Adaptive Autonomous Controller for Energetic State Management
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Solution Overview
Problem
Existing autonomous machines face challenges in maximizing energy efficiency and maintaining a positive energetic state, as conventional controllers often converge to locally optimal actions rather than globally optimal results, leading to inefficient energy restoration and operation.
Innovation Solution
The invention employs a control system with dual predictive models that distinguish between internal and external environments using orthogonal common mode signals, enabling adaptive learning to minimize error flux and optimize energy management through a rechargeable offline power supply.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional controllers are used, then the machine can operate autonomously, but the controller converges to locally optimal actions rather than globally optimal results, leading to inefficient energy restoration
Solution Approach 1:
The control system is divided into multiple specialized modules: a predictive model generator that creates multiple possible future state predictions, an evaluative model that assesses each prediction's energetic value, and a controller that selects actions based on evaluated predictions. This segmentation allows the system to explore multiple potential pathways rather than converging to a single local optimum, improving energy restoration efficiency while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The controller employs dynamic model generation where predictive models are continuously updated based on incoming sensor data and changing environmental conditions. The system dynamically adjusts its prediction horizon and model parameters to adapt to varying operational states, enabling it to escape local optima and find globally optimal energy restoration strategies rather than being constrained by static control parameters.
2Duration of action of moving object
If the machine maximizes energy efficiency, then the time periods between energy restoration increase, but sufficient energy must be stored to facilitate restoration
Solution Approach 1:
The predictive model generator creates multiple prospective action sequences in advance, evaluating potential energy restoration pathways before actual energy depletion occurs. By planning and simulating future actions based on current sensor data and environmental conditions, the system can identify optimal energy harvesting opportunities ahead of time, extending operational duration without requiring proportionally larger energy storage capacity.
Solution Approach 2:
The evaluative model continuously monitors actual energy consumption and restoration outcomes, comparing them against predicted values. This feedback loop allows the system to refine its predictive models and adjust its energy management strategy in real-time, optimizing the balance between operational duration and required storage capacity by learning from actual performance deviations.
3Adaptability or versatility
If adaptive learning is implemented, then the controller can update predictive models according to measured events and outcomes, but the system complexity increases
Solution Approach 1:
The controller implements self-service learning where the evaluative model automatically assesses prediction accuracy and triggers model updates based on measured deviations between predicted and actual outcomes. The system autonomously identifies when learning is needed, selects relevant features for model refinement, and updates predictive parameters without external intervention, enhancing environmental adaptability while keeping the learning mechanism integrated within the existing control architecture rather than adding separate complex learning systems.
Data Source
AI summary
An autonomous machine arranged to provide an observable measure of the mechanical energy dissipated by the machine relative to that stored by the machine, so that the decision making of the machine's control system can be based on a real energetic stress of the machine. The autonomous machine has a control system with predictive models for the internal and external environments. Both predictive models are based on the same set of information representing a common energetic basis of the machine. The set of information includes: (i) a plurality of reciprocal signals indicative of the machine's direct interactions, and (ii) a plurality of non-reciprocal signals indicative of information that is available to the machine without requiring it to expend energy. The plurality of non-reciprocal signals includes emulated signals where needed to ensure that the predictive models for the internal and external environments are based on an equivalent set of parameters.