Observer‐based adaptive neural control of uncertain MIMO nonlinear systems with unknown control direction

Abstract

This paper investigates adaptive neural network output feedback control for a class of uncertain multi-input multi-output (MIMO) nonlinear systems with an unknown sign of control gain matrix. Because the system states are not required to be available for measurement, an observer is designed to estimate the system states. In order to deal with the unknown sign of control gain matrix, the Nussbaum-type function is utilized. By using neural network, we approximated the unknown nonlinear functions and perfectly avoided the controller singularity problem. The stability of the closed-loop system is analyzed by using Lyapunov method. Theoretical results are illustrated through a simulation example.

Adaptive output feedback control of uncertain nonlinear systems with input delay and output constraint

“…Consider the close-loop system consisting of system (5) under Assumptions 1 to 4; virtual controls (32), (54), and (67); adaptation laws (34), (35) and (97), control law (96), and the initial conditions satisfy z 1 (0) ∈ Ω z 1 = {z 1 ∈ R| − k b1 < z 1 < k b1 }, then: Consider the close-loop system consisting of system (5) under Assumptions 1 to 4; virtual controls (32), (54), and (67); adaptation laws (34), (35) and (97), control law (96), and the initial conditions satisfy z 1 (0) ∈ Ω z 1 = {z 1 ∈ R| − k b1 < z 1 < k b1 }, then:…”

“…Using dynamic signal to deal with unmodeled dynamics, two adaptive output feedback control schemes were proposed for a class of nonlinear systems with unmodeled dynamics and unmeasured states as well as unknown high-frequency gain. [32][33][34][35] To sum up, although the problems of input delay and output constraint have been discussed in some literature, [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] adaptive control for systems simultaneously with input delay, unknown control gain, unmodeled dynamics, and output constraint has not been fully investigated, and there remains importance and demand for further research. 29 A Nussbaum gain technique was used to handle the unknown high-frequency gain sign for the systems in the work of Nussbaum.…”

Peaking-Free Output-Feedback Adaptive Neural Control Under a Nonseparation Principle

“…Adaptive fuzzy/NN observers have also been further developed to relax the limitations of highgain observers [29]- [35]. To realize output feedback of AAC under an immeasurable vector of estimation errors, the original observation error dynamics should usually be augmented by a linear low-pass filter, which makes the augmented dynamics satisfy a strictly positive real (SPR) condition, such that the Meyer-Kalman-Yakubovich lemma can be applied to control synthesis [23]- [25], [33], [34]. Yet, this technique results in filtering basis functions of approximators, which makes the order of the controller dynamics very large.…”

Learning from adaptive neural network control of an underactuated rigid spacecraft

“…Precise attitude control in the presence of uncertain nature of spacecraft dynamical systems has attracted considerable research interest in the existing literature [25][26][27][28][29][30][31]. Although the existing literature addresses important issues related to attitude control of spacecraft, such as adapting the control system to modeling uncertainties, only limited results explicitly deal with underactuated systems [32].…”