OGCVL integrates symbolic and numerical techniques to learn effective nonlinear controlled variables for scalable self-optimizing control in chemical processes.
CasADi: a software framework for nonlinear optimization and optimal control
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OLAhGP optimizes receding-horizon waypoints using the Gaussian process posterior over multiple steps to produce more informative paths for environmental monitoring than prior methods.
A vectorized reformulation of global self-optimizing control makes structural causality constraints linear for batch processes and enables a shortcut method that yields simple, repetitive combination matrices for near-optimal control, shown on a fed-batch reactor.
An iterative GP-based NMPC learning scheme for batch processes achieves 83% tracking error reduction after 4 iterations and 17-fold product mass increase by iteration 8 in simulations, matching full-model NMPC performance.
An open-sourced Unified Autonomy Stack fuses LiDAR, radar, vision and inertial data with sampling-based planning and control barrier functions to deliver resilient autonomy on aerial and ground robots in challenging real-world settings.
citing papers explorer
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Generalized Global Self-Optimizing Control for Chemical Processes: Part II Objective-Guided Controlled Variable Learning Approach
OGCVL integrates symbolic and numerical techniques to learn effective nonlinear controlled variables for scalable self-optimizing control in chemical processes.
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Multi-Step Gaussian Process Propagation for Adaptive Path Planning
OLAhGP optimizes receding-horizon waypoints using the Gaussian process posterior over multiple steps to produce more informative paths for environmental monitoring than prior methods.
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Global self-optimizing control of batch processes
A vectorized reformulation of global self-optimizing control makes structural causality constraints linear for batch processes and enables a shortcut method that yields simple, repetitive combination matrices for near-optimal control, shown on a fed-batch reactor.
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Iterative Model-Learning Scheme via Gaussian Processes for Nonlinear Model Predictive Control of (Semi-)Batch Processes
An iterative GP-based NMPC learning scheme for batch processes achieves 83% tracking error reduction after 4 iterations and 17-fold product mass increase by iteration 8 in simulations, matching full-model NMPC performance.
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The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy
An open-sourced Unified Autonomy Stack fuses LiDAR, radar, vision and inertial data with sampling-based planning and control barrier functions to deliver resilient autonomy on aerial and ground robots in challenging real-world settings.