Adaptive Control of Nonlinear TRMS Model by Using Gradient Descent Optimizers

International Conference on Artificial Intelligence and Data Processing (IDAP) -- SEP 28-30, 2018 -- Inonu Univ, Malatya, TURKEYThis study demonstrates an application of direct gradient descent control for adaptively control of a nonlinear stable system models. The approach is based on utilization of gradient descent optimization techniques for the synthesis of control signals to control a specific plant model. In a former work, gradient descent optimizers were designed by considering a first degree instant input-output relation model assumption of the controlled system and this can allow model independent adaptive control of a class of plant models that can approximate to first order stable plant dynamics. The current study is an extension of this scheme for the purpose of nonlinear adaptive control. Here, we consider a higher degree polynomial assumption of instant input-output relations of the controlled system to obtain gradient descent optimizers that can be applied for adaptive control of a class of nonlinear systems. For evaluation of control performance of gradient descent optimizers, it is applied for the control of nonlinear TRMS model and the results are compared with performance of conventional PID control.Inonu Univ, Comp Sci Dept, IEEE Turkey Sect, Anatolian Sc

Computation of limit cycles in nonlinear feedback loops with fractional order plants

The paper deals with an aspect of the analysis of nonlinear feedback control systems with a fractional order transfer function. A review of the classical describing function (DF) method is given and its application to a control system with a fractional order plant is demonstrated. Unlike the DF method the frequency domain approach of Tsypkin is known to give exact results for limit cycles in relay systems and it is shown that this approach extends to systems with fractional order transfer functions. The formulation is done in terms of A loci which are related to and more general than the Tsypkin loci. Programs have been developed in MATLAB to compute the limit cycle frequency and also to show the results graphically. Examples are provided to illustrate the approach for a relay with no dead zone

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09 juin 19161916/06/09 (A17,N5618)

Reference-shaping adaptive control by using gradient descent optimizers - Fig 3

<p>The reference input, outputs of the reference model and the plant: (a) a view of full-time simulation, (b) a close view of initial responses, (c) a close view of final responses.</p

Matlab/Simulink design of the proposed adaptive control system.

<p>Matlab/Simulink design of the proposed adaptive control system.</p

Simulink simulation model for the second scenario.

<p>Simulink simulation model for the second scenario.</p

Electrical components of experimental system.

<p>Electrical components of experimental system.</p

The short-time average square error calculated for the second simulation scenario.

<p>The short-time average square error calculated for the second simulation scenario.</p

Simulink simulation model for the first scenario.

<p>Simulink simulation model for the first scenario.</p