Models and training. Static/dynamic models, based on data, rules, linear/nonlinear, multivariable models. Hypothesis, training, validation.
[SSM, ARIMA, NARX, ANN, CNN, RNN, SVM, DT, RF, K-M, KNN Bayes, Graphs, FEM, ANFIS, PyTorch, TensorFlow, ONNX]
State Generation. Anomaly detection, classification of operation modes.
[SPC, MDP, Petri-Nets, Automatas, Finite-State, Event-Driven, FIS]
Simulation and Estimation. Generation of predictive dynamics and real-time estimation of process variables.
[Observation, Kalman Filter, MHE, EKF]
Digital Twins. Digital replicas of real-life processes for optimization in sandbox environments. Reward system.
[BIM, Matlab, Software]
Module 6: Control and numerical optimization
Control systems. Automatic control of variables in centralized or multi-agent settings.
[PLC, DCS, SCADA, PID, LQR, MPC, FLC, DCOP, PO-MDP, MAPF, MAS, RL]
Optimization. Numerical techniques for maximization of efficiency, minimization of cost.
[LP, QP, MILNP, SDP, KKT, CPLEX, CVX, Gurobi, GA, Q-Learning]
Automatic Planning. Path finding and execution in real-time-series.
[SQP, A*, M*, DPOP, MGM-2]