This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

A Glimpse at Set Theory. The Real Numbers. The Topology of Cartesian Spaces. Convergence. Continuous Functions. Functions of One Variable. Infinite Series. Differentiation in RP Integration in RP.

The elements of real analysis (2nd edition), by Robert G. Bartle. Pp xv, 480. £10. 1976. SBM 0 471 05464 X (Wiley)

We show that for an adaptive controller that uses recorded and instantaneous data concurrently for adaptation, a verifiable condition on linear independence of the recorded data is sufficient to guarantee exponential tracking error and parameter error convergence. This condition is found to be less restrictive and easier to monitor than a condition on persistently exciting exogenous input signal required by traditional adaptive laws that use only instantaneous data for adaptation.

Concurrent learning for convergence in adaptive control without persistency of excitation

Thank you for reading stable adaptive systems. As you may know, people have look hundreds times for their chosen readings like this stable adaptive systems, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they cope with some harmful virus inside their desktop computer. stable adaptive systems is available in our book collection an online access to it is set as public so you can get it instantly. Our digital library saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the stable adaptive systems is universally compatible with any devices to read.

Stable Adaptive Systems

Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalising previously learned rules to new scenarios. For these reasons, the use of deep learning for vehicle control is becoming increasingly popular. Although important advancements have been achieved in this field, these works have not been fully summarised. This paper surveys a wide range of research works reported in the literature which aim to control a vehicle through deep learning methods. Although there exists overlap between control and perception, the focus of this paper is on vehicle control, rather than the wider perception problem which includes tasks such as semantic segmentation and object detection. The paper identifies the strengths and limitations of available deep learning methods through comparative analysis and discusses the research challenges in terms of computation, architecture selection, goal specification, generalisation, verification and validation, as well as safety. Overall, this survey brings timely and topical information to a rapidly evolving field relevant to intelligent transportation systems.

A Survey of Deep Learning Applications to Autonomous Vehicle Control

This brief presents the systematic design and real-time experimental results of a fault detection, isolation, and accommodation algorithm for quadrotor actuator faults using nonlinear adaptive estimation techniques. The fault diagnosis architecture consists of a nonlinear fault detection estimator and a bank of nonlinear adaptive fault isolation estimators designed based on the functional structures of the faults under consideration. Adaptive thresholds for fault detection and isolation are systematically designed to enhance the robustness and fault sensitivity of the diagnostic algorithm. After fault isolation, the fault parameter estimate generated by the matched isolation estimator is used for accommodating the fault effect. Using an indoor quadrotor test environment, real-time experimental results are shown to illustrate the effectiveness of the algorithms.

Quadrotor Actuator Fault Diagnosis and Accommodation Using Nonlinear Adaptive Estimators

This brief presents the design, analysis, and implementation of a nonlinear robust adaptive fault-tolerant altitude and attitude tracking method for accommodating actuator faults in quadrotor unmanned aerial vehicles without the need of a fault diagnosis mechanism. Actuator faults are modeled as a constant loss of effectiveness in the thrust generated by the rotors. The fault-tolerant control (FTC) algorithm guarantees asymptotic convergence of the altitude and attitude tracking error even in the presence of possible multiple actuator faults and modeling uncertainties. The FTC method is implemented using a real-time indoor quadrotor test environment. Experimental results are shown to illustrate the effectiveness of the algorithm.

Nonlinear Adaptive Fault-Tolerant Quadrotor Altitude and Attitude Tracking With Multiple Actuator Faults

This paper presents a new modification term for use in adaptive control to improve an already existing design. By employing this term in a conventional adaptive law, the loop transfer properties of a reference model associated with a non-adaptive control design can be preserved. Consequently, this term increases the level of confidence of adaptive flight control systems for purposes of increased flight safety. The results are illustrated on an unmanned combat aerial vehicle dynamic model. I. Introduction n this paper we present an improved method of adaptation that enhances robustness of adaptive control systems. The method is arrived at by examining the loop transfer properties of an adaptive system when linearized about a given flight condition. The design approach modifies a conventional adaptive law with the goal of preserving the loop transfer properties of a reference model associated with a non-adaptive control design. The aim is to achieve an adaptive system that preserves the stability margins of a non-adaptive design, while at the same time providing the benefits of adaptation to modeling error. I

http://enu.kz/repository/2009/AIAA-2009-5967.pdf

A Loop Recovery Method for Adaptive Control

IMU Sensor Fault Diagnosis and Estimation for Quadrotor UAVs

This paper presents two control algorithms enabling a UAV to circumnavigate an unknown target using range and range rate (i.e., the derivative of range) measurements. Given a prescribed orbit radius, both control algorithms (i) tend to drive the UAV toward the tangent of prescribed orbit when the UAV is outside or on the orbit, and (ii) apply zero control input if the UAV is inside the desired orbit. The algorithms differ in that, the first algorithm is smooth and unsaturated while the second algorithm is non-smooth and saturated. By analyzing properties associated with the bearing angle of the UAV relative to the target and through proper design of Lyapunov functions, it is shown that both algorithms produce the desired orbit for an arbitrary initial state. Three examples are provided as a proof of concept.

Jonathan A. Muse

Papers

Quadrotor Actuator Fault Diagnosis and Accommodation Using Nonlinear Adaptive Estimators

Nonlinear Adaptive Fault-Tolerant Quadrotor Altitude and Attitude Tracking With Multiple Actuator Faults

A Loop Recovery Method for Adaptive Control

IMU Sensor Fault Diagnosis and Estimation for Quadrotor UAVs

Circumnavigation of an Unknown Target Using UAVs with Range and Range Rate Measurements