Actuator Control Apparatus with Dynamic Parameter Compensation
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Solution Overview
Problem
Existing speed and position control systems for actuators, particularly in electric vehicles, face challenges in achieving precise control due to varying motor parameters and environmental conditions, leading to instability and fine vibrations.
Innovation Solution
An actuator control apparatus that includes an observer to estimate load states, a controller to output control signals, a compensation unit to adjust these signals based on estimated parameter changes, and a parameter estimator to update modeled parameters using a neural network and Recursive Least Square Method, enhancing control precision by accounting for parameter variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a PI controller is used for speed control, then the controller can be easily implemented, but it does not provide high performance in tracking control
Solution Approach 1:
The patent combines a PI controller with a disturbance observer and parameter compensation unit to create a hybrid control system. The PI controller handles basic speed control while the disturbance observer estimates and compensates for disturbances, and the parameter compensation unit adjusts for parameter variations, achieving both ease of implementation and high control performance.
Solution Approach 2:
The patent introduces a disturbance observer as an intermediary component that estimates disturbances and feeds this information to the controller. This intermediary layer enables the simple PI controller to achieve high-performance tracking by compensating for disturbances through the observer's estimates.
2Measurement precision
If a disturbance observer is used to detect inputs, then control precision is improved, but parameter differences between observer and real system cause control issues
Solution Approach 1:
The patent implements a dynamic parameter compensation unit that continuously estimates parameter variations using a neural network and updates the observer parameters in real-time. This dynamic adaptation allows the disturbance observer to maintain accuracy despite parameter differences between the observer model and the actual system, ensuring both precision and stability.
Solution Approach 2:
The patent changes the parameters of the disturbance observer dynamically by using a neural network to estimate parameter variations and compensate for them. This parameter adaptation mechanism allows the observer to track the actual system parameters, maintaining control precision and stability even when system parameters change.
3Stability of the object's composition
If VSS and fuzzy logic are used for high rigidity control, then control rigidity is improved, but fine vibrations cannot be completely removed
Solution Approach 1:
The patent employs a disturbance observer that provides feedback on estimated disturbances to the control system. This feedback mechanism allows the controller to actively compensate for disturbances and vibrations, achieving high rigidity while suppressing fine vibrations through continuous disturbance estimation and compensation.
Solution Approach 2:
The patent extracts and separates the disturbance estimation function from the main control loop by using a dedicated disturbance observer. This extracted disturbance estimate is then used to compensate for vibrations, allowing the system to achieve high rigidity while removing fine vibrations through the separate compensation path.
4Adaptability or versatility
If motor parameters are adapted to environmental conditions, then control flexibility is improved, but parameter changes cause instability
Solution Approach 1:
The patent uses a neural network to predict and estimate parameter variations before they significantly affect system performance. By preliminarily estimating parameter changes based on environmental conditions and compensating in advance, the system maintains stability while adapting to environmental variations.
Solution Approach 2:
The patent implements a feedback mechanism where the disturbance observer continuously monitors system behavior and estimates parameter variations. This feedback information is used to dynamically adjust the controller parameters, allowing the system to adapt to environmental conditions while maintaining stability through continuous compensation.
Data Source
AI summary
Provided is an actuator control apparatus including: an observer configured to estimate a state of a load based on a state equation including at least one modeled load parameter; a controller which outputs a signal for controlling the load; a compensation unit which compensates for the signal output from the controller; and an estimator configured to estimate a change of a load parameter, to decide a gain of the compensation unit based on the estimated change of the load parameter, and to update the modeled load parameter.