Robust integral of neural network and sign of tracking error control of uncertain nonlinear affine systems using state and output feedback

2011 ◽  
Author(s):  
Qinmin Yang ◽  
S. Jagannathan ◽  
Youxian Sun
Keyword(s):  
Neural Network ◽  
Output Feedback ◽  
Error Control ◽  
Tracking Error ◽  
Affine Systems ◽  
Tracking Error Control

scholarly journals A tracking error–based fully probabilistic control for stochastic discrete-time systems with multiplicative noise

2020 ◽  
Vol 26 (23-24) ◽  
pp. 2329-2339
Author(s):  
Randa Herzallah ◽  
Yuyang Zhou
Keyword(s):  
Error Control ◽  
Multiplicative Noise ◽  
Stochastic Systems ◽  
Design Method ◽  
Tracking Error ◽  
Flexible Beam ◽  
System Control ◽  
Probabilistic Design ◽  
Leibler Divergence ◽  
Tracking Error Control

This article proposes the exploitation of the Kullback–Leibler divergence to characterise the uncertainty of the tracking error for general stochastic systems without constraints of certain distributions. The general solution to the fully probabilistic design of the tracking error control problem is first stated. Further development then focuses on the derivation of a randomised controller for a class of linear stochastic Gaussian systems that are affected by multiplicative noise. The derived control solution takes the multiplicative noise of the controlled system into consideration in the derivation of the randomised controller. The proposed fully probabilistic design of the tracking error of the system dynamics is a more legitimate approach than the conventional fully probabilistic design method. It directly characterises the main objective of system control. The efficiency of the proposed method is then demonstrated on a flexible beam example where the vibration quenching in flexible beams is shown to be effectively suppressed.

scholarly journals MASALAH GALAT PENJEJAKAN MINIMUM PADA SISTEM PENDULUM TERBALIK

2010 ◽  
Vol 9 (1) ◽  
pp. 15
Author(s):  
T. BAKHTIAR
Keyword(s):  
Error Control ◽  
Inverted Pendulum ◽  
Tracking Error ◽  
Closed Form Expression ◽  
Form Expression ◽  
Minimum Error ◽  
Optimal Tracking ◽  
Inverted Pendulum Model ◽  
Pendulum Model ◽  
Tracking Error Control

This paper studies the optimal tracking error control problem on an inverted pendulum model. We characterize the optimal tracking error in term of pendulum’s parameters. Particularly, we derive the closed form expression for the pendulum length which gives minimum error. It is shown that the minimum error can always be accomplished as long as the ratio between the mass of the pendulum and that of the cart satisfies a certain constancy, regardless the type of material we use for the pendulum.

Multi-dimensional Taylor network modelling and optimal control of SISO nonlinear systems for tracking by output feedback

2019 ◽  
Vol 37 (3) ◽  
pp. 699-717 ◽  
Author(s):  
Qi-Ming Sun ◽  
Hong-Sen Yan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Nonlinear Systems ◽  
Real Time ◽  
Output Feedback ◽  
Tracking Error ◽  
Real Time Control ◽  
Network Modelling ◽  
Single Input Single Output ◽  
Time Control

Abstract In this paper, a multi-dimensional Taylor network (MTN) output feedback tracking control of nonlinear single-input single-output (SISO) systems in discrete-time form is studied. To date, neural networks are generally used to identify unknown nonlinear systems. However, the neuron of neural networks includes the exponential function, which contributes to the complexity of calculation, making the neural network control unable to meet the real-time requirements. In order to identify the controlled object whose model is unknown, the MTN, which requires only addition and multiplication, is utilized for successful real-time control of the SISO nonlinear system based on only its output feedback. Lyapunov analysis proves that output signals in the closed-loop system remain bounded and the tracking error converges to an arbitrarily small neighbourhood around the origin. In contrast to the back propagation (BP) neural network self-adaption reconstitution controller, the edge of the scheme is that the MTN optimal controller promises desirable response speed, robustness and real-time control.

Minimum Tracking Error Control of Flexible Ball Screw Drives Using a Discrete-Time Sliding Mode Controller

2009 ◽  
Vol 131 (5) ◽  
Author(s):  
Chinedum Okwudire ◽  
Yusuf Altintas
Keyword(s):  
Discrete Time ◽  
Error Control ◽  
Sliding Mode ◽  
Tracking Error ◽  
Open Loop ◽  
Ball Screw ◽  
Sliding Mode Controller ◽  
Tracking Accuracy ◽  
Ball Screw Drives ◽  
Tracking Error Control

This paper presents modeling, identification, and discrete-time sliding mode control of ball screw drives with structural flexibility. The mechanical system of the drive is modeled by a two degree-of-freedom system dominated by the coupled longitudinal and torsional dynamics of the drive assembly whose parameters are identified. A mode-compensating disturbance adaptive discrete-time sliding mode controller is then designed to actively suppress the vibrations of the drive. However, it is shown theoretically that, without using minimum tracking error filters, the tracking errors of the drive do not go to zero when sliding mode is reached. Therefore, a method for designing stable and robust minimum tracking error filters, irrespective of the identified open-loop behavior of the drive is proposed. The identification and control of flexible ball screw drives are experimentally tested, and the tracking accuracy of the drives is shown to improve considerably as a result of the designed minimum tracking error filters.

scholarly journals Multidimensional Taylor Network Optimal Control of MIMO Nonlinear Systems without Models for Tracking by Output Feedback

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Qi-Ming Sun ◽  
Hong-Sen Yan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Nonlinear Systems ◽  
Real Time ◽  
Output Feedback ◽  
Tracking Error ◽  
Network Control ◽  
Real Time Control ◽  
Time Dynamic ◽  
Mimo Nonlinear Systems

The actual controlled objects are generally multi-input and multioutput (MIMO) nonlinear systems with imprecise models or even without models, so it is one of the hot topics in the control theory. Due to the complex internal structure, the general control methods without models tend to be based on neural networks. However, the neuron of neural networks includes the exponential function, which contributes to the complexity of calculation, making the neural network control unable to meet the real-time requirements. The newly developed multidimensional Taylor network (MTN) requires only addition and multiplication, so it is easy to realize real-time control. In the present study, the MTN approach is extended to MIMO nonlinear systems without models to realize adaptive output feedback control. The MTN controller is proposed to guarantee the stability of the closed-loop system. Our experimental results show that the output signals of the system are bounded and the tracking error goes nearly to zero. The MTN optimal controller is proven to promise far better real-time dynamic performance and robustness than the BP neural network self-adaption reconstitution controller.

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