PythonLinearNonLinearControl is a library implementing the linear and nonlinear control theories in python.
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Updated
Aug 23, 2021 - Python
PythonLinearNonLinearControl is a library implementing the linear and nonlinear control theories in python.
A modular and scalable simulation framework for ultra-rapid prototyping of self-adaptive, stochastic and robust Nonlinear Model Predictive Control for Autonomous Vehicle Motion Control
Library for simulation of nonlinear control systems, control design, and Lyapunov-based learning.
MIT Spring 2020 Mechanical Engineering 2.152 Nonlinear Control (Professor Jean-Jacques Slotine)
Robust and nonlinear Direct Data-Driven MPC controllers for LTI and nonlinear systems in Python
JAX compilation of RDDL description files, and a differentiable planner in JAX.
Github mirror of https://gitlab.kuleuven.be/meco-software/impact
Airborne Nonlinear Guidance Simulator for Teaming (ANGST)
A toolbox to simulate and analyse nonlinear systems in a control context.
Code base for disturbance-robust backup control barrier functions (DR-bCBFs).
Code accompanying the publication "Guiding Soft Robots with Motor-Imagery Brain Signals and Impedance Control" that received the Best Paper Award at the RoboSoft 2024.
AI-Driven Control Strategy for Differential Drive Wheeled Mobile Robots: Neural Network-Based Parameter Optimization and Real-Time Stabilization for Discretized Path Tracking.
Control of the Three-Link Robotic Manipulator discussed in Section VI of Matrix-Scheduling of QSR-Dissipative Systems
Gurobi compilation of RDDL description files to mixed-integer programs, and optimization tools.
A python simulation demonstrating a feedback controller for wheeled mobile robots (WMR)
Control of the Two-Link Robotic Manipulator shown in Figure 2 of Passivity-Based Gain-Scheduled Control with Scheduling Matrices
Simulator of a Two Wheeled Inverted Pendulum Dynamics and Controllers
Hierarchical Split-Brain Architecture for Real-Time Nonlinear Control (Lorenz Chaos) on ESP8266 via Python.
Dealing with nonlinear processes and their complexity for confident process control
Lab exercises on robotics
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