Tilt-rotor Tailsitter Global Acceleration Control: Behavioural Cloning of a Nonlinear Model Predictive Controller

Capable of both vertical take-off and landing and forward flight, tail-sitters are a versatile class of UAVs with a large range of potential applications. A variant of tailsitters using tilt-rotors instead of ailerons for pitch and roll control has been proposed to mitigate the reduced control authority at low to zero velocities. The control of...

Integrated Torque Vectoring and Path Following using Nonlinear Model Predictive Control

This thesis introduces a novel model predictive controller (MPC) that integrates both torque vectoring and path following into one controller. Due to a need to improve vehicle safety, systems are being developed in order to improve vehicle handling. One system that is able to improve the vehicle handling is torque vectoring (TV). With torque...

Nonlinear Incremental Optimal Control for Underactuated Mechanical Systems: Robust Tightly-Coupled NMPC and INDI applied to Underactuated Mechanical Systems

Underactuated mechanical systems (UMS) feature prominently in robotics and aerospace, with aircraft, unmanned air vehicles, and aeroelastic wings as prime examples. These systems present multifaceted control challenges, ranging from inherent underactuation and stability concerns to state and control saturation and an overarching need for...

Autonomous greenhouse climate control with Q-learning using ENMPC as a function approximator

Greenhouses allow production of crops that would otherwise be impossible. Permitting more local, fresher and nutrient richer crop production. Eorts are taken to minimize societal harm due to energy and resource consumption by greenhouse production systems. One way to control such systems is by using model predictive control. Optimal crop yield...

Game-Theoretic Intention-Aware Planning for Autonomous Vehicles

As Autonomous Vehicles (AVs) navigate through dynamic and constantly changing environments, it is crucial that they take into account the impact of their actions on the decisions of others for safe and efficient interaction with humans. In doing so, they need to anticipate how humans will behave in different situations based on their intentions....

A real-time optimal control approach to autonomous drifting

Full vehicle automation requires complete control over all driving scenarios that can be encountered on roads in order to ensure passenger safety at all times. This extends to dangerous situations such as losing control on slippery surfaces, commonly known as drifting. This work aims to make a step toward ensuring passenger safety in these...

Nonlinear model predictive control for improving range-based relative localization by maximizing observability

Wireless ranging measurements have been proposed for enabling multiple Micro Air Vehicles (MAVs) to localize with respect to each other. However, the high-dimensional relative states are weakly observable due to the scalar distance measurement. Hence, the MAVs have degraded relative localization and control performance under unobservable...

Nonlinear model predictive control of salinity and water level in polder networks: Case study of Lissertocht catchment

A significant increase in surface water salinization in low-lying deltas is expected globally due to saline groundwater exfiltration driven by rising sea levels and decreasing freshwater availability. Sustaining fresh water-dependent agriculture in such areas will entail an increased demand for fresh water flushing. Unfortunately, the...

Visual Navigation and Optimal Control for Autonomous Drone Racing

Drones, especially quadrotors, have shown their great value for applications like aerial photography, object delivery and warehouse inspection. At the same time, with the de- velopment of Artificial Intelligence (AI), computers can replace humans and even per- form better than humans in some areas where it was impossible before like the AI pro-...

Automatic Docking for Underactuated Ships Based on Multi-Objective Nonlinear Model Predictive Control

Autonomous shipping refers to the ability of a ship to independently control its own actions while transporting cargo from one port to another, which places higher requirements on ship motion control methods. When a ship enters a port, it is important to ensure that the ship sails from the fairway area to the assigned position at the berth...

Safer reinforcement learning for robotics

Reinforcement learning is an active research area in the fields of artificial intelligence and machine learning, with applications in control. The most important feature of reinforcement learning is its ability to learn without prior knowledge about the system. However, in the real world, reinforcement learning actions may lead to serious damage...

Optimal trajectory tracking control design: Emulating fighter aircraft flight test tracks with nonlinear model predictive control

Aircraft lifespan can be extended by upgrading and modernizing the electrical subsystems and instruments. However, this often introduces increased power demands and heat generation that the aircraft was not originally designed for and can result in a reduced flight performance and increased wear. Netherlands Aerospace Centre (NLR) and the...

Combining multi-level real-time iterations of Nonlinear Model Predictive Control to realize squatting motions on Leo

Today’s humanoid robots are complex mechanical systems with many degrees of freedom that are built to achieve locomotion skills comparable to humans. In order to synthesize whole-body motions, real-tme capable direct methods of optimal control are a subject of contemporary research. To this end, Nonlinear Model Predictive Control is the method...

A projected gradient and constraint linearization method for nonlinear model predictive control

Projected gradient descent denotes a class of iterative methods for solving optimization programs. In convex optimization, its computational complexity is relatively low whenever the projection onto the feasible set is relatively easy to compute. On the other hand, when the problem is nonconvex, e.g., because of nonlinear equality constraints...

Benchmarking model-free and model-based optimal control

Model-free reinforcement learning and nonlinear model predictive control are two different approaches for controlling a dynamic system in an optimal way according to a prescribed cost function. Reinforcement learning acquires a control policy through exploratory interaction with the system, while nonlinear model predictive control exploits an...

Adjoint-based model predictive control of wind farms: Beyond the quasi steady-state power maximization

In this paper, we extend our closed-loop optimal control framework for wind farms to minimize wake-induced power losses. We develop an adjoint-based model predictive controller which employs a medium-fidelity 2D dynamic wind farm model. The wind turbine axial induction factors are considered here as the control inputs to influence the overall...