Robust Predictive Control for Discrete-Time Uncertain Lur'e Systems
A large number of control systems in practice can be modeled as a Lur'e system, for example, flexible robotic arms or nonlinear systems with common sector-bounded nonlinealities such as quantization, deadzone and so on. In most cases, the nonlinearities are uncertain and possibly varying over time. There are many approaches to control Lur'e systems; however, most of them cannot take into account the constraints of states and inputs. On the other hand, MPC can handle the constraints efficiently but finding a global solution for a nonlinear optimization problem is often demanding, not to mention the uncertainty.
Alternatively, one can be formulated as a robust MPC problem and recast it in the form of a Linear Matrix Inequality (LMI), which can be solved by currently available softwares.
The main tasks of this thesis are:
- Formulate the problems in form of LMIs for discrete-time Lur'e systems
- Show that the proposed method is feasible and can guarantee the stability of the system
- Do simulations to illustrate the method.
Control Systems Theory, MPC, Nonlinear Systems, LMI.
Background knowledge: Optimal Control, Nonlinear Control and Linear Algebra.
Estimated time requirements:
Hoang Hai Nguyen