Model-Free Adaptive Disturbance Compensation for MIMO Tracking Control

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Solution Overview

Problem

Existing partial-form model-free adaptive control methods for MIMO systems fail to effectively address the challenge of compensation control in the presence of unmeasurable disturbances, leading to degradation or instability in control performance.

Innovation Solution

A method for partial-form model-free adaptive disturbance compensation control is introduced, which establishes a dynamic data model using pseudo Jacobian input and disturbance matrices, constructs cost functions, and employs a momentum gradient descent method to optimize control laws, thereby attenuating the effect of unmeasurable disturbances on MIMO systems.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional PID control methods are used for MIMO systems, then the control structure is simple and easy to implement, but the control performance degrades and becomes unstable in the presence of unmeasurable disturbances

Engineering Contradiction:

Improvecontrol performance stabilityVSAvoidcontrol method complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent introduces an intermediary disturbance compensation term derived from the dynamic data model that acts as a mediator between the control input and system output. This intermediary component specifically addresses unmeasurable disturbances by estimating their effect through the pseudo-Jacobian matrices and compensating for them in the control law, thereby improving reliability without requiring direct measurement of disturbances

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces the traditional PID mechanical control structure with a model-free adaptive control approach that uses data-driven pseudo-Jacobian matrices. This substitution eliminates the need for explicit disturbance models or measurements while maintaining control effectiveness through adaptive learning from input-output data, resolving the contradiction between simplicity and disturbance rejection capability

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If model-free adaptive control methods are used for MIMO systems, then physical information requirements are reduced, but the ability to compensate for unmeasurable disturbances is insufficient

Engineering Contradiction:

Improveapplication flexibilityVSAvoiddisturbance compensation capability

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent implements a feedback mechanism where the dynamic data model continuously learns from input-output data to update the pseudo-Jacobian matrices. This feedback loop enables the system to adapt to changing conditions and accurately estimate disturbance effects without requiring prior physical models, thereby simultaneously improving adaptability and disturbance compensation capability

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent dynamically changes the parameters (pseudo-Jacobian matrices) based on operating conditions by solving optimization problems with cost functions. This parameter adaptation allows the control method to maintain high reliability across different operating points while preserving the model-free adaptability advantage

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If dynamic data models with pseudo-Jacobian matrices are established, then disturbance estimation accuracy is improved, but the computational complexity and optimization requirements increase

Engineering Contradiction:

Improvedisturbance estimation accuracyVSAvoidoptimization computation complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent applies partial action by focusing the optimization only on the essential pseudo-Jacobian matrices needed for disturbance estimation, rather than optimizing all possible system parameters. The cost functions are designed to selectively update only the matrices directly related to disturbance compensation, reducing computational burden while maintaining estimation accuracy

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20240152122A1Partial-form model-free adaptive disturbance compensation control in the presence of unmeasurable disturbances

Publication Date: 2024.05.09 ZHEJIANG UNIV

AI summary

A method of partial-form model-free adaptive disturbance compensation control in the presence of unmeasurable disturbances includes establishing a dynamic data model of a controlled plant subject to unmeasurable 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 partial-form model-free adaptive disturbance compensation control law in the presence of unmeasurable disturbances; constructing an energy function and solving it by using a momentum gradient descent method to find optimal values of the partial-form adaptive input matrix and the partial-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.