Bifurcated Nonlinear Plasma Control With Frame-Based Adaptation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current adaptive controllers for plasma processing systems lack inherent stability, struggle with nonlinearities and uncertainties, and are limited in adaptability to various situations, leading to inefficiencies and potential system failures.
Innovation Solution
The development of an adaptive engine with a bifurcated nonlinear model, which uses a time-varying linear system to approximate nonlinear behavior and adapt model parameters in real-time, allowing for faster and more robust control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing adaptive controllers are used, then control is provided to actuators, but the controllers lack inherent stability and struggle with nonlinearities and uncertainties
Solution Approach 1:
The adaptive controller is segmented into multiple independent sub-engines, each implementing a different control law. This modular architecture allows each sub-engine to specialize in handling specific types of nonlinearities or uncertainties while maintaining overall system stability through the structured combination of individual controller outputs.
Solution Approach 2:
The controller is designed as a universal adaptive system that can handle multiple types of nonlinearities and uncertainties through its plurality of sub-engines. Each sub-engine can be configured with different control laws to address various operating conditions, making the overall controller versatile across different plasma processing scenarios while maintaining stability through the unified selection mechanism.
2Adaptability or versatility
If existing adaptive controllers are used, then control is provided to actuators, but they are limited in adaptability to various situations
Solution Approach 1:
The controller architecture is segmented into standardized sub-engines with uniform interfaces, allowing each to handle specific control scenarios independently. This segmentation enables easy addition or removal of sub-engines based on operational requirements without redesigning the entire system, thus improving adaptability while managing complexity through modular organization.
Solution Approach 2:
The controller dynamically selects and combines outputs from multiple sub-engines based on real-time system conditions. This dynamic adaptation allows the controller to respond flexibly to various situations by activating only the necessary sub-engines, thereby improving versatility while keeping the effective complexity low through selective operation rather than simultaneous management of all components.
3Speed
If rail voltage is held at high level for much of pulse cycle, then power amplifier can provide desired pulsed waveform, but this leads to overheating of components and premature system failure
Solution Approach 1:
The adaptive controller predicts future system states and adjusts the rail voltage in advance to achieve the desired pulsed waveform without maintaining high voltage throughout the entire pulse cycle. By performing preliminary control actions based on predicted requirements, the system can rapidly adjust voltage only when necessary, ensuring fast response while minimizing thermal stress on components and improving reliability.
Solution Approach 2:
The controller dynamically adjusts the rail voltage level based on real-time plasma conditions and waveform requirements, rather than maintaining a static high voltage level. This dynamic control allows the power amplifier to respond quickly to changing conditions while keeping the rail voltage at the minimum necessary level at each moment, thereby reducing heat generation and improving system reliability without sacrificing response speed.
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.