Klementyna Szwaykowska

Task Scheduling for Control Oriented Requirements for Cyber-Physical Systems

The wide applications of cyber-physical systems (CPS) call for effective design strategies that optimize the performance of both computing units and physical plants.We study the task scheduling problem for a class of CPS whose behaviors are regulated by feedback control laws. We co-design the control law and the task scheduling algorithm for predictable performance and power consumption for both the computing and the physical systems. We use a typical example, multiple inverted pendulums controlled by one processor, to illustrate our method.

Trend and Bounds for Error Growth in Controlled Lagrangian Particle Tracking

This paper establishes the method of controlled Lagrangian particle tracking (CLPT) to analyze the offsets between physical positions of marine robots in the ocean and simulated positions of controlled particles in an ocean model. The offset, which we term the CLPT error, demonstrates distinguished characteristics not previously seen in drifters and floats that cannot be actively controlled. The CLPT error growth over time is exponential until it reaches a turning point that only depends on the resolution of the ocean model. After this turning point, the error growth slows down significantly to polynomial functions of time. In the ideal case, a theoretical upper threshold on exponential growth of CLPT error can be derived. These characteristics are proved theoretically, verified via simulation, and justified with ocean experimental data. The method of CLPT may be applied to improve the accuracy of ocean circulation models and the performance of navigation algorithms for marine robots.

A lower bound on navigation error for marine robots guided by ocean circulation models

This paper establishes the method of controlled Lagrangian particle tracking (CLPT) to analyse the offsets between physical positions of marine robots in the ocean and simulated positions of controlled particles in an ocean model. This offset (which we term CLPT error) has characteristics that are not previously seen in free-drifting ocean sampling platforms with no active control. CLPT error growth over time is exponential until it reaches a turning point that depends only on the resolution of the ocean model, after which the error growth is bounded by polynomial functions of time. In the ideal case, a theoretical lower bound on the steady-state CLPT error can be derived. These characteristics are proved theoretically for particles moving in a planar flow field. The method of CLPT may be applied to improve the accuracy of ocean circulation models and navigation performance of marine robots.

Collective Motions of Heterogeneous Swarms

The emerging collective motions of swarms of interacting agents are a subject of great interest in application areas ranging from biology to physics and robotics. In this paper, we conduct a careful analysis of the collective dynamics of a swarm of self-propelled heterogeneous, delay-coupled agents. We show the emergence of collective motion patterns and segregation of populations of agents with different dynamic properties; both of these behaviors (pattern formation and segregation) emerge naturally in our model, which is based on self-propulsion and attractive pairwise interactions between agents. We derive the bifurcation structure for emergence of different swarming behaviors in the mean field as a function of physical parameters and verify these results through simulation.

Controlled Lagrangian particle tracking error under biased flow prediction

In this paper we model the controlled Lagrangian particle tracking (CLPT) error for marine vehicles moving in an ocean flow field, with guidance from ocean models. We linearize the error about the nominal modeled trajectory of the system and derive an exact expression for the linearized error in the case of constant modeled ocean flow. We show that this simple error model can be used to estimate error in predicted positions of autonomous vehicles, using data from a field deployment of autonomous underwater gliders in Long Bay, SC, in winter 2012.

A lower bound for controlled Lagrangian particle tracking error

Autonomous underwater vehicles are flexible mobile platforms for ocean sampling and surveillance missions. However, navigation of these vehicles in unstructured, highly variable ocean environments poses a significant challenge. Model-based prediction of vehicle position may be used to improve navigation capability, but prediction error exists due to limited resolution and accuracy of flow values obtained from ocean models that calculate flow velocity at discrete grid points. We present a theoretical lower bound on the steady-state error in position prediction for underwater vehicles using ocean model flow data and show that it is determined by the gridsize used by the ocean models. Our conclusions are justified by simulation and data collected during an ocean experiment.

Patterned dynamics of delay-coupled swarms with random communication graphs

Swarm and modular robotics are an emerging area in control of autonomous systems. However, coordinating a large group of interacting autonomous agents requires careful consideration of the logistical issues involved. In particular, inter-agent communication generally involves time delay, and bandwidth restrictions limit the number of neighbors with which each agent in the swarm can communicate. In this paper, we analyze coherent pattern dynamics of groups of delay-coupled agents, where the communication network is an Erdös-Renyi graph. We show that overall motion patterns for a globally-coupled swarm persist under decreasing network connectivity, and derive the bifurcation structure scaling relations for the emergence of different swarming behaviors as a function of the average network degree. We show excellent agreement between the theoretical scaling results and numerical simulations.

Tracking performance under time delay and asynchronicity in distributed camera systems

Distributed camera systems are typically used to simultaneously track multiple targets. Communication between cameras enables the ability to monitor targets in a complex environment where occlusion happens. Asynchronicity and time delay are among the most important factors that affect tracking error. We provide a feedback law controlling the pan and tilt of each camera to track the centroid of a cluster of point targets in the field of view. Assuming that the estimates for target motion are available only infrequently and asynchronously when occlusion happens, we compute a worst-case lower bound on the frequency for exchanging estimates between cameras. Our results may help camera system designers to determine the response time and tracking ability for distributed camera systems.

Controlled Lagrangian Particle Tracking: Error Growth Under Feedback Control

The method of Lagrangian particle tracking (LPT) is extended to autonomous underwater vehicles (AUVs) that are modeled as controlled particles. Controlled LPT (CLPT) evaluates the performance of ocean models used for the navigation of AUVs by computing the differences between predicted vehicle trajectories and actual vehicle trajectories. Such difference, measured by the controlled Lagrangian prediction error (CLPE), demonstrates growth rate that is influenced by the accuracy of the ocean model and the strength of the feedback control laws used for vehicle navigation. Despite the limited accuracy and resolution of the ocean model, the error growth can be bounded by feedback control when localization service is available, which is a unique property of CLPT. Theoretical relationship among CLPE growth rate, quality of ocean models, and feedback control are established for two control strategies: a transect-following controller and a station-keeping controller that are often used by AUVs. Upper bounds for error growth are derived and verified by both simulation and experimental data collected during the operations of underwater gliders in coastal ocean.