Double Dynamic Model Control for Autonomous Actuator Powertrains
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Solution Overview
Problem
Autonomous motion systems face challenges in controlling complex motion in dynamically changing environments without continuous observability and controllability, particularly in real-world scenarios like on-road and off-road conditions for autonomous vehicles and in live tissue for robotic surgical devices.
Innovation Solution
The Double Dynamic Model (DDM) integrated with the Energy Exchange System (EES) platform, which includes a first component representing an autonomous motion system and a second component simulating its environment, uses sensorless actuators and a processor to generate control vectors for mechanical energy exchange, recalculating model parameters to predict and regulate dynamic behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If autonomous motion systems operate in dynamically changing environments without continuous observability, then adaptability is improved, but control reliability deteriorates
Solution Approach 1:
The patent creates a virtual copy of the physical system through the Double Dynamic Model. The first dynamic model represents the actual autonomous motion system, while the second dynamic model represents a virtual copy that simulates system behavior. This virtual copy allows continuous observation and control calculations without requiring continuous physical observability, resolving the contradiction between adaptability to dynamic environments and control reliability.
Solution Approach 2:
The Energy Exchange Platform acts as an intermediary between the physical system and the virtual model. It receives control data, operation data, and learning data from both the actual system and the virtual model, processes this information, and generates control vectors. This intermediary enables continuous control reliability even when direct observability of the physical system is interrupted, while maintaining adaptability through the virtual model's simulation capabilities.
2Speed
If sensorless actuators are used for fast control, then response speed is improved, but measurement precision deteriorates
Solution Approach 1:
The patent replaces physical sensors with a virtual sensing system based on the Double Dynamic Model. Instead of using sensorless actuators that lack measurement capability, the system uses the second dynamic model to virtually sense and estimate the system state. This virtual sensing provides measurement precision without the speed limitations of physical sensors, as the model can process and predict state information computationally.
Solution Approach 2:
The virtual copy in the second dynamic model serves as a sensorless actuator's state estimator. By simulating the system dynamics and comparing with control inputs, the virtual model infers system state without requiring physical sensors. This copying approach maintains fast control response while achieving measurement precision through computational estimation rather than physical measurement.
3Manufacturing precision
If double dynamic model with parameter recalculation is implemented, then control accuracy is improved, but device complexity increases
Solution Approach 1:
The Energy Exchange Platform performs multiple functions: it manages both dynamic models, processes control data and operation data, generates control vectors, and handles learning data. By consolidating these functions in a single platform rather than separate systems, the patent achieves high control accuracy through the Double Dynamic Model while minimizing the increase in device complexity through functional integration.
Solution Approach 2:
The patent merges the two dynamic models and their parameter recalculation processes into a unified Energy Exchange Platform. Instead of having separate systems for each model, the platform integrates both models and performs coordinated parameter recalculation, achieving improved control accuracy while reducing overall system complexity through consolidation and shared computational resources.
Data Source
AI summary
Disclosed are actuators and power-train sub-systems of Autonomous Motion Systems (AMS), having a Double Dynamic Model (DDM) with combined functionality of control (including continuous controllability), operation (e.g., automated/robotic/autonomous operation in normal mode or safety mode) and learning using Energy Exchange System (EES) platform. DDM solution provides ability for an AMS to operate in real-world scenarios involving dynamic geometry and changing physical environment. For example, the first component can be an actuator and power train (A&P) system of an autonomous vehicle. The second component can be an autonomous simulation and test (AST) fixture on which a wheel of the autonomous vehicle is mounted, wherein the vehicle AST simulates a road or off-road condition for the autonomous vehicle. In another example, the first component can be a surgical A&P system of a robotic surgical device, and the second component can be a surgical AST that simulates an environment of living tissue.