Download or read book Stochastic Control Via Chance Constrained Optimization and Its Application to Unmanned Aerial Vehicles written by Michael Peter Vitus and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Automation has had a strong presence in manufacturing for centuries, however its use in society has been very limited. The most ubiquitous robot in everyday life is the Roomba which has sold over 6 million, but the vacuuming task of this robot is simple in comparison with the difficult tasks performed in the manufacturing sector. Automation is successful in manufacturing because the task and environment can be well-defined to simplify the problem, whereas for automation to penetrate into society, it needs to safely operate in the presence of significantly greater uncertainty. Applications that could benefit from increased autonomy include: robot-assisted surgery, energy efficient control of buildings, autonomous control of vehicles, robotic assistance for elderly and disabled people, routing aircraft around weather, and home automation for tasks such as folding laundry, loading and emptying a dish washer, or tidying up a room. All of the above applications are examples of stochastic systems which contain three sources of uncertainty: process uncertainty, sensing uncertainty, and environment uncertainty. In order to safely operate in the intended domains, the system must account for all three types of uncertainty in generating a safe control strategy. This problem of generating control inputs for systems under uncertainty is commonly referred to as the stochastic control problem. One way of formulating this problem is as a chance constrained optimization problem that restricts the risk of violating the system's constraints to be below a user supplied threshold. This thesis develops several extensions over existing chance constrained programming solutions. The feedback controller is used to shape the uncertainty of the system to facilitate the satisfaction of the stochastic constraints, enabling previously infeasible solutions. Systems with component failures are also studied, and the computational complexity is drastically reduced over previous solution methods. The chance constrained framework is also extended to handle systems operating in uncertain environments. A novel hybrid approach is developed that uses a combination of sampling and analytic functions to represent the uncertainty. This approach results in a convex optimization program, guaranteeing the optimal solution and reducing the complexity over other methods. The formulation is further extended to incorporate future measurements of the uncertain environment to increase the performance of the system. The proposed stochastic control methods are solved in real-time to plan trajectories for a quadrotor unmanned aerial vehicle navigating in a three-dimensional cluttered, uncertain environment. The solution method enables the quadrotor to explore the environment to gather more information, allowing it to successfully complete its objective.
Download or read book Probabilistic and Randomized Methods for Design under Uncertainty written by Giuseppe Calafiore and published by Springer Science & Business Media. This book was released on 2006-03-06 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic and Randomized Methods for Design under Uncertainty is a collection of contributions from the world’s leading experts in a fast-emerging branch of control engineering and operations research. The book will be bought by university researchers and lecturers along with graduate students in control engineering and operational research.
Download or read book Optimization for Control Observation and Safety written by Guillermo Valencia-Palomo and published by MDPI. This book was released on 2020-04-01 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical optimization is the selection of the best element in a set with respect to a given criterion. Optimization has become one of the most used tools in control theory to compute control laws, adjust parameters (tuning), estimate states, fit model parameters, find conditions in order to fulfill a given closed-loop property, among others. Optimization also plays an important role in the design of fault detection and isolation systems to prevent safety hazards and production losses that require the detection and identification of faults, as early as possible to minimize their impacts by implementing real-time fault detection and fault-tolerant systems. Recently, it has been proven that many optimization problems with convex objective functions and linear matrix inequality (LMI) constraints can be solved easily and efficiently using existing software, which increases the flexibility and applicability of the control algorithms. Therefore, real-world control systems need to comply with several conditions and constraints that have to be taken into account in the problem formulation, which represents a challenge in the application of the optimization algorithms. This book offers an overview of the state-of-the-art of the most advanced optimization techniques and their applications in control engineering.
Download or read book Numerical Methods for Hybrid Control and Chance constrained Optimization Problems written by Achille Sassi and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis is devoted to the analysis of numerical methods in the field of optimal control, and it is composed of two parts. The first part is dedicated to new results on the subject of numerical methods for the optimal control of hybrid systems, controlled by measurable functions and discontinuous jumps in the state variable simultaneously. The second part focuses on a particular application of trajectory optimization problems for space launchers. Here we use some nonlinear optimization methods combined with non-parametric statistics techniques. This kind of problems belongs to the family of stochastic optimization problems and it features the minimization of a cost function in the presence of a constraint which needs to be satisfied within a desired probability threshold.
Download or read book Robust Goal directed Plan Execution with Bounded Risk written by Masahiro Ono (Ph. D.) and published by . This book was released on 2012 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is an increasing need for robust optimal plan execution for multi-agent systems in uncertain environments, while guaranteeing an acceptable probability of success. For example, a fleet of unmanned aerial vehicles (UAVs) and autonomous underwater vehicles (AUVs) are required to operate autonomously for an extensive mission duration in an uncertain environment. Previous work introduced the concept of a model-based executive, which increases the level of autonomy, elevating the level at which systems are commanded. This thesis develops model-based executives that reason explicitly from a stochastic plant model to find the optimal course of action, while ensuring that the probability of failure is within a user-specified risk bound. This thesis presents two robust mode-based executives: probabilistic Sulu or p-Sulu, and distributed probabilistic Sulu or dp-Sulu. The objective for p-Sulu and dp-Sulu is to allow users to command continuous, stochastic multi-agent systems in a manner that is both intuitive and safe. The user specifies the desired evolution of the plant state, as well as the acceptable probabilities of failure, as a temporal plan on states called a chance-constrained qualitative state plan (CCQSP). An example of a CCQSP statement is "go to A through B within 30 minutes, with less than 0.001% probability of failure." p-Sulu and dp-Sulu take a CCQSP, a continuous plant model with stochastic uncertainty, and an objective function as inputs, and outputs an optimal continuous control sequence, as well as an optimal discrete schedule. The difference between p-Sulu and dp-Sulu is that p-Sulu plans in a centralized manner while dp-Sulu plans in a distributed manner. dp-Sulu enables robust CCQSP execution for multi-agent systems. We solve the problem based on the key concept of risk allocation, which achieves tractability by allocating the specified risk to individual constraints and mapping the result into an equivalent deterministic constrained optimization problem. Risk allocation also enables a distributed plan execution for multi-agent systems by distributing the risk among agents to decompose the optimization problem. Building upon the risk allocation approach, we develop our first CCQSP executive, p-Sulu, in four spirals. First, we develop the Convex Risk Allocation (CRA) algorithm, which can solve a CCQSP planning problem with a convex state space and a fixed schedule, highlighting the capability of optimally allocating risk to individual constraints. Second, we develop the Non-convex Iterative Risk Allocation (NIRA) algorithm, which can handle non-convex state space. Third, we build upon NIRA a full-horizon CCQSP planner, p-Sulu FH, which can optimize not only the control sequence but also the schedule. Fourth, we develop p-Sulu, which enables the real-time execution of CCQSPs by employing the receding horizon approach. Our second CCQSP executive, dp-Sulu, is developed in two spirals. First, we develop the Market-based Iterative Risk Allocation (MIRA) algorithm, which can control a multiagent system in a distributed manner by optimally distributing risk among agents through the market-based method called tatonnement. Second and finally, we integrate the capability of MIRA into p-Sulu to build the robust model-based executive, dp-Sulu, which can execute CCQSPs on multi-agent systems in a distributed manner. Our simulation results demonstrate that our executives can efficiently execute CCQSP planning problems with significantly reduced suboptimality compared to prior art.
Download or read book Chance Constraints for Stochastic Optimal Control and Stochastic Optimization written by Onur Celik and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book NASA Formal Methods written by Sanjai Rayadurgam and published by Springer. This book was released on 2016-06-03 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 8th International Symposium on NASA Formal Methods, NFM 2016, held in Minneapolis, MN, USA, in June 2016. The 19 full and 10 short papers presented in this volume were carefully reviewed and selected from 70 submissions. The papers were organized in topical sections named: requirements and architectures; testing and run-time enforcement; theorem proving and proofs; application of formal methods; code generation and synthesis; model checking and verification; and correctness and certification.
Download or read book Control of Autonomous Aerial Vehicles written by Andrea L'Afflitto and published by Springer Nature. This book was released on 2023-11-20 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: Control of Autonomous Aerial Vehicles is an edited book that provides a single-volume snapshot on the state of the art in the field of control theory applied to the design of autonomous unmanned aerial vehicles (UAVs), aka “drones”, employed in a variety of applications. The homogeneous structure allows the reader to transition seamlessly through results in guidance, navigation, and control of UAVs, according to the canonical classification of the main components of a UAV’s autopilot. Each chapter has been written to assist graduate students and practitioners in the fields of aerospace engineering and control theory. The contributing authors duly present detailed literature reviews, conveying their arguments in a systematic way with the help of diagrams, plots, and algorithms. They showcase the applicability of their results by means of flight tests and numerical simulations, the results of which are discussed in detail. Control of Autonomous Aerial Vehicles will interest readers who are researchers, practitioners or graduate students in control theory, autonomous systems or robotics, or in aerospace, mechanical or electrical engineering.
Download or read book Stochastic Control Synthesis of Systems with Structured Uncertainty written by National Aeronautics and Space Administration (NASA) and published by Createspace Independent Publishing Platform. This book was released on 2018-06-11 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper presents a study on the design of robust controllers by using random variables to model structured uncertainty for both SISO and MIMO feedback systems. Once the parameter uncertainty is prescribed with probability density functions, its effects are propagated through the analysis leading to stochastic metrics for the system's output. Control designs that aim for satisfactory performances while guaranteeing robust closed loop stability are attained by solving constrained non-linear optimization problems in the frequency domain. This approach permits not only to quantify the probability of having unstable and unfavorable responses for a particular control design but also to search for controls while favoring the values of the parameters with higher chance of occurrence. In this manner, robust optimality is achieved while the characteristic conservatism of conventional robust control methods is eliminated. Examples that admit closed form expressions for the probabilistic metrics of the output are used to elucidate the nature of the problem at hand and validate the proposed formulations.Padula, Sharon L. (Technical Monitor) and Crespo, Luis G.Langley Research CenterSTOCHASTIC PROCESSES; CONTROL SYSTEMS DESIGN; OPTIMIZATION; ROBUSTNESS (MATHEMATICS); PROBABILITY THEORY; UNCERTAIN SYSTEMS; MIMO (CONTROL SYSTEMS); SISO (CONTROL SYSTEMS); PROBABILITY DENSITY FUNCTIONS; STABILITY; HIGH FREQUENCIES; DETERMINANTS; FEEDBACK CONTROL
Download or read book Randomized Algorithms for Analysis and Control of Uncertain Systems written by Roberto Tempo and published by Springer Science & Business Media. This book was released on 2012-10-21 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: The presence of uncertainty in a system description has always been a critical issue in control. The main objective of Randomized Algorithms for Analysis and Control of Uncertain Systems, with Applications (Second Edition) is to introduce the reader to the fundamentals of probabilistic methods in the analysis and design of systems subject to deterministic and stochastic uncertainty. The approach propounded by this text guarantees a reduction in the computational complexity of classical control algorithms and in the conservativeness of standard robust control techniques. The second edition has been thoroughly updated to reflect recent research and new applications with chapters on statistical learning theory, sequential methods for control and the scenario approach being completely rewritten. Features: · self-contained treatment explaining Monte Carlo and Las Vegas randomized algorithms from their genesis in the principles of probability theory to their use for system analysis; · development of a novel paradigm for (convex and nonconvex) controller synthesis in the presence of uncertainty and in the context of randomized algorithms; · comprehensive treatment of multivariate sample generation techniques, including consideration of the difficulties involved in obtaining identically and independently distributed samples; · applications of randomized algorithms in various endeavours, such as PageRank computation for the Google Web search engine, unmanned aerial vehicle design (both new in the second edition), congestion control of high-speed communications networks and stability of quantized sampled-data systems. Randomized Algorithms for Analysis and Control of Uncertain Systems (second edition) is certain to interest academic researchers and graduate control students working in probabilistic, robust or optimal control methods and control engineers dealing with system uncertainties. The present book is a very timely contribution to the literature. I have no hesitation in asserting that it will remain a widely cited reference work for many years. M. Vidyasagar
Download or read book Cooperative Control Models Applications and Algorithms written by Sergiy Butenko and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the last decades, considerable progress has been observed in all aspects regarding the study of cooperative systems including modeling of cooperative systems, resource allocation, discrete event driven dynamical control, continuous and hybrid dynamical control, and theory of the interaction of information, control, and hierarchy. Solution methods have been proposed using control and optimization approaches, emergent rule based techniques, game theoretic and team theoretic approaches. Measures of performance have been suggested that include the effects of hierarchies and information structures on solutions, performance bounds, concepts of convergence and stability, and problem complexity. These and other topics were discusses at the Second Annual Conference on Cooperative Control and Optimization in Gainesville, Florida. Refereed papers written by selected conference participants from the conference are gathered in this volume, which presents problem models, theoretical results, and algorithms for various aspects of cooperative control. Audience: The book is addressed to faculty, graduate students, and researchers in optimization and control, computer sciences and engineering.
Download or read book State Estimation and Control for Low cost Unmanned Aerial Vehicles written by Chingiz Hajiyev and published by Springer. This book was released on 2015-06-10 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses state estimation and control procedures for a low-cost unmanned aerial vehicle (UAV). The authors consider the use of robust adaptive Kalman filter algorithms and demonstrate their advantages over the optimal Kalman filter in the context of the difficult and varied environments in which UAVs may be employed. Fault detection and isolation (FDI) and data fusion for UAV air-data systems are also investigated, and control algorithms, including the classical, optimal, and fuzzy controllers, are given for the UAV. The performance of different control methods is investigated and the results compared. State Estimation and Control of Low-Cost Unmanned Aerial Vehicles covers all the important issues for designing a guidance, navigation and control (GNC) system of a low-cost UAV. It proposes significant new approaches that can be exploited by GNC system designers in the future and also reviews the current literature. The state estimation, control and FDI methods are illustrated by examples and MATLAB® simulations. State Estimation and Control of Low-Cost Unmanned Aerial Vehicles will be of interest to both researchers in academia and professional engineers in the aerospace industry. Graduate students may also find it useful, and some sections are suitable for an undergraduate readership.
Download or read book UAV Cooperative Decision and Control written by Tal Shima and published by SIAM. This book was released on 2009-02-19 with total page 179 pages. Available in PDF, EPUB and Kindle. Book excerpt: An authoritative reference on cooperative decision and control of unmanned aerial vehicles.
Download or read book Optimization and Cooperative Control Strategies written by Michael Hirsch and published by Springer. This book was released on 2008-10-18 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cooperative, collaborating autonomous systems are at the forefront of research efforts in numerous disciplines across the applied sciences. There is constant progress in solution techniques for these systems. However, despite this progress, cooperating systems have continued to be extremely difficult to model, analyze, and solve. Theoretical results are very difficult to come by. Each year, the International Conference on Cooperative Control and Optimization (CCO) brings together top researchers from around the world to present new, cutting-edge, ideas, theories, applications, and advances in the fields of autonomous agents, cooperative systems, control theory, information flow, and optimization. The works in this volume are a result of invited papers and selected presentations at the Eighth Annual International Conference on Cooperative Control and Optimization, held in Gainesville, Florida, January 30 – February 1, 2008.
Download or read book Journal of Guidance Control and Dynamics written by and published by . This book was released on 2009 with total page 954 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Proceedings of 2023 7th Chinese Conference on Swarm Intelligence and Cooperative Control written by Guo-Ping Jiang and published by Springer Nature. This book was released on with total page 674 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Advanced Robust Nonlinear Control Approaches for Quadrotor Unmanned Aerial Vehicle written by Moussa Labbadi and published by Springer. This book was released on 2021-10-21 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book studies selected advanced flight control schemes for an uncertain quadrotor unmanned aerial vehicle (UAV) systems in the presence of constant external disturbances, parametric uncertainties, measurement noise, time-varying external disturbances, and random external disturbances. Furthermore, in all the control techniques proposed in this book, it includes the simulation results with comparison to other nonlinear control schemes recently developed for the tracking control of a quadrotor UAV. The main contributions of the present book for quadrotor UAV systems are as follows: (i) the proposed control methods are based on the high-order sliding mode controller (SMC) and hybrid control algorithm with an optimization method. (ii) the finite-time control schemes are developed by using fast terminal SMC (FTSMC), nonsingular FTSMC (NFTSMC), global time-varying SMC, and adaptive laws. (iii) the fractional-order flight control schemes are developed by using the fractional-order calculus theory, super twisting algorithm, NFTSMC, and the SMC. This book covers the research history and importance of quadrotor system subject to system uncertainties, external wind disturbances, and noise measurements, as well as the research status of advanced flight control methods, adaptive flight control methods, and flight control based on fractional-order theory. The book would be interesting to most academic undergraduate, postgraduates, researchers on flight control for drones and applications of advanced controllers in engineering field. This book presents a must-survey for advanced finite-time control for quadrotor system. Some parts of this book have the potential of becoming the courses for the modelling and control of autonomous flying machines. Readers (academic researcher, undergraduate student, postgraduate student, MBA/executive, and education practitioner) interested in nonlinear control methods find this book an investigation. This book can be used as a good reference for the academic research on the control theory, drones, terminal sliding mode control, and related to this or used in Ph.D. study of control theory and their application in field engineering.
