Model-Free Adaptive Disturbance Compensation for MIMO Control
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Solution Overview
Problem
Existing full-form model-free adaptive control methods for MIMO systems do not effectively address the challenge of compensation control in the presence of measurable disturbances, limiting their application and performance in industrial control systems.
Innovation Solution
A method of full-form model-free adaptive disturbance compensation control is introduced, which involves establishing a dynamic data model using pseudo Jacobian input and disturbance matrices, constructing cost functions, and employing a momentum gradient descent method to optimize adaptive input and disturbance matrices, thereby designing a control law that attenuates disturbances and stabilizes the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional PID control methods are used, then the control structure is simple and easy to implement, but the control performance degrades in the presence of measurable disturbances
Solution Approach 1:
The control system performs self-identification of the dynamic data model and self-adjustment of control parameters through iterative optimization algorithms. The pseudo Jacobian matrices are automatically updated using I/O data without requiring external system identification or manual tuning, enabling the system to adapt to changing conditions and disturbances autonomously
Solution Approach 2:
The control method dynamically changes the parameters of the dynamic data model (pseudo Jacobian input matrix and pseudo Jacobian disturbance matrix) based on real-time I/O data. The control law parameters are continuously adjusted through cost function optimization, allowing the system to maintain optimal performance despite varying operating conditions and disturbance characteristics
2Reliability
If model-based control methods are used, then the control performance can be improved, but the requirement for accurate system models increases complexity
Solution Approach 1:
The patent replaces traditional mechanical model-based control with a data-driven dynamic data model approach. Instead of relying on physical system models and mathematical equations, the method uses pseudo Jacobian matrices derived directly from input-output data to represent system dynamics, eliminating the need for complex system identification and physical modeling
Solution Approach 2:
The system automatically identifies and updates its own dynamic characteristics through real-time data processing. The pseudo Jacobian matrices are computed on-line from I/O data without requiring external system identification experiments or manual model parameter tuning, enabling the system to adapt to changing conditions autonomously
3Adaptability or versatility
If full-form model-free adaptive control is used for MIMO systems, then the control method can handle complex systems without physical information, but it cannot effectively compensate for measurable disturbances
Solution Approach 1:
The patent segments the control problem by separately identifying and controlling the effects of control inputs and disturbances on system outputs. The dynamic data model is divided into two distinct pseudo Jacobian matrices: one for input effects and one for disturbance effects. This segmentation allows independent optimization of control and disturbance compensation, enabling effective MIMO control with disturbance rejection
Solution Approach 2:
The method dynamically adjusts the parameters of both the pseudo Jacobian input matrix and pseudo Jacobian disturbance matrix based on real-time I/O data. By continuously updating these parameters through cost function optimization, the system can adapt to changing system characteristics and disturbance patterns, maintaining effective disturbance compensation in MIMO configurations
Data Source
AI summary
A method of full-form model-free adaptive disturbance compensation control in the presence of measurable disturbances, includes establishing a dynamic data model of a controlled plant subject to measurable disturbances, wherein the dynamic data model is described by a pseudo Jacobian input matrix and a pseudo Jacobian disturbance matrix; constructing cost functions and solving their optimization problems to find optimal values of the pseudo Jacobian input matrix and the pseudo Jacobian disturbance matrix; designing a full-form model-free adaptive disturbance compensation control law in the presence of measurable disturbances; constructing an energy function and solving it by using a momentum gradient descent method to find optimal values of the full-form adaptive input matrix and the full-form adaptive disturbance matrix; controlling the controlled plant by using the control law. The control method of the present invention provides significant improvements in disturbance compensation control performance and achieves effective tracking of desired system outputs.