taowu520136

2022-code-CDC-A-Distributed-Linear-Quadratic-Discrete-Time-Game-Approach-to-Multi-Agent-Consensus

Code for paper "Decoupling Approach for Solving Linear Quadratic Discrete-Time Games with Application to Consensus Problem" by P. Aditya and H. Werner

A-data-driven-method-for-linear-quadratic-control-with-state-and-control-constraints

A data-driven method for linear quadratic control with state and control constraints

A-VA-and-VS-Approach-for-Event-Triggered-Fault-Tolerant-Control-of-Multi-Agent-Systems

Safeprocess-2024 A Virtual Actuator and Sensor Approach for Event-Triggered Fault-Tolerant Control of Multi-Agent Systems

acc2023-rs-rp-paper

Paper and auxiliaries for the conference paper entitled "Maintaining Robust Stability and Performance through Sampling and Quantization", presented at the 2023 American Control Conference, May 31 - June 2, 2023 | San Diego, CA, USA

continuous-time-Value-Iteration

Implementation of model-free Value Iteration algorithm to design optimal state feedback controller for continuous-time LTI systems

Continuous_Finite_LQR

Derivation and implementation of continuous-time finite-horizon Linear Quadratic Regulator

Learning-Based-MPC

Learning-Based Model Predictive Control (LBMPC)

OTCP_Quad

An Off-policy Reinforcement Learning Algorithm for Optimal Tracking Control Problem

QLMI

Matlab Implementation of the paper "Model-Free LQR Design by Q-function Learning"

Resilient-MPC-for-Cyber-Physical-Multi-Agent-Systems-under-DoS-Attacks

taowu520136/A-VA-and-VS-Approach-for-Event-Triggered-Fault-Tolerant-Control-of-Multi-Agent-Systems

Safeprocess-2024 A Virtual Actuator and Sensor Approach for Event-Triggered Fault-Tolerant Control of Multi-Agent Systems

taowu520136/continuous-time-Value-Iteration

Implementation of model-free Value Iteration algorithm to design optimal state feedback controller for continuous-time LTI systems

taowu520136/OTCP_Quad

An Off-policy Reinforcement Learning Algorithm for Optimal Tracking Control Problem

taowu520136/QLMI

Matlab Implementation of the paper "Model-Free LQR Design by Q-function Learning"

taowu520136/acc2023-rs-rp-paper

Paper and auxiliaries for the conference paper entitled "Maintaining Robust Stability and Performance through Sampling and Quantization", presented at the 2023 American Control Conference, May 31 - June 2, 2023 | San Diego, CA, USA

taowu520136/Book-Mathmatical-Foundation-of-Reinforcement-Learning

This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning."

taowu520136/phd-code

taowu520136/AdaDL-for-HJB-Equations

Framework for Optimal Feedback Control of Reaction-Diffusion Systems via Adaptive Deep Learning

taowu520136/consensus-synchronization-of-MAS

consensus and synchronization algorithms of networked multi agent systems with event triggered communications

taowu520136/D3IOC-Direct-data-driven-inverse-optimal-control

taowu520136/data-driven-MPC-of-helicopters

Software for Data-driven Model Predictive Control of 3-DoF Helicopters

taowu520136/data_driven_pos

Data-driven stabilization of positive linear systems. Full-state feedback and Linf sample noise by default.

taowu520136/FedLQR

Code for the examples in the paper: Model-free Learning with Heterogeneous Dynamical Systems: A Federated LQR Approach

taowu520136/fixed_time_consensus_based_event_triggered_nash_equilibrium_seeking

taowu520136/IQCs-for-Discrete-Time-Impulsive-Uncertainties

IQC Based Analysis and Estimator Design for Discrete-Time Systems Affected by Impulsive Uncertainties

taowu520136/lb_sd

Learning-based robust sampled-data control for uncertain systems.

taowu520136/lqr_continuous

Simple example of a finite-horizon LQR in continuous time

taowu520136/LQR_SVRPG

Code for Stochastic Variance-Reduced Policy Gradient for the LQR problem

taowu520136/Multi-agent-System-control

reference code of the MAS textbook which written by Zhao Jichao

taowu520136/NonZeroSumGame-RL-OffPolicy-for-DT-System

taowu520136/Online-Dynamic-Learning-and-Robust-Control-for-Nonlinear-Systems

We try to answer the following question "Can online reinforcement learning (RL) methods learn the dynamics of non-stationary and nonlinear systems online, and if so, can it be used in the robust control architecture instead of the inverse nonlinear dynamics term?"

taowu520136/PDE_CONTROL

PDE boundary control

taowu520136/Policy_Iteration_LQR

Model-Free Optimal Control Design Using Policy Iteration for LQR Problems - MATLAB

taowu520136/Polytopic-SLSMPC

Implement SLS MPC for linear systems subject to polytopic model uncertainty and additive disturbances.

taowu520136/RL_Nash_Games

Reinforcement Learning Policy Iterations for Nash Differential Games

taowu520136/RL_tracking_PI_algorithm_single_agent

the code implements a training algorithm for a tracking control system using dynamic programming and reinforcement learning. It uses neural networks to approximate the control policy and iteratively updates the networks to improve the system's performance.

taowu520136/Solving-the-discrete-LQR-problem-using-Reinforcement-Learning

taowu520136/stochasticDDP

Seminar Control, Identification and Learning Algorithms on Stochastic Differential Dynamic Programming (SDDP)

taowu520136/TDRC-Chaos

taowu520136/TDRC-DeePC