Event-triggering architectures for adaptive control of uncertain dynamical systems

In this dissertation, new approaches are presented for the design and implementation of networked adaptive control systems to reduce the wireless network utilization while guaranteeing system stability in the presence of system uncertainties. Specifically, the design and analysis of state feedback adaptive control systems over wireless networks using event-triggering control theory is first presented. The state feedback adaptive control results are then generalized to the output feedback case for dynamical systems with unmeasurable state vectors. This event-triggering approach is then adopted for large-scale uncertain dynamical systems. In particular, decentralized and distributed adaptive control methodologies are proposed with reduced wireless network utilization with stability guarantees. In addition, for systems in the absence of uncertainties, a new observer-free output feedback cooperative control architecture is developed. Specifically, the proposed architecture is predicated on a nonminimal state-space realization that generates an expanded set of states only using the filtered input and filtered output and their derivatives for each vehicle, without the need for designing an observer for each vehicle. Building on the results of this new observer-free output feedback cooperative control architecture, an event-triggering methodology is next proposed for the output feedback cooperative control to schedule the exchanged output measurements information between the agents in order to reduce wireless network utilization. Finally, the output feedback cooperative control architecture is generalized to adaptive control for handling exogenous disturbances in the follower vehicles. For each methodology, the closed-loop system stability properties are rigorously analyzed, the effect of the user-defined event-triggering thresholds and the controller design parameters on the overall system performance are characterized, and Zeno behavior is shown not to occur with the proposed algorithms --Abstract, page iv

Observer-Based Unknown Input Estimator of Wave Excitation Force for a Wave Energy Converter

Several energy maximization control approaches for point-absorber wave-energy converter (PAWEC) systems require knowledge of the wave excitation force (WEF) that is not measurable during the PAWEC operation. Many WEF estimators have been proposed based on stochastic PAWEC modeling using the Kalman filter (KF), extended KF (EKF), or receding-horizon estimation. Alternatively, a deterministic WEF estimator is proposed here based on the fast unknown input estimation (FUIE) concept. The WEF is estimated as an unknown input obviating the requirement to represent its dynamics. The proposed observer-based unknown input estimator (OBUIE) inherits the capability of estimating fast-changing signals from the FUIE, which is important when considering irregular wave conditions. Unlike preceding methods, the OBUIE is designed based on a PAWEC model, including the nonlinear viscous drag force. It has been shown that the nonlinear viscous drag force is essential for accurate PAWEC model description, within the energy maximization control role. The performance of the proposed estimator is evaluated in terms of PAWEC conversion efficiency in a single degree-of-freedom PAWEC device operating in regular and irregular waves. Simulation results are obtained using MATLAB to evaluate the estimator under different control methods and subject to parametric uncertainty

Predictive control approaches to fault tolerant control of wind turbines

This thesis focuses on active fault tolerant control (AFTC) of wind turbine systems. Faults in wind turbine systems can be in the form of sensor faults, actuator faults, or component faults. These faults can occur in different locations, such as the wind speed sensor, the generator system, drive train system or pitch system. In this thesis, some AFTC schemes are proposed for wind turbine faults in the above locations. Model predictive control (MPC) is used in these schemes to design the wind turbine controller such that system constraints and dual control goals of the wind turbine are considered. In order to deal with the nonlinearity in the turbine model, MPC is combined with Takagi-Sugeno (T-S) fuzzy modelling. Different fault diagnosis methods are also proposed in different AFTC schemes to isolate or estimate wind turbine faults.The main contributions of the thesis are summarized as follows:A new effective wind speed (EWS) estimation method via least-squares support vector machines (LSSVM) is proposed. Measurements from the wind turbine rotor speed sensor and the generator speed sensor are utilized by LSSVM to estimate the EWS. Following the EWS estimation, a wind speed sensor fault isolation scheme via LSSVM is proposed.A robust predictive controller is designed to consider the EWS estimation error. This predictive controller serves as the baseline controller for the wind turbine system operating in the region below rated wind speed.T-S fuzzy MPC combining MPC and T-S fuzzy modelling is proposed to design the wind turbine controller. MPC can deal with wind turbine system constraints externally. On the other hand, T-S fuzzy modelling can approximate the nonlinear wind turbine system with a linear time varying (LTV) model such that controller design can be based on this LTV model. Therefore, the advantages of MPC and T-S fuzzy modelling are both preserved in the proposed T-S fuzzy MPC.A T-S fuzzy observer, based on online eigenvalue assignment, is proposed as the sensor fault isolation scheme for the wind turbine system. In this approach, the fuzzy observer is proposed to deal with the nonlinearity in the wind turbine system and estimate system states. Furthermore, the residual signal generated from this fuzzy observer is used to isolate the faulty sensor.A sensor fault diagnosis strategy utilizing both analytical and hardware redundancies is proposed for wind turbine systems. This approach is proposed due to the fact that in the real application scenario, both analytical and hardware redundancies of wind turbines are available for designing AFTC systems.An actuator fault estimation method based on moving horizon estimation (MHE) is proposed for wind turbine systems. The estimated fault by MHE is then compensated by a T-S fuzzy predictive controller. The fault estimation unit and the T-S fuzzy predictive controller are combined to form an AFTC scheme for wind turbine actuator faults

지도 및 비지도 학습을 이용한 로봇 머니퓰레이터 충돌 감지

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2022.2. 박종우.사람과 공유된 구조화되지 않은 동적 환경에서 작동하는 협업 로봇 머니퓰레이터는 날카로운 충돌(경성 충돌)에서 더 긴 지속 시간의 밀고 당기는 동작(연성 충돌)에 이르기까지의 다양한 충돌을 빠르고 정확하게 감지해야 한다. 모터 전류 측정값을 이용해 외부 조인트 토크를 추정하는 동역학 모델 기반 감지 방법을 사용할 경우, 정확한 마찰 파라미터 모델링 및 식별과 같은 모터 마찰에 대한 적절한 처리가 필요하다. 올바르게 적용하면 매우 효과적이지만, 동역학과 마찰 파라미터를 모델링 및 식별하고 여러 개의 감지 임계값을 수동으로 설정하는 데에는 상당한 노력이 필요하기 때문에 대량 생산되는 산업용 로봇에 이를 적용하기는 어렵다. 또한 적절한 식별 후에도 동역학에 백래시, 탄성 등 모델링되지 않은 효과나 불확실성이 여전히 존재할 수 있다. 본 논문에서는 순수 모델 기반 방법의 구현 어려움을 피하고 불확실한 동역학적 효과를 보상하는 수단으로 로봇 머니퓰레이터를 위한 총 네 가지의 학습 기반 충돌 감지 방법을 제안한다. 두 개의 방법은 학습을 위해 충돌 및 비충돌 동작 데이터가 모두 필요한 지도 학습 알고리즘(서포트 벡터 머신 회귀, 일차원 합성곱 신경망 기반)을 사용하며 나머지 두 개의 방법은 학습을 위해 비충돌 동작 데이터만을 필요로 하는 비지도 이상치 탐지 알고리즘(단일 클래스 서포트 벡터 머신, 오토인코더 기반)에 기반한다. 로봇 동역학 모델과 모터 전류 측정값만을 필요로 하며 추가적인 외부 센서나 마찰 모델링, 여러 개의 감지 임계값에 대한 수동 조정은 필요하지 않다. 먼저 지도 및 비지도 감지 방법을 학습시키고 검증하는 데 사용되는, 6자유도 협업 로봇 머니퓰레이터를 이용해 수집된 로봇 충돌 데이터를 설명한다. 우리가 고려하는 충돌 시나리오는 경성 충돌, 연성 충돌, 비충돌 동작으로, 경성 및 연성 충돌은 모두 동일하게 충돌로 간주한다. 감지 성능 검증을 위한 테스트 데이터는 총 787건의 충돌과 62.4분의 비충돌 동작으로 이루어져 있으며, 이는 로봇이 랜덤 점대점 6관절 동작을 수행하는 동안 수집된다. 데이터 수집 중 로봇의 끝단에는 미부착, 3.3 kg, 5.0 kg의 세 가지 유형의 페이로드를 부착한다. 다음으로, 수집된 테스트 데이터를 이용해 지도 감지 방법의 감지 성능을 실험적으로 검증한다. 실험 결과는 지도 감지 방법이 가벼운 네트워크를 이용해 광범위한 경성 및 연성 충돌을 실시간으로 정확하게 감지할 수 있음을 보여주며, 이를 통해 모델 파라미터의 불확실성과 측정 노이즈, 백래시, 변형 등 모델링되지 않은 효과까지 보상됨을 알 수 있다. 또한 서포트 벡터 머신 회귀 기반 방법은 하나의 감지 임계값에 대한 조정만 필요하며 일차원 합성곱 신경망 기반 방법은 하나의 아웃풋 필터 파라미터에 대한 조정만 필요한데, 두 방법 모두 직관적인 감도 조정이 가능하다. 나아가 일련의 시뮬레이션 실험을 통해 지도 감지 방법의 일반화 성능을 실험적으로 검증한다. 마지막으로, 동일한 테스트 데이터에 대해 비지도 감지 방법의 감지 성능과 일반화 성능 또한 검증한다. 실험 결과는 비지도 감지 방법 또한 가벼운 계산과 하나의 감지 임계값에 대한 조정만으로 다양한 경성 및 연성 충돌을 실시간으로 강인하게 감지할 수 있음을 보여주며, 이를 통해 모델링되지 않은 마찰을 포함한 불확실한 동역학적 효과를 비지도 학습으로도 보상할 수 있음을 알 수 있다. 지도 감지 방법이 더 나은 감지 성능을 보이지만, 비지도 감지 방법은 학습을 위해 비충돌 동작 데이터만을 필요로 하며 발생할 수 있는 모든 유형의 충돌에 대한 정보를 필요로 하지 않기 때문에 대량 생산되는 산업용 로봇에 더 적합하다.Collaborative robot manipulators operating in dynamic and unstructured environments shared with humans require fast and accurate detection of collisions, which can range from sharp impacts (hard collisions) to pulling and pushing motions of longer duration (soft collisions). When using dynamics model-based detection methods that estimate the external joint torque with motor current measurements, proper treatment for friction in the motors is required, such as accurate modeling and identification of friction parameters. Although highly effective when done correctly, modeling and identifying the dynamics and friction parameters, and manually setting multiple detection thresholds require considerable effort, making them difficult to be replicated for mass-produced industrial robots. There may also still exist unmodeled effects or uncertainties in the dynamics even after proper identification, e.g., backlash, elasticity. This dissertation presents a total of four learning-based collision detection methods for robot manipulators as a means of sidestepping some of the implementation difficulties of pure model-based methods and compensating for uncertain dynamic effects. Two methods use supervised learning algorithms – support vector machine regression and a one-dimensional convolutional neural network-based – that require both the collision and collision-free motion data for training. The other two methods are based on unsupervised anomaly detection algorithms – a one-class support vector machine and an autoencoder-based – that require only the collision-free motion data for training. Only the motor current measurements together with a robot dynamics model are required while no additional external sensors, friction modeling, or manual tuning of multiple detection thresholds are needed. We first describe the robot collision dataset collected with a six-dof collaborative robot manipulator, which is used for training and validating our supervised and unsupervised detection methods. The collision scenarios we consider are hard collisions, soft collisions, and collision-free, where both hard and soft collisions are treated in the same manner as just collisions. The test dataset for detection performance verification includes a total of 787 collisions and 62.4 minutes of collision-free motions, all collected while the robot is executing random point-to-point six-joint motions. During data collection, three types of payloads are attached to the end-effector: no payload, 3.3 kg payload, and 5.0 kg payload. Then the detection performance of our supervised detection methods is experimentally verified with the collected test dataset. Results demonstrate that our supervised detection methods can accurately detect a wide range of hard and soft collisions in real-time using a light network, compensating for uncertainties in the model parameters as well as unmodeled effects like friction, measurement noise, backlash, and deformations. Moreover, the SVMR-based method requires only one constant detection threshold to be tuned while the 1-D CNN-based method requires only one output filter parameter to be tuned, both of which allow intuitive sensitivity tuning. Furthermore, the generalization capability of our supervised detection methods is experimentally verified with a set of simulation experiments. Finally, our unsupervised detection methods are also validated for the same test dataset; the detection performance and the generalization capability are verified. The experimental results show that our unsupervised detection methods are also able to robustly detect a variety of hard and soft collisions in real-time with very light computation and with only one constant detection threshold required to be tuned, validating that uncertain dynamic effects including the unmodeled friction can be successfully compensated also with unsupervised learning. Although our supervised detection methods show better detection performance, our unsupervised detection methods are more practical for mass-produced industrial robots since they require only the data for collision-free motions for training, and the knowledge of every possible type of collision that can occur is not required.1 Introduction 1 1.1 Model-Free Methods 2 1.2 Model-Based Methods 2 1.3 Learning-Based Methods 4 1.3.1 Using Supervised Learning Algorithms 5 1.3.2 Using Unsupervised Learning Algorithms 6 1.4 Contributions of This Dissertation 7 1.4.1 Supervised Learning-Based Model-Compensating Detection 7 1.4.2 Unsupervised Learning-Based Model-Compensating Detection 8 1.4.3 Comparison with Existing Detection Methods 9 1.5 Organization of This Dissertation 14 2 Preliminaries 17 2.1 Introduction 17 2.2 Robot Dynamics 17 2.3 Momentum Observer-Based Collision Detection 19 2.4 Supervised Learning Algorithms 21 2.4.1 Support Vector Machine Regression 21 2.4.2 One-Dimensional Convolutional Neural Network 23 2.5 Unsupervised Anomaly Detection 25 2.6 One-Class Support Vector Machine 26 2.7 Autoencoder-Based Anomaly Detection 28 2.7.1 Autoencoder Network Architecture and Training 28 2.7.2 Anomaly Detection Using Autoencoders 29 3 Robot Collision Data 31 3.1 Introduction 31 3.2 True Collision Index Labeling 31 3.3 Collision Scenarios 35 3.4 Monitoring Signal 36 3.5 Signal Normalization and Sampling 37 3.6 Test Data for Detection Performance Verification 39 4 Supervised Learning-Based Model-Compensating Detection 43 4.1 Introduction 43 4.2 SVMR-Based Collision Detection 44 4.2.1 Input Feature Vector Design 44 4.2.2 SVMR Training 45 4.2.3 Collision Detection Sensitivity Adjustment 46 4.3 1-D CNN-Based Collision Detection 50 4.3.1 Network Input Design 50 4.3.2 Network Architecture and Training 50 4.3.3 An Output Filtering Method to Reduce False Alarms 53 4.4 Collision Detection Performance Criteria 54 4.4.1 Area Under the Precision-Recall Curve (PRAUC) 54 4.4.2 Detection Delay and Number of Detection Failures 54 4.5 Collision Detection Performance Analysis 56 4.5.1 Global Performance with Varying Thresholds 56 4.5.2 Detection Delay and Number of Detection Failures 57 4.5.3 Real-Time Inference 60 4.6 Generalization Capability Analysis 60 4.6.1 Generalization to Small Perturbations 60 4.6.2 Generalization to an Unseen Payload 62 5 Unsupervised Learning-Based Model-Compensating Detection 67 5.1 Introduction 67 5.2 OC-SVM-Based Collision Detection 68 5.2.1 Input Feature Vector 68 5.2.2 OC-SVM Training 70 5.2.3 Collision Detection with the Trained OC-SVM 70 5.3 Autoencoder-Based Collision Detection 70 5.3.1 Network Input and Output 71 5.3.2 Network Architecture and Training 71 5.3.3 Collision Detection with the Trained Autoencoder 72 5.4 Collision Detection Performance Analysis 74 5.4.1 Global Performance with Varying Thresholds 75 5.4.2 Detection Delay and Number of Detection Failures 75 5.4.3 Comparison with Supervised Learning-Based Methods 80 5.4.4 Real-Time Inference 83 5.5 Generalization Capability Analysis 83 5.5.1 Generalization to Small Perturbations 84 5.5.2 Generalization to an Unseen Payload 85 6 Conclusion 89 6.1 Summary and Discussion 89 6.2 Future Work 93 A Appendix 95 A.1 SVM-Based Classification of Detected Collisions 95 A.2 Direct Estimation-Based Detection Methods 97 A.3 Model-Independent Supervised Detection Methods 101 A.4 Generalization to Large Changes in the Dynamics Model 102 Bibliography 106 Abstract 112박

Disturbance observer design for nonlinear systems represented by input-output models

A new approach to the design of nonlinear disturbance observers for a class of nonlinear systems described by inputoutput differential equations is presented in this paper. In contrast with established forms of nonlinear disturbance observers, the most important feature of this new type of disturbance observer is that only measurement of the output variable is required, rather than the state variables. An inverse simulation model is first constructed based on knowledge of the structure and parameters of a conventional model of the system. The disturbance can then be estimated by comparing the output of the inverse model and the input of the original nonlinear system. Mathematical analysis demonstrates the convergence of this new form of nonlinear disturbance observer. The approach has been applied to disturbance estimation for a linear system and a new form of linear disturbance observer has been developed. The differences between the proposed linear disturbance observer and the conventional form of frequency-domain disturbance observer are discussed through a numerical example. Finally, the nonlinear disturbance observer design method is illustrated through an application involving a simulation of a jacketed continuous stirred tank reactor syste

Design criteria for flight evaluation. Monograph 4 - Control system evaluation

Methods and analyses for flight evaluation of control systems for multistage launch vehicle

Review of dynamic positioning control in maritime microgrid systems

For many offshore activities, including offshore oil and gas exploration and offshore wind farm construction, it is essential to keep the position and heading of the vessel stable. The dynamic positioning system is a progressive technology, which is extensively used in shipping and other maritime structures. To maintain the vessels or platforms from displacement, its thrusters are used automatically to control and stabilize the position and heading of vessels in sea state disturbances. The theory of dynamic positioning has been studied and developed in terms of control techniques to achieve greater accuracy and reduce ship movement caused by environmental disturbance for more than 30 years. This paper reviews the control strategies and architecture of the DPS in marine vessels. In addition, it suggests possible control principles and makes a comparison between the advantages and disadvantages of existing literature. Some details for future research on DP control challenges are discussed in this paper