is designed to achieve closed-loop yaw tracking, which can guarantee the exponential convergence of tracking errors in the presence of uncertainties of yaw actuators.^{[1]}In this article, a novel composite learning control scheme based on nonlinear disturbance observer (NDOB), neural network (NN), and model-based state observer (MSOB) is investigated for the manned submersible vehicle.

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## composite learning control

In this article, a novel composite learning control scheme based on nonlinear disturbance observer (NDOB), neural network (NN), and model-based state observer (MSOB) is investigated for the manned submersible vehicle.^{[1]}In this brief, a novel OHD-driven parameter estimation scheme that exploits only partial OHD is presented to improve parameter convergence and is incorporated with direct adaptive control to construct a composite learning control strategy.

^{[2]}In this study, a composite learning control strategy is proposed for a class of strict-feedback nonlinear systems with mismatched uncertainties, where raised-cosine radial basis function NNs with compact supports are applied to approximate system uncertainties.

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## composite learning technique

To improve the NNs learning rapidity, the prediction error which reflect the learning performance is constructed, further the updating law is designed utilizing the composite learning technique.^{[1]}The modeling accuracy is theoretically guaranteed by a composite learning technique, it explores both online historical data and instantaneous data to achieve NN weight convergence under a much weaker interval-excitation condition than the stringent persistent-excitation condition.

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## composite learning method

In this paper, we propose a learning approach for interval Fuzzy Logic Systems (FLSs) to end up with models that are capable to cover an expected amount of uncertainty with a high accuracy by exploiting a composite learning method with quantile regression.^{[1]}Moreover, the composite learning method is utilized to estimate unknown nonlinear functions.

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