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A D V a N C E D S E M I N a R Robust Model Predictive Control and Its Application
| Content Provider | Semantic Scholar |
|---|---|
| Author | Jalali, A. A. Nadimi, Vahid Ding, Hao |
| Copyright Year | 2007 |
| Abstract | Figure 1: Principle of MPC Model Predictive Control (MPC), also known as receding horizon control, is a model-based control theory based on the process models to predict future behaviors. The main concept of MPC, shown in Figure 1, is to find control actions, aiming at minimization of a performance criterion over a future horizon while subjecting to constraints, by iteratively solving an optimal control problem on line. It is widely adopted in industry as an effective means to deal with large multivariable constrained control problems [4]. However, plant-model mismatch and model uncertainty may decrease the control performance. Therefore, issues arise for guaranteeing closed loop stability, handling model uncertainty, and reducing on-line computations. Robust predictive controller (RMPC) chooses an optimal control action as MPC but considering the process and model uncertainties, which minimizes the worst disturbance effect to the process behavior [3][5]. An overview of RMPC has been presented in [1] and [2]. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.lsr.ei.tum.de/team/ding/RMPC.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
