wuyou33,github

tristan

Time-series-based methods to assess power system (static and dynamic) stability margins.

trnm

Efficient implementation of truncated reweighting low-rank matrix approximation, 2019

tro_cbfs-ltl_robotarium_experiment

Control Of Mobile Robotics Using Barrier Functions And Temporal Logic

tsdr

MATLAB code to simulate model predictive control of thermal storage for demand response.

tse2018-sddp

Model for paper on IEEE transactions on sustainable energy: Application of Stochastic Dual Dynamic Programming to the Real-Time Dispatch of Storage under Renewable Supply Uncertainty

tsg-p2p-pricing

Public Repository for TSG submission of paper: Pricing and energy trading in peer-to-peer zero marginal-cost microgrids

tsst-glgd

Second-order time-reassigned transform based on Gaussian-modulated linear group delay model

tuchong-spider

A spider for TuChong pictures: Collecting pictures of a specified author

tum_ardrone

Repository for the tum_ardrone ROS package, implementing autonomous flight with PTAM-based visual navigation for the Parrot AR.Drone.

tunempc

Economic tuning of tracking (N)MPC problems.

two-stage-stochastic-optimization

Minimizing costs in reservoir storage systems has been a challenging problem over the years. Several methods have been used previously to solve this problem. This paper would be considering the Two-Stage Stochastic Programming technique in solving this problem but the objective function would be a quadratic function. This function is the squared difference between release and the demand. Risk is also considered in solving this problem and the results are benchmarked against that of the Fletcher-Ponnambalam method. The results are also compared to that of the deterministic solution and the case of perfect information. The Two-Stage Stochastic Programming applied to a quadratic objective function gave promising results. As the number of scenarios increased, the optimal value approached that of the FP method. The stochastic solution from the TSP was shown to give 16.8% improvement in costs compared to the deterministic solution and the results of EVPI showed that getting perfect information for our problem would be highly beneficial leading to an 86.7% reduction in cost over time.