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 do not effectively address the challenge of compensation control in the presence of measurable disturbances, which affects control performance and stability.
Innovation Solution
A method for partial-form model-free adaptive disturbance compensation control is introduced, utilizing a dynamic data model described by pseudo Jacobian input and disturbance matrices, with optimization of these matrices to design a control law that attenuates disturbances and stabilizes the system using I/O data without physical information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If existing partial-form model-free adaptive control methods are used for MIMO systems, then the control system can operate without physical information, but the control performance degrades in the presence of measurable disturbances
Solution Approach 1:
The control law is segmented into two distinct parts: a disturbance compensation term and a tracking control term. The disturbance compensation term specifically addresses measurable disturbances by using the disturbance measurement signal, while the tracking control term handles the reference tracking. This segmentation allows each term to optimize for its specific function, resolving the contradiction between ease of setup and reliability under disturbance conditions.
Solution Approach 2:
The disturbance measurement signal serves as an intermediary that bridges the gap between the disturbance source and the control action. By introducing this intermediate measurement, the system can actively compensate for disturbances without requiring complete physical models, thus maintaining both ease of implementation and improved reliability in disturbed environments.
2Reliability
If disturbance compensation control is implemented in MIMO systems with measurable disturbances, then control performance and stability improve, but the control law complexity increases
Solution Approach 1:
The control law uses adaptive parameters that are updated online based on measurement data. The pseudo-Jacobian matrices are estimated from input-output data without requiring physical models, and the control parameters are continuously adjusted to optimize performance. This parameter adaptation approach improves stability under disturbance while avoiding the complexity of fixed complex control structures.
Solution Approach 2:
The control system is self-adjusting through online estimation of pseudo-Jacobian matrices from operational data. The disturbance compensation terms are automatically computed based on measured disturbances and adaptive parameters, without requiring manual tuning or complex predetermined control structures. This self-service mechanism improves reliability while keeping the control law implementation relatively simple.
3Ease of operation
If traditional PID control methods are used, then the control law is simple to implement, but the ability to attenuate measurable disturbances is insufficient
Solution Approach 1:
The control law includes a preliminary anti-action term that actively counteracts measurable disturbances before they significantly affect the system output. By using the disturbance measurement signal to compute a compensation term, the system preemptively counteracts the harmful effect of disturbances, going beyond the reactive nature of traditional PID control while maintaining similar implementation complexity.
Data Source
AI summary
A method of partial-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 partial-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 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.