Dr Yuanbo Nie
Department of Automatic Control and Systems Engineering
Lecturer in Control and Systems Engineering
Full contact details
Department of Automatic Control and Systems Engineering
Amy Johnson Building
Dr. Yuanbo Nie is a Lecturer in the Department of Automatic Control and Systems Engineering, University of Sheffield. He received an MSc degree in Aerospace Engineering from the Delft University of Technology, and MSc degree in Advanced Computational Methods from Imperial College London, and a Ph.D. degree in Aeronautics from Imperial College London (Thesis: Numerical Optimal Control with Applications in Aerospace) in 2021.
Between 2012 and 2013, he was with the Institute of System Dynamics and Control, German Aerospace Center (DLR), and between 2021 and 2022, he was a post-doctoral research associate with the Rolls-Royce Control, Monitoring and Systems Engineering University Technology Centre.
- Research interests
Numerical Methods for Dynamic Optimization
Dynamic optimization is integral to many aspects of science and engineering, commonly found in trajectory optimization, optimal control, state estimation, system identification and design synthesis problems. A key characteristic of dynamic optimization problems (DOPs) is that the decision variables can be functions or trajectories, leading to infinite-dimensional optimization problems that are often more challenging to solve.
My current focus is on the development of a type of direct transcription method named the integrated residual methods. This is an excellent starting point to develop new DOP solution methods and next-generation software toolboxes. The advancements would allow DOPs to be formulated intuitively based on the problems' mission specifications and successfully solved thereafter, making the method easily accessible for scientists and engineers.
Optimization-based control explores the use of optimization algorithms for feedback control of dynamical systems. For example, model predictive control (MPC) is a widely used optimization-based control method, allowing systematic and optimal handling of constraints, nonlinearities and uncertainties.
The area I am particularly interested in is the design of optimization-based control with the optimization problem formulated directly based on the original problem specifications. Although such problems are typically more difficult to solve numerically, the difficulties are often offset by the availability of guarantees in solution properties, so that any local optimum solution (to a certain extent, even any feasible solution) can be considered suitable for real-world implementation.
Control and Simulation of Aerospace Systems
I have a strong interest in the control and simulation of aerospace systems, particularly when unconventional and counterintuitive solutions are needed. My current focuses are on
- Development of tool-chains that can be integrated into the aircraft's daily operations (e.g. as next-generation flight management systems), where optimal flight trajectories can be automatically obtained based on the information regarding aircraft aerodynamics, propulsion, departure and arrival airport, atmospheric conditions and any relevant air traffic control restrictions,
- Optimal energy management for electric, hydrogen and hybrid aircraft concepts,
- Multi-disciplinary optimal design of aerospace vehicles and flight control systems, for example, regarding the optimal sizing and placement of flight control surfaces, and the integration of distributed propulsion systems in flight control designs,
- Guidance and automatic control for the safe recovery of airliners in extreme conditions known as upset, such as stall and spin,
- Next-generation flight simulator concepts, e.g. ones that are suitable for upset recovery training
- Solving Dynamic Optimization Problems to a Specified Accuracy: An Alternating Approach using Integrated Residuals. IEEE Transactions on Automatic Control, 1-1.
- Efficient Implementation of Rate Constraints for Nonlinear Optimal Control. IEEE Transactions on Automatic Control, 66(1), 329-334.
- Efficient and More Accurate Representation of Solution Trajectories in Numerical Optimal Control. IEEE Control Systems Letters, 4(1), 61-66.
- External Constraint Handling for Solving Optimal Control Problems With Simultaneous Approaches and Interior Point Methods. IEEE Control Systems Letters, 4(1), 7-12.
Conference proceedings papers
- Fast and accurate method for computing non-smooth solutions to constrained control problems. 2022 European Control Conference (ECC), 12 July 2022 - 15 July 2022.
- Towards a Framework for Nonlinear Predictive Control using Derivative-Free Optimization. IFAC papers online, Vol. 54(6)
- Direct Transcription for Dynamic Optimization: A Tutorial with a Case Study on Dual-Patient Ventilation During the COVID-19 Pandemic. 2020 59th IEEE Conference on Decision and Control (CDC), 14 December 2020 - 18 December 2020.
- How Should Rate Constraints be Implemented in Nonlinear Optimal Control Solvers?. IFAC papers online, Vol. 51(20)
- Energy-efficient communication in mobile aerial relay-assisted networks using predictive control. IFAC-PapersOnLine, Vol. 20
- Capturing Discontinuities in Optimal Control Problems. 2018 UKACC 12th International Conference on Control (CONTROL), 5 September 2018 - 7 September 2018.
- Efficient Implementation of Rate Constraints for Nonlinear Optimal Control. 2018 UKACC 12th International Conference on Control (CONTROL), 5 September 2018 - 7 September 2018.
- ICLOCS2: Try this Optimal Control Problem Solver Before you Try the Rest. 2018 UKACC 12th International Conference on Control (CONTROL), 5 September 2018 - 7 September 2018.
- Aircraft Upset and Recovery Simulation with the DLR Robot Motion Simulator. AIAA Modeling and Simulation Technologies Conference
- Geometry Based Quick Aircraft Modeling Method for Upset Recovery Applications. AIAA Modeling and Simulation Technologies Conference
Theses / Dissertations
- Teaching activities
ACS6124 Multisensor and Decision Systems