Lyapunov-stable Neural Control for State and Output Feedback
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Updated
Jul 15, 2024 - Python
Lyapunov-stable Neural Control for State and Output Feedback
In this project, an observer in the form of a stable neural network is proposed for any nonlinear MIMO system. As a result of experience, this observer utilizes a nonlinear in parameter neural network (NLPNN) which unlike LPNN, supports systems with higher degree of nonlinearity with no pre-knowledge of its dynamics. The learning rule for this n…
A mobile robot equipped with a 3D camera moves in a room with four circular areas, and it has to localise and classify four objects which are positioned inside every area. A robotic arm with 6-DoF picks up the object and places it in a basket according to its class
Research on Control of Cyber-Physical Systems
Official Implementation of L4DC paper: Compositional Neural Certificates for Networked Dynamical Systems
The Project involves 3 typical convex optimization problems in control and the SDP (Semi-definite Programming) form or in other words the LMI (Linear Matrix Inequalities) form of each problem is achieved analytically and then the SDP optimization problems are solved in MATLAB using MATLAB CVX toolbox.
Efficient Computation of Lyapunov Functions Using Deep Neural Networks for the Assessment of Stability in Controller Design
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VNAE: A geometric approach to global stability in massive multi-agent systems with asymmetric dissipation, validated at scale.
This repository contains the complete supplementary material for the canonical/quadratic example of the VNAE framework.
This my master's degree graduation thesis, it's about DC motor speed control using the sliding mode method, the motor it's controlled based on three models which are cascade and reduced, and complete model. the method has proved that it's robust against dc motor parameters changing and able to track a reference speed.
Symbolic regression of Control Lyapunov Functions.
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Interactive Monte Carlo simulation of the VNAE framework. A visual playground to test how Theta and Beta parameters influence win rates in asymmetric systems.
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