Dynamic Gain PID Controller for Prosthetic Muscle Force Simulation
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Solution Overview
Problem
Current biomechanical simulation tools face limitations in accurately determining muscle forces due to sequential static solutions and the inability to utilize proportional-integral-derivative (PID) control for optimizing muscle recruitment patterns, which results in potential accuracy issues and failure to preferentially choose the solution with minimum muscle effort.
Innovation Solution
A computer-implemented system that uses dynamic gains within a PID control scheme to optimize muscle forces, allowing for true dynamic solutions that meet user-defined objectives while maintaining physiological limits and kinematic profiles, by integrating a feedback control loop with a dynamic gain module to adjust PID gains over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sequential static optimization is used to determine muscle forces, then the solution can be computed at each time step, but the muscle forces are fixed over time periods rather than being truly dynamically varying, reducing accuracy
Solution Approach 1:
The patent transitions from static optimization to dynamic optimization by implementing a PID control system where muscle forces are continuously adjusted based on real-time feedback from muscle kinematics. The dynamic nature of the PID controller allows muscle forces to vary continuously over time rather than being fixed, accurately capturing the dynamic behavior of real muscles while maintaining computational efficiency through the control framework.
Solution Approach 2:
The patent introduces a feedback control mechanism where muscle kinematics (length, velocity, or joint angle) are sensed and compared to target values, and the PID controller uses this feedback to continuously adjust muscle forces. This closed-loop feedback system enables truly dynamic muscle force determination while maintaining computational tractability, resolving the contradiction between static computation and dynamic accuracy.
2Manufacturing precision
If direct force optimization is used, then muscle forces can be determined within physiological limits, but the advantages of PID control (solution stability and speed) are lost
Solution Approach 1:
The patent merges the benefits of direct force optimization with PID control by integrating the optimization objective (minimizing muscle force while meeting kinematic goals) into the PID control framework. The PID controller structure is retained to provide stability and speed, while the control parameters are optimized to achieve the desired muscle recruitment pattern. This combination maintains solution stability and computational speed while achieving precise muscle force determination.
Solution Approach 2:
The patent changes the parameters of the PID controller (gains and target values) based on the optimization objective. By adjusting the PID parameters dynamically, the system achieves both the stability and speed of PID control and the precision of optimized muscle force determination. The parameter changes allow the control system to adapt to different muscle recruitment scenarios while maintaining computational efficiency.
3Reliability
If classical PID control with constant gains is used, then solution stability and speed are achieved, but the system cannot preferentially choose recruitment patterns that minimize muscle force
Solution Approach 1:
The patent makes the PID controller dynamic by allowing the gains and target values to change over time based on the optimization objective. This dynamic adjustment enables the system to preferentially select recruitment patterns that minimize muscle force while maintaining the stability and speed benefits of PID control. The dynamic parameters allow adaptation to different recruitment scenarios without sacrificing solution reliability.
Solution Approach 2:
The patent performs preliminary optimization to determine the optimal PID parameters before running the simulation. By pre-calculating the gains and target values that will achieve the desired muscle recruitment objective, the system ensures both stability during simulation and optimization of the recruitment pattern. This preliminary action resolves the contradiction between maintaining stable control and achieving optimized recruitment.
Data Source
AI summary
Systems and methods for determining muscle force are presented. Proportional, integral, and derivative control is used to simulate muscle forces for multiple muscles contributing to a kinematic profile. The simulated muscle forces are modified by dynamic gains that are calculated in order to achieve a muscle recruitment objective such as minimizing collective muscle effort while still achieving the kinematic motion.