Adaptive Dynamic Output Feedback Neural Network Control of Uncertain MIMO Nonlinear Systems With Prescribed Performance

Abstract

An adaptive dynamic output feedback neural network controller for a class of multi-input/multi-output affine in the control uncertain nonlinear systems is designed, capable of guaranteeing prescribed performance bounds on the system's output as well as boundedness of all other closed loop signals. It is proved that simply guaranteeing a boundedness property for the states of a specifically defined augmented closed loop system is necessary and sufficient to solve the problem under consideration. The proposed dynamic controller is of switching type. However, its continuity is guaranteed, thus alleviating any issues related to the existence and uniqueness of solutions. Simulations on a planar two-link articulated manipulator illustrate the approach.

Sliding-Mode Control Design for Nonlinear Systems Using Probability Density Function Shaping

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Fuzzy Adaptive Switching Control for an Uncertain Robot Manipulators with Time‐Varying Output Constraint

“…A new idea of finite-time quantized feedback control with NNs is addressed for quantized nonlinear systems with unknown states in [19]. Tracking controllers with prescribed performance are constructed for dynamical nonlinear systems in [20][21][22][23]. A variety of different forms of control technologies are designed for robot manipulators with unknown dynamics based on NNs or FLSs, like output feedback control [24][25][26][27], robust position control [28], decentralized control [29,30], and so on.…”

Echo State Neural Network Based State Feedback Control for SISO Afine Nonlinear Systems

“…When only the system output can be measured, the observer-based adaptive neural network control were proposed for a certain class of unknown nonlinear systems for example Sridhar and Khalil (2000), Chien et al (2011), Castaneda and Esquivel (2012), and Chemachema (2012). In the case of multiple input-multiple output nonlinear systems, many adaptive output feedback control schemes were proposed as in Chen et al (2010), Li et al (2011) and Kostarigka and Rovithakis (2012).…”