모델 예측 제어와 네트워크 지연 보상 기법을 이용한 무인기의 네트워크 제어

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2019. 2. 김현진.본 연구는 시간에 따라 변화하는 네트워크 지연이 존재하는 네트워크 환경에서의 무인 항공기 (UAV)의 제어 기법에 대하여 소개한다. 네트워크 지연은 주로 상태 피드백과 제어 입력의 지연을 야기시키고, 이로 인해 UAV 제어 시스템의 안정성에 악영향을 미친다. 이와 같은 네트워크 지연에 대처하기 위하여 몇 가지 네트워크 제어 알고리즘이 제안되었지만 대부분의 기존 연구에서는 플랜트 동역학이 매우 단순하거나 정확히 알고 있다고 가정하였고, 일정한 네트워크 지연이 발생하는 상황에서만 수행되었다. 하지만 이러한 가정은 비선형 모델 및 시간에 민감한 제어 특성을 가지는 멀티로터 형태의 UAV에 적합하지 않다. 이러한 문제를 해결하기 위하여 멀티로터의 특성을 고려하여 설계된 모델 예측 제어 (MPC)를 이용한 네트워크 제어 시스템을 제안한다. 또한 경로 계획 및 상태 추정의 정확도를 높이고자 가우시안 프로세스 (GP) 기법을 적용하여, 멀티로터 동역학에 고려되지 않은 미지의 모델을 학습하도록 한다. 실내 비행 실험을 통하여 제안 된 알고리즘이 네트워크 지연을 효과적으로 보상하고 가우시안 프로세스 학습이 UAV의 경로 추적 성능을 향상 시킨다는 것을 보여준다.This study addresses an operation of unmanned aerial vehicles (UAVs) in a network environment where there is time-varying network delay. The network delay entails undesirable effects on the stability of the UAV control system due to delayed state feedback and outdated control input. Although several networked control algorithms have been proposed to deal with the network delay, most existing studies have assumed that the plant dynamics is known and simple, or the network delay is constant. These assumptions are improper to multirotor-type UAVs because of their nonlinearity and time-sensitive characteristics. To deal with these problems, we propose a networked control system using model predictive control (MPC) designed under the consideration of multirotor characteristics. We also apply a Gaussian process (GP) to learn an unknown nonlinear model, which increases the accuracy of path planning and state estimation. Flight experiments show that the proposed algorithm successfully compensates the network delay and Gaussian process learning improves the UAVs path tracking performance.Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi Chapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Thesis contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Thesis outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1 GP-MPC for path planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Uplink delay compensation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.3 Downlink delay compensation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.4 Clock synchronization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3 Model learning using Gaussian process . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.1 System dynamics for multirotor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.2 Gaussian process to improve dynamic model . . . . . . . . . . . . . . . . . . . . . . 11 4 Model predictive control for networked UAV . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.1 MPC formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.2 MPC formulation for networked control systems . . . . . . . . . . . . . . . . . . . 15 5 Flight experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 5.1 Delay analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 5.2 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 5.3 Experiment 1: circular flight with network delays . . . . . . . . . . . . . . . . . . . 20 5.4 Experiment 2: two UAVs control with different network delays . . . . . . . . . . . 24 6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27Maste

Networked Predictive Fuzzy Control of Systems with Forward Channel Delays based on a Linear Model Predictor

This paper presents a novel networked control framework, using fuzzylogic control, for systems with network delays which are known togreatly weaken the control performance of the controlled system. Todeal with the network delays, the predicted differences between thedesired future set-points and the predicted outputs from a modelpredictor are utilized as the inputs of a fuzzy controller, thus aseries of future control actions are generated. By selecting theappropriated control sequence in the plant side, the network delaysare compensated. The simulative results demonstrate that theproposed method can obviously reduce the effect of network delays,and improve the system dynamic performance

Sequence-based Anytime Control

We present two related anytime algorithms for control of nonlinear systems when the processing resources available are time-varying. The basic idea is to calculate tentative control input sequences for as many time steps into the future as allowed by the available processing resources at every time step. This serves to compensate for the time steps when the processor is not available to perform any control calculations. Using a stochastic Lyapunov function based approach, we analyze the stability of the resulting closed loop system for the cases when the processor availability can be modeled as an independent and identically distributed sequence and via an underlying Markov chain. Numerical simulations indicate that the increase in performance due to the proposed algorithms can be significant.Comment: 14 page

Multiple Loop Self-Triggered Model Predictive Control for Network Scheduling and Control

We present an algorithm for controlling and scheduling multiple linear time-invariant processes on a shared bandwidth limited communication network using adaptive sampling intervals. The controller is centralized and computes at every sampling instant not only the new control command for a process, but also decides the time interval to wait until taking the next sample. The approach relies on model predictive control ideas, where the cost function penalizes the state and control effort as well as the time interval until the next sample is taken. The latter is introduced in order to generate an adaptive sampling scheme for the overall system such that the sampling time increases as the norm of the system state goes to zero. The paper presents a method for synthesizing such a predictive controller and gives explicit sufficient conditions for when it is stabilizing. Further explicit conditions are given which guarantee conflict free transmissions on the network. It is shown that the optimization problem may be solved off-line and that the controller can be implemented as a lookup table of state feedback gains. Simulation studies which compare the proposed algorithm to periodic sampling illustrate potential performance gains.Comment: Accepted for publication in IEEE Transactions on Control Systems Technolog

Fault diagnosis for uncertain networked systems

Fault diagnosis has been at the forefront of technological developments for several decades. Recent advances in many engineering fields have led to the networked interconnection of various systems. The increased complexity of modern systems leads to a larger number of sources of uncertainty which must be taken into consideration and addressed properly in the design of monitoring and fault diagnosis architectures. This chapter reviews a model-based distributed fault diagnosis approach for uncertain nonlinear large-scale networked systems to specifically address: (a) the presence of measurement noise by devising a filtering scheme for dampening the effect of noise; (b) the modeling of uncertainty by developing an adaptive learning scheme; (c) the uncertainty issues emerging when considering networked systems such as the presence of delays and packet dropouts in the communication networks. The proposed architecture considers in an integrated way the various components of complex distributed systems such as the physical environment, the sensor level, the fault diagnosers, and the communication networks. Finally, some actions taken after the detection of a fault, such as the identification of the fault location and its magnitude or the learning of the fault function, are illustrated