In the neural network adaptation, the Newton method could find a minimum with its second-
order partial derivatives, and convergent gradient steepest descent could assure its error …

Robust and precise motion tracking for micro-electro-mechanical systems in the presence of
inherent nonlinearity and external disturbance is of great importance in many applications …

We develop a new robust control scheme for a non-holonomic spherical robot. To this end,
the mathematical model of a pendulum driven non-holonomic spherical robot is first …

This paper proposes a new approach to address robust control design for nonlinear
continuous-time systems with unmatched uncertainties. First, we transform the robust control …

This paper addresses global stabilization of fractional-order memristor-based neural
networks (FMNNs) with incommensurate orders and multiple time-varying delays (MTDs) …

Although robust control has been studied for decades, the output-feedback robust control
design is still challenging in the control field. This article proposes a new approach to …

This paper proposes a new constructive identification and adaptive control method for
nonlinear pure-feedback systems, which remedies the'explosion of complexity'and potential …

The piezo-driven nanopositioning stage (PNS) is a key device to provide fast and precise
motions for applications such as micromanipulation, microfabrication, and microscopy …

This paper focuses on the optimal tracking control problem (OTCP) for the unknown multi-
input system by using a reinforcement learning (RL) scheme and nonzero-sum (NZS) game …

In this article, we propose a novel finite-memory-structured online training algorithm (FiMos-
TA) for neural networks to identify and predict the unknown functions and states of an …