The precision and productivity requirements for modern manufacturing process drive precision systems to their physical limitations, such as the limitation of sensor resolution, actuator saturation, and traversing speed. Incorporating these restrictions, constrained optimization has been an active research area that can explicitly handle the physical constraints. This dissertation covers three important topics in productivity and precision improvements through constrained optimization: sub-count sensor estimation, constraint violation avoidance, and cycle-time minimization. To solve sensor quantization and measurement synchronization problem, a model-based encoder-triggered estimation method was developed addressing state continuity. By enforcing smooth estimation in important physical states such as position and velocity, this method avoids abrupt estimation change when encoder trigger occurs. Furthermore, because continuous-time open-loop model is used to predict inter-trigger behavior, the developed sub-count estimator can be applied to asynchronous systems with untimely measurement updates. By applying this sub-count estimation method, the precision system can improve tracking performance when only quantized and untimely sensor measurement is available. When improving tracking performance, the precision system is often pushed to its limitation. This often causes the precision system to violate system constraints (such as control signal saturation and excessive acceleration), which results in unexpected vibration and sacrifices the tracking performance. To avoid such kind of constraint violation, the system model can be used to predict the dynamic behavior of the plant given a known trajectory and plant model. A model predictive control based optimal feed-forward tracking method is proposed to be integrable with any feedback controller. Besides tracking performance, cycle time is also an important index for modern precision motion control. The shorter the cycle time is, the more productive the machine can be. Therefore, to reduce cycle time for precision machines, a minimal time contour tracking problem is formulated to explicitly constrained receding horizon control where the trajectory (feed) profile is determined for the specified contour under system dynamics, contour error, axial velocity, signal saturation, and monotonic feed constraints. Because minimal-time operations typically drive the system near its constraint boundary, it is vital to reject measurement noise and compensate for modeling error in real-time. To accommodate the high sampling rate controllers in precision systems, an efficient real-time quadratic programming solver is proposed with robust warm start strategy in order to handle active constraints.

Precision Motion Systems: Modeling, Control, and Applications presents basic dynamics and the control knowledge needed for the daily challenges of researchers and professionals working in the field. The book explains accurate dynamics and control algorithms, along with experimental validation of precision systems in industrial, medical, airborne and spaceborne applications. By using the proposed experimental designs, readers will be able to make further developments and validations. Presents accurate dynamics and control algorithms in industrial, medical, airborne and spaceborne applications Explains basic dynamics and control knowledge, such as Laplace transformations and stability analysis Teaches how to design, develop and control typical precision systems

This comprehensive book deals with motion estimation for autonomous systems from a biological, algorithmic and digital perspective. An algorithm, which is based on the optical flow constraint equation, is described in detail.

A revolutionary new framework that draws on insights from ecology for the design and analysis of long-duration robots Robots are increasingly leaving the confines of laboratories, warehouses, and manufacturing facilities, venturing into agriculture and other settings where they must operate in uncertain conditions over long timescales. This multidisciplinary book draws on the principles of ecology to show how robots can take full advantage of the environments they inhabit, including as sources of energy. Magnus Egerstedt introduces a revolutionary new design paradigm—robot ecology—that makes it possible to achieve long-duration autonomy while avoiding catastrophic failures. Central to ecology is the idea that the richness of an organism’s behavior is a function of the environmental constraints imposed by its habitat. Moving beyond traditional strategies that focus on optimal policies for making robots achieve targeted tasks, Egerstedt explores how to use survivability constraints to produce both effective and provably safe robot behaviors. He blends discussions of ecological principles with the development of control barrier functions as a formal approach to constraint-based control design, and provides an in-depth look at the design of the SlothBot, a slow and energy-efficient robot used for environmental monitoring and conservation. Visionary in scope, Robot Ecology presents a comprehensive and unified methodology for designing robots that can function over long durations in diverse natural environments.

This supplement to the VSD-Journal (2001) contains the full papers to lectures on vehicle system dynamics given at the world congress of IUTAM in Chicago in 2000. It thereby represents the advances in rail and automobile dynamics research.

Fundamental applications in control, sensing, and robotics, motivate the design of systems by selecting system elements, such as actuators or sensors, subject to constraints that require the elements not only to be a few in number, but also, to satisfy heterogeneity or interdependency constraints (called matroid constraints). For example, consider the scenarios: (1) (Control) Actuator placement: In a power grid, how should we place a few generators both to guarantee its stabilization with minimal control effort, and to satisfy interdependency constraints where the power grid must be controllable from the generators? (2) (Sensing) Sensor placement: In medical brain-wearable devices, how should we place a few sensors to ensure smoothing estimation capabilities? (3) (Robotics) Sensor scheduling: At a team of mobile robots, which few on-board sensors should we activate at each robot--subject to heterogeneity constraints on the number of sensors that each robot can activate at each time--so both to maximize the robots' battery life, and to ensure the robots' capability to complete a formation control task? In the first part of this thesis we motivate the above design problems, and propose the first algorithms to address them. In particular, although traditional approaches to matroid-constrained maximization have met great success in machine learning and facility location, they are unable to meet the aforementioned problem of actuator placement. In addition, although traditional approaches to sensor selection enable Kalman filtering capabilities, they do not enable smoothing or formation control capabilities, as required in the above problems of sensor placement and scheduling. Therefore, in the first part of the thesis we provide the first algorithms, and prove they achieve the following characteristics: provable approximation performance : the algorithms guarantee a solution close to the optimal; minimal running time: the algorithms terminate with the same running time as state-of-the-art algorithms for matroid-constrained maximization; adaptiveness: where applicable, at each time step the algorithms select system elements based on both the history of selections. We achieve the above ends by taking advantage of a submodular structure of in all aforementioned problems--submodularity is a diminishing property for set functions, parallel to convexity for continuous functions. But in failure-prone and adversarial environments, sensors and actuators can fail; sensors and actuators can get attacked. Thence, the traditional design paradigms over matroid-constraints become insufficient, and in contrast, resilient designs against attacks or failures become important. However, no approximation algorithms are known for their solution; relevantly, the problem of resilient maximization over matroid constraints is NP-hard. In the second part of this thesis we motivate the general problem of resilient maximization over matroid constraints, and propose the first algorithms to address it, to protect that way any design over matroid constraints, not only within the boundaries of control, sensing, and robotics, but also within machine learning, facility location, and matroid-constrained optimization in general. In particular, in the second part of this thesis we provide the first algorithms, and prove they achieve the following characteristics: resiliency: the algorithms are valid for any number of attacks or failures; adaptiveness: where applicable, at each time step the algorithms select system elements based on both the history of selections, and on the history of attacks or failures; provable approximation guarantees: the algorithms guarantee for any submodular or merely monotone function a solution close to the optimal; minimal running time: the algorithms terminate with the same running time as state-of-the-art algorithms for matroid-constrained maximization. We bound the performance of our algorithms by using notions of curvature for monotone (not necessarily submodular) set functions, which are established in the literature of submodular maximization.In the third and final part of this thesis we apply our tools for resilient maximization in robotics, and in particular, to the problem of active information gathering with mobile robots. This problem calls for the motion-design of a team of mobile robots so to enable the effective information gathering about a process of interest, to support, e.g., critical missions such as hazardous environmental monitoring, and search and rescue. Therefore, in the third part of this thesis we aim to protect such multi-robot information gathering tasks against attacks or failures that can result to the withdrawal of robots from the task. We conduct both numerical and hardware experiments in multi-robot multi-target tracking scenarios, and exemplify the benefits, as well as, the performance of our approach.