Pinned Repositories
Adaptive-bias-RBFNN-control
Adaptive_Sliding_Mode_Controller_Aerial_Manipulator
AdaptiveCriticDesigns
ACD project
AFF-RBFNN-Control-with-the-DPE
paper code of "Adaptive feedforward RBF neural network control with the deterministic persistence of excitation"
AFISMC
a new observer-based adaptive fuzzy integral sliding mode controller (AFISMC) is proposed based on the Lyapunov stability theorem. The plant under study is subjected to a square-integrable disturbance and is assumed to have mismatch uncertainties both in state- and input-matrices. In addition, a norm-bounded time varying term is introduced to address the possible existence of un-modelled/nonlinear dynamics. Based on the classical sliding mode controller (SMC), the equivalent control effort is obtained to satisfy the sufficient requirement of SMC and then the control law is modified to guarantee the reachability of the system trajectory to the sliding manifold. The sliding surface is compensated based on the observed states in the form of linear matrix inequality (LMI). In order to relax the norm-bounded constrains on the control law and solve the chattering problem of SMC, a fuzzy logic (FL) inference mechanism is combined with the controller. An adaptive law is then introduced to tune the parameters of the fuzzy system on-line. Finally, by aiming at evaluating the validity of the controller and the robust performance of the closed-loop system, the proposed regulator is implemented on a real-time mechanical vibrating system.
boundedline-pkg
Plot line(s) with error bounds/confidence intervals/etc. in Matlab
Code-for-TC
code for paper Event-Triggered Impulsive Control for Nonlinear Stochastic Systems
DeepMPC
Tube based Model Predictive Control that utilize Neural Networks to improve performance with adaptive control inputs.
Event-triggered-controller
Simple examples for Event-triggered control for network controlled system
reinforcement-learning
Implementation of Single-Agent and Multi-Agent Reinforcement Learning Algorithms. MATLAB.
ZhuLinche's Repositories
ZhuLinche/flocking_network
(Complete). An implementation of flocking for a large network of aerial vehicles
ZhuLinche/DMPC-for-multi-agent
Distributed model predictive control for multi-agent point-to-point transitions.
ZhuLinche/consensus-synchronization-of-MAS
ZhuLinche/RobustAdaptiveMPC
ZhuLinche/EGH400-Event-Triggered-Consensus
Creating future technologies for unmanned aircraft / drone traffic management
ZhuLinche/Value_and_Policy_iterations_to_solve_the_Bellman_Equation_for_Grid_Navigation_MDP
ZhuLinche/auv_lqr
Addressing the combined problem of trajectory planning and tracking control for under-actuated autonomous underwater vehicle (AUV).
ZhuLinche/Robust-Formation-Control-of-Multi-Vehicle-Systems
University of Adelaide FYP 23301 2019 s2
ZhuLinche/Event-triggered-controller
Simple examples for Event-triggered control for network controlled system
ZhuLinche/quadrotor
Quadrotor control, path planning and trajectory optimization
ZhuLinche/robotarium-rendezvous-RSSDOA
This repository contains the Matlab source codes (to use in Robotarium platform) of various rendezvous controllers for consensus control in a multi-agent / multi-robot system.
ZhuLinche/download
Lantern官方版本下载 蓝灯 翻墙 代理 科学上网 外网 加速器 梯子 路由 proxy vpn circumvention gfw
ZhuLinche/NeuroDynamicProgramming-MFD
Source code of the Neuro-dynamic programming approach for optimal control of Macroscopic fundamental diagram (MFD) system)
ZhuLinche/multiagent_planning
Implementation of several multiagent trajectory generation algorithms
ZhuLinche/Robot
ZhuLinche/mrca-mav
Collision avoidance for mavs in dynamic environments using model predictive control
ZhuLinche/OpenMAS-1
OpenMAS is an open source multi-agent simulator based in Matlab for the simulation of decentralized intelligent systems defined by arbitrary behaviours and dynamics.
ZhuLinche/Control-algorithm-for-quadcopter
ZhuLinche/AFISMC
a new observer-based adaptive fuzzy integral sliding mode controller (AFISMC) is proposed based on the Lyapunov stability theorem. The plant under study is subjected to a square-integrable disturbance and is assumed to have mismatch uncertainties both in state- and input-matrices. In addition, a norm-bounded time varying term is introduced to address the possible existence of un-modelled/nonlinear dynamics. Based on the classical sliding mode controller (SMC), the equivalent control effort is obtained to satisfy the sufficient requirement of SMC and then the control law is modified to guarantee the reachability of the system trajectory to the sliding manifold. The sliding surface is compensated based on the observed states in the form of linear matrix inequality (LMI). In order to relax the norm-bounded constrains on the control law and solve the chattering problem of SMC, a fuzzy logic (FL) inference mechanism is combined with the controller. An adaptive law is then introduced to tune the parameters of the fuzzy system on-line. Finally, by aiming at evaluating the validity of the controller and the robust performance of the closed-loop system, the proposed regulator is implemented on a real-time mechanical vibrating system.
ZhuLinche/Disturbance_observer
In this note, disturbance rejection control (DRC) based on unknown input observation (UIO), and disturbance-observer based control (DOBC) methods are revisited for a class of MIMO systems with mismatch disturbance conditions. In both of these methods, the estimated disturbance is considered to be in the feedback channel. The disturbance term could represent either unknown mismatched signals penetrating the states, or unknown dynamics not captured in the modeling process, or physical parameter variations not accounted for in the mathematical model of the plant. Unlike the high-gain approaches and variable structure methods, a systematic synthesis of the state/disturbance observer-based controller is carried out. For this purpose, first, using a series of singular value decompositions, the linearized plant is transformed into disturbance-free and disturbance-dependent subsystems. Then, functional state reconstruction based on generalized detectability concept is proposed for the disturbance-free part. Then, a DRC based on quadratic stability theorem is employed to guarantee the performance of the closed-loop system. An important contribution offered in this article is the independence of the estimated disturbance from the control input which seem to be missing in the literature for disturbance decoupling problems. In the second method, DOBC is reconsidered with the aim of achieving a high level of robustness against modeling uncertainties and matched/mismatched disturbances, while at the same time retaining performance. Accordingly, unlike the first method, DRC, full information state observation is developed independent of the disturbance estimation. An advantage of such a combination is that disturbance estimation does not involve output derivatives. Finally, the case of systems with matched disturbances is presented as a corollary of the main results.
ZhuLinche/AFTC_Attitude_Control
Code for active fault-tolerant control design from TIE19 paper
ZhuLinche/distr_formation_control
Robust 3D Distributed Formation Control with Collision Avoidance for Multirotor Aerial Vehicles.
ZhuLinche/IROS2020_LearningBarriers
Synthesis of Control Barrier Functions Using a Supervised Machine Learning Approach
ZhuLinche/Formation-Control-Simulation-matlab
Simulation of several Formation Control algorithm
ZhuLinche/Dynamic-Modelling-Simulation-and-Control-of-Asymmetrical-VTOL-Multi-Rotor-UAVs
Master's Thesis Project: Design, Development, Modelling and Simulating of a Y6 Multi-Rotor UAV, Imlementing Control Schemes such as Proportional Integral Derivative Control, Linear Quadratic Gaussian Control and Model Predictive Control on a BeagleBone Blue
ZhuLinche/SDRE-Path-Following
ZhuLinche/self-triggered-control
ZhuLinche/Attitude-Optimal-Backstepping-Controller-Based-Quaternion-for-a-UAV
Implementation of Attitude Optimal Backstepping Controller for UAV
ZhuLinche/Robust-Attitude-Controller-for-UAV-Using-Dynamic-Inversion-and-Extended-State-Observer-controller
A robust feedback linearization controller is presented for attitude control of an unmanned aerial vehicle (UAV). The objective of this controller is to make the roll angle, pitch angle, and yaw angle track the given trajectories(commands) respectively. This design is developed using dynamic inversion and extended state observer (ESO). Firstly, dynamic inversion is used to linearize and decouple UAV attitude system into three single-input-single-output (SISO) systems, then three proportional-derivative (PD) controllers are designed for these linearized systems. Extended state observers are used to estimate and compensate unmodeled dynamics and extent disturbances. Simulation results show that the proposed controller is effective and robust.
ZhuLinche/Model-Predictive-Control-Lecture
Model Predictive Control Lecture (151-0660-00) Spring 2019 Programming Exercise