Data-Efficient Static Cost Optimization via Extremum-Seeking Control with Kernel-Based Function Approximation

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Abstract

We present a novel type of sampled-data extremum-seeking control (ESC) aimed at speeding up convergence to the optimum and reducing the number of costly performance measurements in practical applications. The approach uses collected output measurements to construct online an approximation of the system's steady-state performance function using kernel-based function approximation. In regions where this approximation is detected to be sufficiently accurate, the proposed approach utilizes it to determine the search direction and compute a suitable optimizer gain for the update step. In regions where the approximation is not yet accurate, additional data is collected and employed in a ‘standard’ ESC update step, while also using it to refine the approximation of the performance function. By using the approximation of the performance function to determine the search direction and optimizer gain when possible, the number of required performance measurements and parameter update steps can be significantly reduced, e.g., with respectively 75% and 45 % in our simulation study involving a static cost function.
Original languageEnglish
Title of host publication2023 62nd IEEE Conference on Decision and Control, CDC 2023
PublisherInstitute of Electrical and Electronics Engineers
Pages6761-6767
Number of pages7
ISBN (Electronic)979-8-3503-0124-3
DOIs
Publication statusPublished - 19 Jan 2024
Event62nd IEEE Conference on Decision and Control, CDC 2023 - Singapore, Singapore, Singapore, Singapore
Duration: 13 Dec 202315 Dec 2023
Conference number: 62

Conference

Conference62nd IEEE Conference on Decision and Control, CDC 2023
Abbreviated titleCDC 2023
Country/TerritorySingapore
CitySingapore
Period13/12/2315/12/23

Keywords

  • Costs
  • Gain measurement
  • Cost function
  • Control systems
  • Steady-state
  • Digital twins
  • Function approximation

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