We study the distributed model predictive control (DMPC) problem for a group of linear discrete-time systems with both local constraints and global constraints in the presence of stochastic ...

For patients with stroke, hemiplegia and ankle injuries, ankle rehabilitation robots can provide personalized rehabilitation treatment programs through quantitative evaluation and precise control. In ...

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 ...

Process optimization can be achieved by implementing optimization algorithms on the overall integrated model. Figure 1 shows a flowsheet model (simulated in gPROMS, Process Systems Enterprise) of a ...

Trajectory Optimization (TO) and Model Predictive Control (MPC) are model-based optimization approaches, built upon Optimal Control theory, which are becoming increasingly popular in robotics. They ...

Keywords: phasor measurement unit, model predictive control, voltage dynamic optimization, voltage sensitivity matrix, distributed energy. Citation: Liu S, Zhang L, Wu Z, Zhao J and Li L (2022) ...

This repository is motion planning of autonomous driving using Model Predictive Control (MPC) based on CommonRoad Framework. We develop the algorithm with two tools, i.e., CasADi (IPOPT solver) and ...

Model Predictive Control (MPC) is a widely used optimization-based control strategy for constrained systems. MPC relies on the repeated online solution of an optimal control problem, which determines ...

Choosing right algorithm for predictive modeling, boils down to - Understanding the trade-off between Model performance vs Model interpretability. Define Goals - To balance the above trade-off, it ...

The control of batch crystallizers is an intensively investigated topic as suitable crystallizer operation can reduce considerably the downstream operation costs and produce crystals of desired ...