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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Johansen, T.A. Jackson, W. Schreiber, R. Tondel, P. |
| Copyright Year | 2006 |
| Description | Author affiliation: Dept. of Eng. Cybern., Norwegian Univ. of Sci. & Technol., Trondheim (Johansen, T.A.) |
| Abstract | Solutions to constrained linear and hybrid model predictive control (MPC) can be explicitly characterized in terms of piecewise linear (PWL) state feedback control. A PWL controller is pre-computed using parametric programming, and the exact explicit MPC implementation corresponds to the evaluation of a PWL function in the control unit. It has recently been shown that such evaluation can be boosted by binary search tree data structures. We report digital hardware architecture design results for this type of PWL control, and show that explicit MPC solutions can be implemented in a standard field programmable gate array (FPGA) or an application specific integrated circuit (ASIC) with about 20 kGates leading to computation times around one microsecond per evaluation for typical applications. This facilitates implementation of constrained MPC in small-scale industrial and consumer electronics applications that are characterized by fast sampling or low production cost, such as mechatronics, MEMS, rotating machinery, power electronics, and acoustics |
| Sponsorship | American Automatic Control Council IFAC |
| File Size | 340360 |
| File Format | |
| ISBN | 1424402093 |
| e-ISBN | 1424402107 |
| DOI | 10.1109/ACC.2006.1656501 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-06-14 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | American Automatic Control Council(AACC) |
| Subject Keyword | Hardware Predictive models Predictive control Field programmable gate arrays Application specific integrated circuits Piecewise linear techniques State feedback Linear feedback control systems Functional programming Binary search trees |
| Content Type | Text |
| Resource Type | Article |
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