Compact-form model-free adaptive disturbance compensation control in the presence of unmeasurable disturbances
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Solution Overview
Problem
Existing compact-form model-free adaptive control methods for MIMO systems fail to effectively address the challenge of unmeasurable disturbances, leading to degradation of control performance and instability in industrial control systems.
Innovation Solution
A compact-form model-free adaptive disturbance compensation control method is developed, 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 adaptive input and disturbance matrices, thereby designing a control law that attenuates unmeasurable disturbances and stabilizes the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing compact-form model-free adaptive control methods are used for MIMO systems, then the control structure remains simple and easy to implement, but the control performance degrades and instability occurs in the presence of unmeasurable disturbances
Solution Approach 1:
The patent segments the control problem by separately estimating input and disturbance effects through pseudo-Jacobian matrices. The dynamic data model divides the system response into controllable input components and disturbance components, allowing independent optimization of each while maintaining overall system stability and performance.
Solution Approach 2:
The patent introduces pseudo-Jacobian matrices as intermediary elements that mediate between the control inputs and system outputs, and between disturbances and outputs. These matrices serve as adaptive intermediaries that capture the system's dynamic characteristics without requiring explicit mathematical models, enabling effective disturbance compensation.
2Reliability
If traditional PID control methods are used, then the control implementation is simple, but the ability to attenuate unmeasurable disturbances and track desired outputs is insufficient
Solution Approach 1:
The patent implements adaptive feedback mechanisms where the pseudo-Jacobian matrices are continuously updated based on real-time system responses. The cost functions provide feedback signals that guide the optimization of input and disturbance estimates, enabling the system to adapt to changing disturbance conditions and improve tracking performance dynamically.
Solution Approach 2:
The patent performs preliminary estimation of disturbance effects through the pseudo-Jacobian disturbance matrix before applying compensation. By proactively modeling and estimating disturbance impacts in advance, the control system can pre-compensate for anticipated disturbances, improving the attenuation capability before they fully affect system outputs.
3Reliability
If dynamic data models with pseudo-Jacobian matrices are established to compensate for disturbances, then disturbance attenuation improves, but the computational complexity and optimization requirements increase
Solution Approach 1:
The patent applies partial optimization by focusing computational efforts on optimizing the pseudo-Jacobian matrices and cost functions only when necessary for disturbance compensation. Rather than continuously optimizing all control parameters, the method selectively optimizes disturbance-related components, reducing computational overhead while maintaining effective disturbance attenuation.
Solution Approach 2:
The patent dynamically changes parameters such as the pseudo-Jacobian matrices and cost function weights based on system conditions. By adapting these parameters in response to disturbance levels and system states, the computational complexity is adjusted to match the actual disturbance compensation needs, avoiding unnecessary computational overhead during low-disturbance periods.
Data Source
AI summary
A method of compact-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 compact-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 compact-form adaptive input matrix and the compact-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.