Description about the workshop
This workshop provides comprehensive, hands-on experience designing a nonlinear model predictive controller (NLMPC) with a neural state-space prediction model for a free-flying robot. Throughout the workshop, participants use MATLAB to implement the control strategy. The first step is to identify the neural state-space model by loading a dataset of state and input trajectories and training a multi-layer perceptron (MLP) network on this data. Once the neural state-space model is identified, participants estimate the unmeasured states of the flying robot model using an extended Kalman filter. Next, participants plan optimal state and input trajectories using a multistage NLMPC that employs the previously identified neural state-space model as its predictor. Finally, participants simulate the NLMPC in closed loop with the flying robot model and plot the robot’s actual state trajectories and inputs against the optimal ones to confirm successful tracking.
Profile of the instructors

Ms. Sragdhara is an online content developer at MathWorks focused on developing Simulink-based learning content. She holds a PhD degree in Control Systems. Her research areas include robust control, multirate control, cooperative control of multi-agent systems, decentralized control, and control of cyber-physical systems. She has published papers in international journals and conferences in the domain of her research. She is also an AI enthusiast and is currently exploring the applications of machine learning and deep learning in the areas of signal and image processing, and developing course content.

Ms. Priyanka is an online content developer at MathWorks focused on learning content related to physical modeling. She holds a master’s degree in Power Systems and is pursuing her part-time PhD in Electrical Engineering. Her research areas are the implementation of Machine Learning and Deep Learning techniques in Wind Power Systems for Power System Stability, Fault Detection, and Fault Classification. She has published papers in international journals and conferences on Deep Learning techniques for Wind Power Prediction and Fault Classification.
Certification Criteria
Attending the workshop is mandatory for certification.
Reviews
There are no reviews yet.