60 research outputs found
Benelux meeting on systems and control, 23rd, March 17-19, 2004, Helvoirt, The Netherlands
Book of abstract
Simultaneous Prediction and Planning in Crowds using Learnt Models of Social Response
The ability of autonomous mobile robots to work alongside humans and animals in real world environments has the potential to revolutionise the way in which many routine and labour intensive tasks are completed. Whilst we are seeing increasing applications in controlled environments, such as traffic and warehousing, robots are still far from ubiquitous in everyday life.
In unstructured environments, such as agriculture or pedestrian crowds, where interactions between agents are not guided by infrastructure, there exist additional challenges that need to be overcome before we are likely to see the widespread adoption of mobile robots.
Safe navigation in shared environments requires the accurate perception of nearby individuals using a robot's on board sensors. Additionally, the future motion of detected individuals needs to be predicted both for collision avoidance and efficient navigation. These predictions should reflect the inherent uncertainty of the individual's future, including the ways in which an individual might respond to its neighbours, including the robot itself. As such, there exists a dependency between any prediction of an individual's motion and the planned path of the robot, which needs to be accounted for both during the prediction and planning stages of navigation.
This thesis focuses on how prediction and planning can be approached in a single framework to address this dependency, using learnt models of social response within a sampling based path planner for simultaneous prediction and planning (SPP). Additional challenges faced in navigating shared and unstructured environments are also addressed, including predicting the uncertain branching and multi-modal nature of agent motion during social interactions, and overcoming the on-board limitations of mobile robots --- such as resource and sensing constraints --- in order to achieve extended autonomy
Deployable Vibration Control Systems for Lightweight Structures
The recent push towards lightweight, efficient, and innovative structural designs has brought forth a range of vibration control issues related to implementation, effectiveness, and control system design that are not fully addressed by existing strategies. In many cases, these structures are capable of withstanding day-to-day loads and only experience excessive vibrations during predictable peak-loading events such as large crowds or wind storms. At the same time, the use of lightweight material coupled with innovative construction methods has given rise to temporary structures which are designed to facilitate rapid implementation and intended for short-term applications. Both scenarios point towards a vibration control system that is suitable for immediate, short-term applications which motivates the concept of deployable autonomous control systems (DACSs).
The deployability aspect implies the control system is capable of being readily implemented on a range of structures with only minor customization to the structure or device while the autonomy aspect refers to the ability of the system to react to changes in the dynamic response and effectively control different structural modes of vibration. A prototype device, consisting of an electromagnetic mass damper (EMD) mounted on an unmanned ground vehicle (UGV) equipped with vision sensors and on-board computational hardware, is developed to study the vibration control performance and demonstrate the advantages of the DACS concept. Both numerical and experimental modelling techniques are used to identify system models for each component of the prototype device. Given the system models, the dynamic interaction between the device and underlying structure is derived theoretically and validated experimentally.
The use of an EMD and UGV introduce a number of practical challenges associated with controller design. These challenges arise due to the presence of physical operating constraints as well as uncertainty in the controller model. Three different candidate controllers, based on linear-quadratic Gaussian (LQG), model-predictive control (MPC), and robust H-infinity control theory, are formulated for the prototype device and comparatively assessed with respect to their ability
to address these challenges. The MPC framework provides a systematic approach to incorporate physical operating constraints directly in the control formulation while robust synthesis of an H-infinity controller is well suited for addressing uncertainty in both the controller and structure models.
A key property of the prototype device is the ability to reposition itself at different locations on the structure. To study the impact of this mobility on the overall control performance, a simultaneous localization and mapping (SLAM) solution is implemented for bridge structures. The SLAM solution generates a map of the structure that can later be used for autonomous navigation of the prototype device. In achieving autonomous mobility, the location of the control force can be added as an additional parameter in the controller formulation.
The overall performance of the prototype device is evaluated through a combination of numerical simulations and experimental studies. Real-time hybrid simulation (RTHS) is used extensively to study the dynamic interaction effects and evaluate the control performance of the prototype device on various structures. A full-scale modular aluminum pedestrian bridge is used to demonstrate autonomous navigation and assess the advantages of a mobile control device. The results from each study point towards DACSs as being a favourable alternative to existing control systems for immediate, short-term vibration control applications
High performance implementation of MPC schemes for fast systems
In recent years, the number of applications of model predictive control (MPC) is rapidly
increasing due to the better control performance that it provides in comparison to
traditional control methods. However, the main limitation of MPC is the computational
e ort required for the online solution of an optimization problem. This shortcoming
restricts the use of MPC for real-time control of dynamic systems with high sampling
rates. This thesis aims to overcome this limitation by implementing high-performance
MPC solvers for real-time control of fast systems. Hence, one of the objectives of this
work is to take the advantage of the particular mathematical structures that MPC
schemes exhibit and use parallel computing to improve the computational e ciency.
Firstly, this thesis focuses on implementing e cient parallel solvers for linear MPC
(LMPC) problems, which are described by block-structured quadratic programming
(QP) problems. Speci cally, three parallel solvers are implemented: a primal-dual
interior-point method with Schur-complement decomposition, a quasi-Newton method
for solving the dual problem, and the operator splitting method based on the alternating
direction method of multipliers (ADMM). The implementation of all these solvers is
based on C++. The software package Eigen is used to implement the linear algebra
operations. The Open Message Passing Interface (Open MPI) library is used for the
communication between processors. Four case-studies are presented to demonstrate the
potential of the implementation. Hence, the implemented solvers have shown high
performance for tackling large-scale LMPC problems by providing the solutions in
computation times below milliseconds.
Secondly, the thesis addresses the solution of nonlinear MPC (NMPC) problems, which
are described by general optimal control problems (OCPs). More precisely,
implementations are done for the combined multiple-shooting and collocation (CMSC)
method using a parallelization scheme. The CMSC method transforms the OCP into a
nonlinear optimization problem (NLP) and de nes a set of underlying sub-problems for
computing the sensitivities and discretized state values within the NLP solver. These
underlying sub-problems are decoupled on the variables and thus, are solved in parallel.
For the implementation, the software package IPOPT is used to solve the resulting NLP
problems. The parallel solution of the sub-problems is performed based on MPI and
Eigen. The computational performance of the parallel CMSC solver is tested using case
studies for both OCPs and NMPC showing very promising results.
Finally, applications to autonomous navigation for the SUMMIT robot are presented.
Specially, reference tracking and obstacle avoidance problems are addressed using an
NMPC approach. Both simulation and experimental results are presented and compared
to a previous work on the SUMMIT, showing a much better computational e ciency
and control performance.Tesi
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