Bifurcated Nonlinear Adaptive Control for Plasma Stability
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Solution Overview
Problem
Existing adaptive controllers struggle with stability, scalability, and adaptability in controlling plasma processing systems due to asynchronous actuator responses, unbounded control values, modeling uncertainties, and input/output disturbances, leading to inefficiencies and premature system failures.
Innovation Solution
An adaptive engine with a bifurcated nonlinear model that includes a control portion and an estimation portion, utilizing a time-varying linear system to approximate nonlinear behavior, with estimated model parameter tensors updated in real-time to manage large and small nonlinearities, ensuring robust control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing adaptive controllers are used to control plasma processing systems, then basic control functionality is provided, but stability and reliability deteriorate due to asynchronous actuator responses and unbounded control values
Solution Approach 1:
The controller employs dynamic adaptation mechanisms that adjust control parameters in real-time based on system state. The adaptive engine modifies control laws dynamically to handle asynchronous actuator responses, ensuring stability while maintaining adaptability to varying system conditions.
Solution Approach 2:
The controller implements feedback mechanisms that monitor actuator responses and system state, using this information to adjust control outputs. This feedback loop bounds control values and prevents instability caused by asynchronous actuator behavior, improving reliability while preserving adaptability.
2Manufacturing precision
If existing adaptive controllers are used, then control functionality is provided, but precision and consistency deteriorate due to modeling uncertainties and input/output disturbances
Solution Approach 1:
The controller performs preliminary estimation of system state and disturbances before applying control actions. By predicting system behavior and potential disturbances in advance, the controller compensates for modeling uncertainties, improving precision without requiring excessively complex models.
Solution Approach 2:
The controller adjusts control parameters dynamically based on estimated system state and disturbances. This parameter adaptation allows the controller to maintain precision under varying conditions while avoiding the need for overly complex fixed models, balancing precision and model complexity.
3Power
If rail voltage is held at high level for most of pulse cycle, then power delivery is ensured, but energy efficiency deteriorates and overheating occurs
Solution Approach 1:
The controller applies periodic pulsed voltage rather than continuous high voltage. By delivering power in controlled pulses synchronized with the plasma process requirements, the system maintains necessary power delivery while reducing average energy consumption and preventing overheating through periodic rest periods.
Solution Approach 2:
The controller anticipates power requirements and applies voltage only when needed, preventing unnecessary energy dissipation. By timing power delivery to match actual plasma process demands, the system avoids the harmful effect of continuous high voltage that causes overheating and energy waste.
Data Source
AI summary
This disclosure describes systems, methods, and apparatus for an adaptive engine with a bifurcated nonlinear model. The adaptive controller uses a nonlinear model having a control portion and an estimation portion, wherein the estimation portion uses a time-varying linear system to approximate nonlinear behavior of the system. Further, the time-varying linear system receives a structure of the underlying matrices for every frame of control samples allowing the time-varying linear system to model large nonlinearities and to pre-process this linear approximation for each frame. At the same time, the time-varying linear system also uses estimated model parameter tensors in the underlying matrices that are updated or adapted every control cycle, in real-time, throughout a frame, such that the linear approximation is also able to approximate small nonlinearities in the system. This bifurcation of a linearized model provides a faster and more robust adaptive controller.