Non-linear modelling and control of a conveyor-belt grain dryer utilizing neuro-fuzzy systems

One characteristic of neuro-fuzzy systems is the possibility of incorporating preliminary information in their structure as well as being able to establish an initial configuration to carry out the training. In this regard, the strategy to establish the configuration of the fuzzy system is a relevant aspect. This document displays the design and implementation of a neuro-fuzzy controller based on Boolean relations to regulate the angular position in an electromechanical plant, composed by a motor coupled to inertia with friction (a widely studied plant that serves to show the control system design process). The structure of fuzzy systems based on Boolean relations considers the operation of sensors and actuators present in the control system. In this way, the initial configuration of fuzzy controller can be determined. In order to perform the optimization of the neuro-fuzzy controller, the continuous plant model is converted to discrete time to be included in the closed-loop controller training equations. For the design process, first the optimization of a Proportional Integral (PI) linear controller is carried out. Thus, linear controller parameters are employed to establish the structure and initial configuration of the neuro-fuzzy controller. The optimization process also includes weighting factors for error and control action in such a way that allows having different system responses. Considering the structure of the control system, the optimization algorithm (training algorithm) employed is dynamic back propagation. The results via simulations show that optimization is achieved in the linear and neuro-fuzzy controllers using different weighting values for the error signal and control action. It is also observed that the proposed control strategy allows disturbance rejection.

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Non Linear Modelling and Control of Hydraulic Actuators

Acta Polytechnica ◽

10.14311/354 ◽

2002 ◽

Vol 42 (3) ◽

Author(s):

B. Šulc ◽

J. A. Jan

Keyword(s):

Linear Models ◽

Hydraulic Actuator ◽

Friction Forces ◽

Linear Modelling ◽

Non Linear ◽

Object Oriented Approach ◽

Physical Laws ◽

And Control ◽

Nonlinear State ◽

Very High

This paper deals with non-linear modelling and control of a differential hydraulic actuator. The nonlinear state space equations are derived from basic physical laws. They are more powerful than the transfer function in the case of linear models, and they allow the application of an object oriented approach in simulation programs. The effects of all friction forces (static, Coulomb and viscous) have been modelled, and many phenomena that are usually neglected are taken into account, e.g., the static term of friction, the leakage between the two chambers and external space. Proportional Differential (PD) and Fuzzy Logic Controllers (FLC) have been applied in order to make a comparison by means of simulation. Simulation is performed using Matlab/Simulink, and some of the results are compared graphically. FLC is tuned in a such way that it produces a constant control signal close to its maximum (or minimum), where possible. In the case of PD control the occurrence of peaks cannot be avoided. These peaks produce a very high velocity that oversteps the allowed values.

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Neuro-Fuzzy Active Control of Mechanical Structures

Engineering Technology Conference on Energy, Parts A and B ◽

10.1115/etce2002/struc-29048 ◽

2002 ◽

Author(s):

Pierre-Yves Couzon ◽

Johan Der Hagopian ◽

Luc Gaudiller

Keyword(s):

Dynamic Behavior ◽

Active Control ◽

Degrees Of Freedom ◽

Automatic Regulation ◽

Electromagnetic Actuators ◽

Fuzzy Function ◽

Neuro Fuzzy ◽

Non Linear ◽

Non Linear System ◽

And Control

The abilities of neural networks combined with fuzzy logic offer interesting prospects for the active control of structures. By identification, they permit discarding the often delicate modeling step and they also permit the automatic regulation of the controllers that have non-linear characteristics. This study describes the application of neuro-fuzzy control to the dynamic behavior of structures. The study first explains the process chosen, which consists of two parts: • the first part is essential for the adjustment of the associated controller and concerns the neural identification of the structure studied; • the second part describes the controller development and the training stage; the controller is based on the simplest neural network model possible. This network is also able to translate Sugeno’s fuzzy function and optimize its performances according to a reference response. The study then presents two applications: the first deals with the identification and control of a linear mechanical system with two degrees of freedom. The second deals with the identification and control of the non-linear dynamic behavior of active electromagnetic actuators along one acting axis. In both cases, the results show the abilities and the efficiency of this process and underline the main advantage of this type of controller operating even on in a strongly non-linear system.

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