Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints

By Yanjun Liu, Jing Li, Shaocheng Tong, Chun Lung Philip Chen. IEEE Transactions on Neural Networks and Learning Systems, 2016

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Event‐triggered adaptive backstepping control for parametric strict‐feedback nonlinear systems (2018, background): “(32) Similarly, the dynamics of the last transmitted state vector x̂i held by the ZOH, during the flow interval becomes ̇̂ xi = 0, for t ∈ [tk, tk+1), which results in α̇i−1 = 0, hence ̇̂ zi = ̇̂ xi − α̇i−1 = 0, t ∈ [tk, tk+1).”
Adaptive Neural Network Tracking Control for Robotic Manipulators With Dead Zone (2019, methodology): “In particular, the fuzzy logic control [1]–[5] and neural network (NN) control methods [6]–[9] have been employed generally to approximate the nonlinear systems.”
Output-Feedback Path-Following Control of Autonomous Underwater Vehicles Based on an Extended State Observer and Projection Neural Networks (2018, background): “As a result, it is desirable to prescribe optimal guidance signals by balancing the guidance law and current state of a vehicle for enforcing velocity constraint satisfaction and control input minimization [46].”