Event Triggered Neuroadaptive Controller (ETNAC) Design for Uncertain Affine Nonlinear Systems

2018 ◽  
Author(s):  
A. Ghafoor ◽  
J. Yao ◽  
S. N. Balakrishnan ◽  
J. Sarangapani ◽  
T. Yucelen
Keyword(s):  
Nonlinear Systems ◽  
Controller Design ◽  
Tracking Error ◽  
Adaptive Controller ◽  
Fast Estimation ◽  
System A ◽  
High Frequency Oscillations ◽  
Feedback Network ◽  
Affine Nonlinear Systems ◽  
Event Triggered

In this paper, a novel event triggered neural network (NN) adaptive controller is presented for uncertain affine nonlinear systems. Controller design is based on an observer, called as Modified State Observer (MSO), which is used to approximate uncertainties online. State is sensed continuously yet sent on feedback network only when required, in aperiodic fashion. Lyapunov analysis is used to derive this condition which is dynamic in nature since it is based on tracking error. In this way ETNAC helps to not only saves communication cost but also computational efforts. MSO formulations have two tunable gains which let you do fast estimation without inducing high frequency oscillations in the system. A benchmark example of 2-link robotic manipulator is used to show the efficacy of the proposed controller.

scholarly journals Direct Adaptive Tracking Control for a Class of Pure-Feedback Stochastic Nonlinear Systems Based on Fuzzy-Approximation

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Huanqing Wang ◽  
Xiaoping Liu ◽  
Qi Zhou ◽  
Hamid Reza Karimi
Keyword(s):  
Nonlinear Systems ◽  
Tracking Control ◽  
Controller Design ◽  
Tracking Error ◽  
Small Neighborhood ◽  
Adaptive Controller ◽  
Stochastic Nonlinear Systems ◽  
Fuzzy Logic Systems ◽  
Adaptive Tracking Control ◽  
Adaptive Tracking

The problem of fuzzy-based direct adaptive tracking control is considered for a class of pure-feedback stochastic nonlinear systems. During the controller design, fuzzy logic systems are used to approximate the packaged unknown nonlinearities, and then a novel direct adaptive controller is constructed via backstepping technique. It is shown that the proposed controller guarantees that all the signals in the closed-loop system are bounded in probability and the tracking error eventually converges to a small neighborhood around the origin in the sense of mean quartic value. The main advantages lie in that the proposed controller structure is simpler and only one adaptive parameter needs to be updated online. Simulation results are used to illustrate the effectiveness of the proposed approach.

Observer-Based State Tracking Adaptive Controller for a Class of Non-Affine Nonlinear Systems: An Intelligent Approach

2012 ◽  
Vol 488-489 ◽  
pp. 1798-1802
Author(s):  
R. Ghasemi ◽  
M.B. Menhaj ◽  
B. Abdi
Keyword(s):  
Nonlinear System ◽  
Nonlinear Systems ◽  
Tracking Error ◽  
Adaptive Controller ◽  
Nonlinear Observer ◽  
Asymptotic Convergence ◽  
Affine Nonlinear Systems ◽  
State Tracking ◽  
Simulation Results ◽  
Intelligent Approach

This paper proposes a new method for designing both nonlinear observer and adaptive controller for a class of non-affine nonlinear systems with unknown functions of the system. The states of the nonlinear system are assumed to be unavailable for measurement. The merits of this paper is as: asymptotic convergence of the observer and tracking error to zero, boundedness of all signals involved, and robustness. The simulation results illustrate the promising performance of the proposed algorithm.

Adaptive Controller Design for a Class of Discrete Nonlinear Systems

2012 ◽  
Vol 182-183 ◽  
pp. 1260-1264
Author(s):  
Xiao Chun Lou
Keyword(s):  
Nonlinear Systems ◽  
Discrete Time ◽  
Controller Design ◽  
Discrete Systems ◽  
Adaptive Controller ◽  
Discrete Time System ◽  
Time System ◽  
New Form ◽  
Discrete Nonlinear Systems ◽  
Nonlinear Discrete Systems

In this paper, we have discussed the adaptive controller problem for a class of nonlinear discrete systems. Firstly, the general nonlinear discrete-time system is transformed into a new form which is more suitable for adaptive controller design. Based on the new model, the observer is proposed to estimate the unavailable states. The adaptive controller is designed to track the desired trajectory.

scholarly journals Neural Networks Approximator Based Robust Adaptive Controller Design of Hypersonic Flight Vehicles Systems Coupled with Stochastic Disturbance and Dynamic Uncertainties

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Guoqiang Zhu ◽  
Lingfang Sun ◽  
Xiuyu Zhang
Keyword(s):  
Controller Design ◽  
Tracking Error ◽  
Adaptive Controller ◽  
Stochastic Disturbance ◽  
Hypersonic Flight ◽  
Flight Vehicles ◽  
Uncertain Dynamics ◽  
Robust Adaptive ◽  
And Performance ◽  
Dynamic Uncertainties

A neural network robust control is proposed for a class of generic hypersonic flight vehicles with uncertain dynamics and stochastic disturbance. Compared with the present schemes of dealing with dynamic uncertainties and stochastic disturbance, the outstanding feature of the proposed scheme is that only one parameter needs to be estimated at each design step, so that the computational burden can be greatly reduced and the designed controller is much simpler. Moreover, by introducing a performance function in controller design, the prespecified transient and performance of tracking error can be guaranteed. It is proved that all signals of closed-loop system are uniformly ultimately bounded. The simulation results are carried out to illustrate effectiveness of the proposed control algorithm.

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