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Model-based estimation and control on multicore platforms

Model-based estimation and control on multicore platforms. Motivation: Streamlined real-time control and estimation software written for single core runs slower on multicore Battery-driven embedded control systems need multicore processors for longer battery life and reduced heat production

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Model-based estimation and control on multicore platforms

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  1. Model-based estimation and control on multicore platforms Motivation: • Streamlined real-time control and estimation software written for single core runs slower on multicore • Battery-driven embedded control systems need multicore processors for longer battery life and reduced heat production Control and estimation algorithms: • Application design must map to the multicore architecture • Parallel • Cache-aware

  2. Work plan Goals: • Re-design of control and estimation algorithms for linear speedup on multicore platforms • Model processor and memory system demand of algorithms for guaranteed real-time performance • Proof of concept in laboratory real-time setups (control) and data from industrial applications (estimation)

  3. Targeted algorithms Computationally intensive and distributed real-time algorithms: • Estimation • Kalman filter • Particle filter • Control • Model-predictive control • Multivariable control

  4. Results so far • Effective implementation of the Kalman filter on multicore • The KF is modified to give linear speedup • Application to echo cancellation • Memory bandwidth model • Effective implementation of the Particle Filters on multicore • A number of PFs is evaluated with respect to scaling, performance, computational burden • Algorithms with good scaling properties on multicore are found. • Application to bearings-only tracking (SAAB Systems) • Feedforward state estimation algorithms are revisited to clarify design issues • Laboratory setup for real-time estimation and control on multicore • LEGO-based mobile robotic wireless sensor network • Multicore central node • Both control (of mobile robots) and estimation

  5. Future and ongoing research • Estimation • MIMO Kalman filtering (sensor fusion) • Anomaly detection (SAAB Systems) • Change detection by Kalman filter • Change detection by Particle filter • New applications • Road grade estimation (Scania) • Control • Parallelization of model-predictive control (parallel optimization)

  6. Bearings-only tracking

  7. Speedup Particle filters

  8. Speedup Kalman filter Grad Kalkyl

  9. Anomaly detection Vid röda punkten 43 försvann Arctic Sea från AIS-systemet. Då var klockan 04.20 onsdagen den 24 juli 2009. En och en halv timme senare dök det upp igen vid den gröna punkten 44. Sedan drev fartyget långsamt norröver i nästan två timmar innan det fick upp farten och vände söderut igen. Karta: Sjöfartsverket.

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