# Innovative approach of nonlinear controllers design for prosthetic knee performance

## TL;DR

This paper introduces new nonlinear control strategies for prosthetic knees to improve mobility and gait by using advanced algorithms and optimization techniques.

## Contribution

The novel contribution is the development and comparison of three robust nonlinear controllers optimized with Red Fox Optimization for prosthetic knee joints.

## Key findings

- The CoBA-based controller showed the highest tracking accuracy and fastest convergence.
- Simulation and hardware-in-the-loop testing confirmed the practical effectiveness of the control framework.
- Red Fox Optimization improved controller performance by fine-tuning gain parameters.

## Abstract

Prosthetic knee joints are essential assistive technologies designed to replicate natural gait and improve mobility for individuals with lower-limb loss. This study presents a comprehensive nonlinear dynamic model of a two-degree-of-freedom prosthetic knee joint and introduces three robust nonlinear control strategies: Integral Sliding Mode Control, Conditional Super-Twisting Sliding Mode Control, and Conditional Adaptive Positive Semidefinite Barrier Function-based Sliding Mode Control. These controllers are designed to address the challenges associated with nonlinear joint dynamics, external disturbances, and modeling uncertainties during locomotion. To optimize control performance, the gain parameters of each controller were fine-tuned using Red Fox Optimization, a metaheuristic algorithm inspired by the intelligent hunting behavior of red foxes. Stability analysis is conducted using Lyapunov theory, and control effectiveness is evaluated through simulations in MATLAB/Simulink and validated via hardware-in-the-loop testing using a C2000 Delfino F28379D microcontroller. Among the three controllers, the CoBA-based approach demonstrated the highest tracking accuracy, fastest convergence, and smoothest torque profile. The close agreement between simulation and experimental results confirms the practical applicability of the proposed control framework, offering a promising solution for intelligent and adaptive prosthetic knee systems.