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Model-based estimation and control on multicore platforms

This research focuses on redesigning control and estimation algorithms for multicore processors to improve real-time performance, particularly in battery-driven embedded systems. Key algorithms like Kalman and Particle filters have been adapted to ensure linear speedup while being efficient in memory usage. Laboratory setups demonstrate effective implementations, including applications in mobile robotics and industrial tracking. Ongoing investigations aim to develop new applications like MIMO Kalman filtering and anomaly detection, ensuring robust performance in diverse environments.

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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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