Feedback Controller Using Probabilistic Solvers for Fast Convergence
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Solution Overview
Problem
Existing control systems struggle with complex dynamics, particularly in systems with unknown analytical forms, leading to slow convergence and inefficiencies in real-time applications, such as in legged robots and soft robotics, due to deterministic nature and iterative optimization methods.
Innovation Solution
A probabilistic framework using a Kalman filter is employed to estimate control inputs, iteratively updating them until a termination condition is met, allowing for real-time control of devices with complex dynamics by formulating the optimal control problem as a probabilistic optimization problem.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deterministic iterative optimization methods are used to solve optimal control problems, then control accuracy is improved, but convergence speed deteriorates leading to slow real-time performance
Solution Approach 1:
The patent replaces deterministic iterative optimization methods with a probabilistic solver that uses Monte Carlo sampling and importance sampling techniques. This substitution transforms the control system from a deterministic iterative approach to a probabilistic parallel computation approach, achieving both high accuracy and fast convergence in real-time control applications.
2Measurement precision
If complex analytical models are used to capture system dynamics accurately, then control precision is improved, but computational complexity increases making real-time control difficult
Solution Approach 1:
The patent changes the parameter representation from deterministic model parameters to probabilistic distributions. By representing system dynamics and control inputs as probability distributions and using importance sampling, the method captures complex dynamics accurately while maintaining computational tractability through parallel Monte Carlo evaluation rather than complex analytical solutions.
3Ease of operation
If traditional gradient-based optimization is used for control, then analytical solutions are obtained, but handling of discontinuous functions and complex dynamics fails
Solution Approach 1:
The patent replaces gradient-based optimization with a probabilistic solver using Monte Carlo methods. This substitution enables the system to handle discontinuous functions and complex dynamics that are intractable for gradient-based methods, while still providing analytical-like solutions through the probabilistic framework's ability to evaluate complex integrals and optimization problems.
Data Source
AI summary
The present disclosure provides a feedback controller and method for controlling an operation of a device at different control steps. The feedback controller comprises at least one processor, and the memory having instructions stored thereon that, when executed by the at least one processor, causes the feedback controller, for a control step, to collect a measurement indicative of a state of the device at the control step, and execute, recursively until a termination condition is met, a probabilistic solver parameterized on a control input to an actuator operating the device to produce a control input for the control step. The feedback controller is further configured to control the actuator operating the device based on the produced control input.