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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 neural network is based on the back-propagation method with a small modification. In addition, in order to guarantee the robustness of the observer, an e-modification term is added. Also, it is noted that there are no hard constraints like SPR on this method. Subsequently, the stability of this neural network observer is guaranteed by Lyapunov's direct method. At the end, the simulation results for a flexible-joint manipulator with this observer is demonstrated.
This repository contains lecture notes, solved problems, and simulation software on control systems in general.
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
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
Real time parameter estimation on Grey Box Dynamic Systems
LQR-LQG-Controller Design
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.
Research on Control of Cyber-Physical Systems
Projeto de iniciação científica que utiliza os conceitos de mecânica quântica moderna e controle não linear para resolver problemas de sistemas quânticos abertos
Project from an introductory course in hybrid systems focussed on stability and performance.
Material from the course of Nonlinear Systems and Robustness at ENSEM - Université de Lorraine.
Codebase of "Actor-Critic Reinforcement Learning for Control with Stability Guarantee" by Han et al. 2020