Model reference control of nonlinear plants with uncertain and unbounded terms

A learning scheme for multilayer feedforward neural networks used as direct adaptive controllers of nonlinear plants is suggested. This scheme is a supervised steepest descent one that does not require backpropagation of the error. Using a neural network controller trained with this method does not require the identification stage and this makes it superior to the other methodologies. Methods for using neural networks in plant control suggested in the literature are discussed and compared with the proposed system. The structure of the network and the training method used are explained. Simulations based on model reference control of some nonlinear plants show satisfactory performance.

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Model Reference Control With Command Shaping for a Micro-Electromagnetic Actuator With Input Constraints

Volume 3, Rapid Fire Interactive Presentations: Advances in Control Systems; Advances in Robotics and Mechatronics; Automotive and Transportation Systems; Motion Planning and Trajectory Tracking; Soft Mechatronic Actuators and Sensors; Unmanned Ground and Aerial Vehicles ◽

10.1115/dscc2019-9162 ◽

2019 ◽

Author(s):

Gerald Eaglin ◽

Joshua Vaughan

Keyword(s):

Reference Model ◽

Electromagnetic Actuator ◽

Input Shaping ◽

Input Constraints ◽

Residual Vibration ◽

Model Reference ◽

Command Shaping ◽

Model Reference Control ◽

Limiting Property ◽

Nonlinear Plants

Abstract Model Reference Control is used to force a system to track the response of an assigned reference model, where the reference model is often designed to reflect the desired properties of the system. If a linear reference model is used, Model Reference Control has a linearizing effect for nonlinear plants, allowing it to be cascaded with linear controllers. Model Reference Control has been used to force nonlinear flexible systems to behave linearly such that input shaping can be used to limit residual vibration. However, when a system encounters saturation limits, the vibration limiting property of input shaping is degraded. This paper proposes Model Reference Control with an adaptive input shaping method to account for saturation by modifying the input shaper after saturation has been encountered. Simulations are presented to illustrate the effectiveness of this method in canceling residual vibration for a nonlinear electromagnetic actuator subject to input constraints.

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Model Reference Control using Time Delay for Nonlinear Plants with Unknown Dynamics

IFAC Proceedings Volumes ◽

10.1016/s1474-6670(17)55111-3 ◽

1987 ◽

Vol 20 (5) ◽

pp. 349-356

Author(s):

K. Youcef-Toumi ◽

O. Ito

Keyword(s):

Time Delay ◽

Model Reference ◽

Model Reference Control ◽

Nonlinear Plants

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Application of Model Reference Control with Time Delay to the Space Shuttle Main Engine

1991 American Control Conference ◽

10.23919/acc.1991.4791679 ◽

1991 ◽

Author(s):

K. Youcef-Toumi ◽

D. Beck

Keyword(s):

Time Delay ◽

Space Shuttle ◽

Model Reference ◽

Model Reference Control ◽

Main Engine

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Optimal Model Reference Control Scheme Design for Nonlinear Strict-Feedback Systems

Engineering and Technology Journal ◽

10.30684/etj.v38i9a.1339 ◽

2020 ◽

Vol 38 (9A) ◽

pp. 1342-1351

Author(s):

Musadaq A. Hadi ◽

Hazem I. Ali

Keyword(s):

Reference Model ◽

Feedback System ◽

Optimization Method ◽

Feedback Systems ◽

Model Reference ◽

Scheme Design ◽

Model Reference Control ◽

Control Scheme ◽

Lyapunov Stability Analysis ◽

Strict Feedback

In this paper, a new design of the model reference control scheme is proposed in a class of nonlinear strict-feedback system. First, the system is analyzed using Lyapunov stability analysis. Next, a model reference is used to improve system performance. Then, the Integral Square Error (ISE) is considered as a cost function to drive the error between the reference model and the system to zero. After that, a powerful metaheuristic optimization method is used to optimize the parameters of the proposed controller. Finally, the results show that the proposed controller can effectively compensate for the strictly-feedback nonlinear system with more desirable performance.

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