Physics-Based Surrogate Models for Optimal Control of a CO 2  Methanation Reactor

Self-driving features rely upon autonomous control of vehicle kinetics, and this manuscript compares several disparate approaches to control predominant kinetics. Classical control using feedback of state position and velocities, open-loop optimal control, real-time optimal control, double-integrator patching filters with and without gain-tuning, and control law inversion patching filters accompanying velocity control are assessed in Simulink, and their performances are compared. Optimal controls are found via Pontryagin’s method of optimization utilizing three necessary conditions: Hamiltonian minimization, adjoint equations, and terminal transversality of the endpoint Lagrangian. It is found that real-time optimal control and control-law patching filter with velocity control incorporating optimization are the two best methods overall as judged in Monte Carlo analysis by means and standard deviations of position and rate errors and cost.

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Physics-Based Surrogate Models for Optimal Control of a CO 2 Methanation Reactor

Cited by 3 publications

References 4 publications

Challenges and opportunities in carbon capture, utilization and storage: A process systems engineering perspective

Challenges and opportunities in carbon capture, utilization and storage: A process systems engineering perspective

Recent advances in CO2 hydrogenation to value-added products — Current challenges and future directions

Autonomous Vehicle Control Comparison

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