Machine learning-based (data-driven) MPC

  1. Reinforcement control for power-to-X systems
  2. Deep neural network-based NMPC/LMPC/HMPC
  3. Differentiable DNN-MPC
  4. Constraints satisfaction and stability of ML-MPC

Perimeter Control of Urban Traffic Networks 

  1. PI controller design for a single reservoir city with a freeway
  2. Analysis of side-effects of perimeter control 
  3. Control-oriented model development 
  4. MPC for multi-reservoir city 
  5. Non-linear MPC for the control emission in the traffic network

Explicit Model Predictive Control

  1. Unum-based explicit MPC – MATLAB Code
  2. Posit-based explicit MPC
  3. FPGA implementation of unum and posit-based explicit MPC
  4. PLC implementation of low-memory explicit MPC
  5. Applications: anesthesia, fuel cell, electric drives

Universal Number Format

  1. MATLAB prototype for unum arithmetic – MATLAB Code
  2. Hybrid MPC using unums 
  3. Implementation first-order optimization method using unums
  4. Applications: anesthesia, fuel cell, electric drives

Modeling and Simulation

  1. Microscopic fundamental diagram for traffic 
  2. Three phase induction motor
  3. Pharmacokinetic & Pharmacodynamic model of a patient
  4. Drum-boiler
  5. Heat exchanger
  6. Spray dryer
  7. Fuel cell

MATLAB Implementation

  1. Matrix decomposition algorithms
  2. Linear Solver methods
  3. Quadratic Programming (QP)  problem solving methods
  4. Proportional-Integral-Derivative (PID) control
  5. Model Predictive Control (MPC)

FPGA Implementation

  1. Implementation of universal number format
  2. IEEE 754 Floating Point (FP) & Logarithmic Number System (LNS)
  3. Addition, subtraction, multiplication using single precision FP
  4. Division, square root & power of a number using LNS
  5. Matrix-Matrix addition, subtraction, multiplication & division
  6. Matrix-Vector multiplication
  7. Matrix decomposition algorithms
  8. Linear Solver methods
  9. Quadratic Programming Problem (QP)  solving methods

Anesthesia  Control

  1. Pharmacokinetic & Pharmacodynamic model of a patient
  2. Linear Model Predictive Control (LMPC)
  3. Testing LMPC for different patients in simulation
  4. Comparison of PID and LMPC

Model Predictive Control

  1. Power-to-X system
  2. DC, PMSM, BLDC motor control
  3. Boiler, heat exchanger, distillation, evaporator, cooling tower processes
  4. Traffic control
  5. Type-3 diabetes control
  6. Anesthesia control