Aero-Engine Predictive Control for Multivariable Constraint Handling
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Solution Overview
Problem
Traditional single input/single output control systems are inadequate for modern aero-engines with complex structures, as they fail to effectively manage multiple engine parameters and consider real-time performance and actuator constraints, limiting their control capabilities.
Innovation Solution
A design method for aero-engine on-line optimization and multivariable control based on model prediction, which continuously establishes a small deviation linear model of the engine and uses a multivariable model predictive controller to optimize control outputs while considering actuator constraints and physical limits, ensuring steady-state and transition-state control across the flight envelope.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If model prediction control is used to achieve multivariable effective control, then control performance is improved, but calculation time increases and real-time performance deteriorates
Solution Approach 1:
The patent segments the complex nonlinear model into multiple local linear models through piecewise linearization. Each local model is valid within a specific operating range, allowing the controller to select and use only the relevant local model for current conditions, thereby reducing calculation time while maintaining control performance across the entire operating envelope.
Solution Approach 2:
The patent performs preliminary offline computation to pre-calculate and store optimal control parameters, lookup tables, and model coefficients for different operating conditions. During real-time operation, the controller only needs to retrieve and apply these pre-computed values rather than performing complex calculations, significantly reducing online computation time while maintaining optimal control performance.
2Adaptability or versatility
If more control variables are selected to achieve multivariable control system, then control capability is improved, but system complexity increases
Solution Approach 1:
The patent implements dynamic selection of active control variables based on current engine operating conditions. The system automatically activates or deactivates specific control channels depending on which are most relevant to the current flight regime and engine state, allowing the system to adapt its complexity to match the actual control needs rather than maintaining fixed high complexity across all conditions.
Solution Approach 2:
The patent applies different control strategies and variable sets to different operating regions. Each local linear model is associated with specific control variables that are most effective for that particular operating range, allowing the system to use simpler control approaches where appropriate while maintaining full multivariable capability when needed, thus optimizing the balance between control capability and system complexity.
Data Source
AI summary
A design method of aero-engine on-line optimization and multivariable control based on model prediction control realizes aero-engine multivariable control and on-line optimization according to thrust, rotational speed and other needs under the condition of meeting constraints. The first part is a prediction model acquisition layer that continuously establishes a small deviation linear model of an aero-engine near different steady state points based on the actual operating state of the aero-engine in each control cycle and external environment parameters and that supplies model parameters to a controller; and the second part is a control law decision-making layer which is a closed loop structure that consists of a model prediction controller and an external output feedback. The model prediction controller determines the output of the controller at next moment by solving a linear optimization problem according to an engine model in the current state, a control instruction and relevant constraints.