Vehicle control and simulation - Centre for Systems and Control

Multi-Agent, Multi Resolution Vehicle Simulation

Platform Survivability

Biomimetic Propulsion Systems for Autonomous Underwater Vehicles

Robot Swarms for Urban Search and Rescue

Nonlinear Sightline Control

Multi-Platform Guidance, Navigation & Control

Guidance and Control UAV Swarm

Control Design & Analysis for Autonomous Mobile Agents

Evolutionary Optimisation for Controller Design

Complex Embedded Automotive Control Systems

Air Traffic Management

Multi-Agent, Multi Resolution Vehicle Simulation

IInvestigators: David Anderson
A multi resolution simulation is one where different fidelity physical models are selected by the simulation engine based upon the operational environment modeled. Proper synchronization of the various vehicles modeled by the engine is often a difficult task as is the selection of the appropriate fidelity when different objects in the simulation interact. In particular, a process known as chain disaggregation results in the simulation moving to the highest-fidelity setting. Modeling each object as an agent as using negotiation protocols removes this problem, settling to a Nash equilibrium solution. Development and analysis of this technique are the basis of this project.

Platform Survivability

Investigators: David Anderson
The survivability of an aircraft depends on the situational awareness provided by automatic systems (and of course pilots and crew),  the agility of the aircraft and the effectiveness of any countermeasures systems available.  Situational awareness is achieved through complex sensor arrays that can detect, track and identify incoming threats using advanced image and signal processing algorithms.  Aircraft agility is maximized for a given flight condition by applying nonlinear control laws derived via inverse simulation and predictive control methods. The purpose of this research is to fuse sightline control and inverse control to achieve improved survivability,

Biomimetic Propulsion Systems for Autonomous Underwater Vehicles

Investigators: Euan McGookin
Autonomous Underwater Vehicles (AUVs) are subsea robots that operate without human control in areas that are hazardous for divers. These vehicles are limited by the onboard power supply which powers all the electric systems. The most power hungry are the propulsion systems. The lifespan of the power supply can be increased by improving the efficiency of these systems. Biologically inspired mechanisms, i.e. fish tail propulsion, can be used to improve efficiency.  Two such projects that look at biomimetic propulsion systems are RoboSalmon (AUV for environmental studies) and SHARC (large scale, deep water AUV). Both involve simulation and hardware implementation

Robot Swarms for Urban Search and Rescue

Investigators: Euan McGookin
Autonomous Underwater Vehicles (AUVs) are subsea robots that operate without human control in areas that are hazardous for divers. These vehicles are limited by the onboard power supply which powers all the electric systems. The most power hungry are the propulsion systems. The lifespan of the power supply can be increased by improving the efficiency of these systems. Biologically inspired mechanisms, i.e. fish tail propulsion, can be used to improve efficiency.  Two such projects that look at biomimetic propulsion systems are RoboSalmon (AUV for environmental studies) and SHARC (large scale, deep water AUV). Both involve simulation and hardware implementation

Nonlinear Sightline Control

Investigators: David Anderson
All precision airborne electro-optic devices required to operate over a large field-ofregard need   some form of pointing and stabilisation system.  The term sightline control incorporates both tasks  and also includes the image processing algorithms necessary for accurate target tracking.
This project is an EPSRC sponsored investigation into 2 fundamental performance limiters in  sightline control – nadir control and stabilisation. Fast nonlinear predictive control is being applied to the nadir (gimbal  lock) problem and nonlinear friction identification to the stabilisation task

Multi-Platform Guidance, Navigation & Control

Investigators: David Anderson
Several investigations have been undertaken into the application of modern controller design paradigms to the problems of guidance, navigation and control of single and multiple-platform fixed, rotary and flapping wing aircraft. Typical controller synthesis approaches considered include: variable structure/sliding mode control; nonlinear predictive control; inverse predictive control; adaptive control; robust control and artificial intelligence methods. There are currently no full-time research staff working on any GNC projects directly, but many investigations occur within this theme as a consequence of work package requirements in the other funded projects.

Control Design & Analysis for Autonomous Mobile Agents

Investigators: Jongrae Kim
Mobile robot or UAV (Unmanned Air Vehicle) has been used in many purposes. To replace human by autonomous or semi-autonomous mobile agents, several issues have to be resolved. To complete a given mission such as search and tracking some ground moving targets, monitoring a certain area, autonomous returning, etc, some optimal control problems have to be solved. In general, the analytic solution is rarely available but some numerical approximations are applied. Main research topics are about design an optimal search/tracking path for mobile agents and the estimation of the location and the orientation using vision sensors.

Guidance and Control UAV Swarm

Investigators: Euan McGookin
Unmanned Air Vehicles (UAVs) are robotic aircraft that can fly autonomously without human intervention.  This level of autonomous control requires guidance techniques to coordinate the tasks performed by the UAV.  When multiple UAVs are controlled simultaneously, the flight of the entire swarm of vehicles has to be coordinated. This project looks at the control of these vehicles and the higher level of artificial intelligence associated with the coordination of multiple vehicles.  This study involves studies of advanced methods for control and heuristics for multi-vehicle manoeuvres.  This investigation involves simulation based design and hardware implementation.  

Complex Embedded Automotive Control Systems

Investigators: Henrik Gollee, Geraint Bevan, Simon O'Neill
6th Framework STREP contract 004175
The CEmACS project is a partnership between DaimlerChrysler Research, the Hamilton Institute at NUI Maynooth, Lund University, Glasgow University and SINTEF. The objective of CEmACS is to contribute to am systematic, modular, model-based approach for designing complex automotive control systems. The Specific Target Research Project is aimed at combining research into the theory of multivariable control and nonlinear observers with a selection of novel prototype automotive control applications. Control and observer designs will be evaluated using two real-life benchmark integrated chassis control design applications: (i) vehicle dynamics control for active safety (collision avoidance and roll-over protection), and
(ii) multivariable control design for ride and handling using multiple actuators (Generic Prototyping). For the evaluation prototype experimental vehicles will be provided by one of the industrial project partners. 
http://www.hamilton.ie/cemacs/

Control Design & Analysis for Autonomous Mobile Agents

Investigators: Jongrae Kim
Mobile robot or UAV (Unmanned Air Vehicle) has been used in many purposes. To replace human by autonomous or semi-autonomous mobile agents, several issues have to be resolved. To complete a given mission such as search and tracking some ground moving targets, monitoring a certain area, autonomous returning, etc, some optimal control problems have to be solved. In general, the analytic solution is rarely available but some numerical approximations are applied. Main research topics are about design an optimal search/tracking path for mobile agents and the estimation of the location and the orientation using vision sensors.

Evolutionary Optimisation for Controller Design

IInvestigators: Euan McGookin
Evolutionary Optimisation techniques are parameter optimisation design methods that are based on Darwinian “survival of the fittest” and based genetic concepts.  The parameters being optimised can represent any quantities that need to be tuned to find a solution to the chosen design problem.  In the case of controller design, specific evolutionary optimisation techniques based on Genetic Algorithms and Genetic Programming are used to provide controller solutions for specific vehicle based guidance problems.  The implementation of these techniques has provided controllers for marine and aerospace vehicles, as well as other engineering applications (e.g. servo control)

Air Traffic Management

Investigators: Colin Goodchild
Work is pursued in fundamental and applied research on air traffic management in collaboration with global manufacturers and regulators through the FP6 projects ASSTAR and ASPASIA.
http://www.asstar.org/
http://www.aspasia.aero