scholarly journals Optimal State Control of Fractional Order Differential Systems: The Infinite State Approach

2021 ◽  
Vol 5 (2) ◽  
pp. 29
Author(s):  
Jean-Claude Trigeassou ◽  
Nezha Maamri

Optimal control of fractional order systems is a long established domain of fractional calculus. Nevertheless, it relies on equations expressed in terms of pseudo-state variables which raise fundamental questions. So in order remedy these problems, the authors propose in this paper a new and original approach to fractional optimal control based on a frequency distributed representation of fractional differential equations called the infinite state approach, associated with an original formulation of fractional energy, which is intended to really control the internal system state. In the first step, the fractional calculus of variations is revisited to express appropriate Euler Lagrange equations. Then, the quadratic optimal control of fractional linear systems is formulated. Thanks to a frequency discretization technique, the previous theoretical equations are converted into an equivalent large dimension integer order system which permits the implementation of a feasible optimal solution. A numerical example illustrates the validity of this new approach.

Author(s):  
Sina Dehghan ◽  
Tiebiao Zhao ◽  
YangQuan Chen ◽  
Taymaz Homayouni

Abstract RIOTS is a Matlab toolbox capable of solving a very general form of integer order optimal control problems. In this paper, we present an approach for implementing Model Predictive Control (MPC) to control a general form of fractional order systems using RIOTS toolbox. This approach is based on time-response-invariant approximation of fractional order system with an integer order model to be used as the internal model in MPC. The implementation of this approach is demonstrated to control a coupled MIMO commensurate fractional order model. Moreover, the performance and its application process is compared to examples reported in the literature.


Author(s):  
Jorn H. Baayen ◽  
Krzysztof Postek

AbstractNon-convex discrete-time optimal control problems in, e.g., water or power systems, typically involve a large number of variables related through nonlinear equality constraints. The ideal goal is to find a globally optimal solution, and numerical experience indicates that algorithms aiming for Karush–Kuhn–Tucker points often find solutions that are indistinguishable from global optima. In our paper, we provide a theoretical underpinning for this phenomenon, showing that on a broad class of problems the objective can be shown to be an invariant convex function (invex function) of the control decision variables when state variables are eliminated using implicit function theory. In this way, optimality guarantees can be obtained, the exact nature of which depends on the position of the solution within the feasible set. In a numerical example, we show how high-quality solutions are obtained with local search for a river control problem where invexity holds.


2017 ◽  
Vol 40 (13) ◽  
pp. 3872-3883 ◽  
Author(s):  
Mohammad-Reza Rahmani ◽  
Mohammad Farrokhi

This paper presents a neuro-fractional-order Hammerstein model with a systematic identification algorithm for identifying unknown nonlinear dynamic systems. The proposed model consists of a Radial Basis Function Neural Network (RBF NN) followed by a Fractional-Order System (FOS). The proposed identification scheme is performed in two stages. First, the fractional-order and the number of state variables (or degree) of the state-space realization of the FOS are estimated in the frequency domain. Then, the parameters of the RBF NN (the weights, centers and widths of the Gaussian functions) and the state matrix of the FOS are determined using the time domain data via the Lyapunov stability theory. Simulating as well as experimental examples are provided to verify the effectiveness of the proposed method. The identification results show that the proposed neuro-fractional-order Hammerstein modeling is superior as compared with the existing Hammerstein modeling in literature.


Author(s):  
Phu Tran Tin ◽  
Le Anh Vu ◽  
Minh Tran ◽  
Nguyen Quang Dung ◽  
Tran Thanh Trang

<p>In this paper, we propose the method of tuning a conventional PID controller for unstable transient characteristics. The results show that: 1) This is the novel practical method based on the desired settling time and overshoot percentage; 2) The results are close to the desired parameters; 3) The novel method can tune an unstable fractional order system by real interpolation method (RIM); 4) The novel method is simplicity and computer efficiency; 5) The novel method can find an optimal solution for tuning task in both academic and industrial purposes.</p>


2011 ◽  
Vol 474-476 ◽  
pp. 108-113
Author(s):  
Xin Gao

In this paper, we numerically investigate the chaotic behaviors of a fractional-order system. We find that chaotic behaviors exist in the fractional-order system with an order being less than 3. The lowest order we find to have chaos is 2.4 in such system. In addition, we numerically simulate the continuances of the chaotic behaviors in the fractional-order system with orders ranging from 2.7 to 3. Finally, a simple, but effective, linear state feedback controller is proposed for controlling the fractional-order chaotic system based on an inverse optimal control approach. Numerical simulations show the effectiveness and feasibility of the proposed controller.


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