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- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 45495 (05)
- University: Sharif University of Technology
- Department: Electrical Engineering
- Advisor(s): Haeri, Mohammad
- Abstract:
- Robust model predictive control(RMPC) is a control strategy that has been widely adopted in industry and academic researches.In this project we have modified robust nonlinear model predictive control using SOS and dynamic feedback. In this control strategy we will linearize the nonlinear model of the system about it’s operating point. We will consider the error of linearization as an uncertainty and find an upper bound for uncertainty. Then we will change control objective to minimizing this upper bound. The most advantage of changing the control objective is that nonlinear system results a non-convex optimization problem but this strategy terminates a convex optimization problem. To find upper bound of the uncertainty a widespread way is to use linear quadratic lyapounov function. Our objective in this project is to find a convenient matrix to change system’s variable such that decrease computation time of the optimization problem and solving the linear matrix inequalities(LMIs).We know that if a system have a large region of convergence it will tolerate larger disturbances and this an important property for a system. So we will use sum of square approximation algorithm to enlarge the region of convergence of the system. The simulation results verify the performance of control strategy on nonlinear systems
- Keywords:
- Sum of Squares (SOS) ; Computational Complexity ; Constrained Optimization ; Linear Matrix Inequality (LMI) ; Model Predictive Control ; Dynamic State Feedback
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