Estimate of the Domain of Attraction for Polynomial Systems Using Bisectional Sum-of-Squares Optimization

Author(s):  
Linhong Lu ◽  
Pingfang Zhu ◽  
Jianping Zeng
2018 ◽  
Vol 41 (7) ◽  
pp. 1993-2004
Author(s):  
Mohsen Rakhshan ◽  
Navid Vafamand ◽  
Mohammad Mehdi Mardani ◽  
Mohammad-Hassan Khooban ◽  
Tomislav Dragičević

This paper proposes a non-iterative state feedback design approach for polynomial systems using polynomial Lyapunov function based on the sum of squares (SOS) decomposition. The polynomial Lyapunov matrix consists of states of the system leading to the non-convex problem. A lower bound on the time derivative of the Lyapunov matrix is considered to turn the non-convex problem into a convex one; and hence, the solutions are computed through semi-definite programming methods in a non-iterative fashion. Furthermore, we show that the proposed approach can be applied to a wide range of practical and industrial systems that their controller design is challenging, such as different chaotic systems, chemical continuous stirred tank reactor, and power permanent magnet synchronous machine. Finally, software-in-the-loop (SiL) real-time simulations are presented to prove the practical application of the proposed approach.


Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1704
Author(s):  
Faiçal Hamidi ◽  
Messaoud Aloui ◽  
Houssem Jerbi ◽  
Mourad Kchaou ◽  
Rabeh Abbassi ◽  
...  

A novel technique for estimating the asymptotic stability region of nonlinear autonomous polynomial systems is established. The key idea consists of examining the optimal Lyapunov function (LF) level set that is fully included in a region satisfying the negative definiteness of its time derivative. The minor bound of the biggest achievable region, denoted as Largest Estimation Domain of Attraction (LEDA), can be calculated through a Generalised Eigenvalue Problem (GEVP) as a quasi-convex Linear Inequality Matrix (LMI) optimising approach. An iterative procedure is developed to attain the optimal volume or attraction region. Furthermore, a Chaotic Particular Swarm Optimisation (CPSO) efficient technique is suggested to compute the LF coefficients. The implementation of the established scheme was performed using the Matlab software environment. The synthesised methodology is evaluated throughout several benchmark examples and assessed with other results of peer technique in the literature.


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