Controle reconfigurável de processos sujeitos a falhas em atuadores : uma abordagem baseada no MPC em duas camadas

Orientadores: Flávio Vasconcelos da Silva, Thiago Vaz da CostaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia QuímicaResumo: Plantas industriais modernas estão suscetíveis a falhas em equipamentos de processo e em instrumentos e componentes da malha de controle. Tais eventos anormais podem acarretar danos a equipamentos, degradação do desempenho do processo e até cenários extremos como a parada da planta e acidentes graves. Em vista disso, o emprego de sistemas de controle tolerante a falhas visa a elevar o grau de confiabilidade e segurança do processo por meio do tratamento e mitigação de eventos anormais, evitando que evoluam para situações críticas. Nesse sentido, este trabalho tem como objetivo desenvolver uma técnica de controle reconfigurável tolerante a falhas para processos sujeitos a falhas em atuadores. A presente proposta é baseada em abordagens por atuadores virtuais e ocultação da falha. Essas técnicas consistem no recálculo das ações de controle e na ocultação da falha do ponto de vista do controlador nominal, permitindo que o mesmo seja mantido após a reconfiguração da malha de controle. Na presente proposta, o atuador virtual é baseado na estrutura do controlador preditivo em duas camadas. Uma camada consiste no cálculo de referências para as variáveis de entrada e para o desvio previsto entre o comportamento da planta nominal e com falha. A outra camada, por sua vez, é responsável por conduzir as variáveis de processo para as referências calculadas na etapa anterior. Ambas as camadas são baseadas em problemas de programação quadrática e levam em consideração as restrições do processo, como limites de atuadores e desvios permissíveis em relação ao comportamento nominal da planta. Essa técnica possibilita a consideração de cenários de falhas nos quais não há graus de liberdade suficientes para a manutenção de variáveis controladas em valores desejados. Assim, a estimativa de perturbações permite que novas referências atingíveis sejam calculadas, ainda que haja erros de identificação do modelo pós-falha do processo. Por fim, a estrutura de controle proposta foi aplicada em simulações utilizando um processo de tanques quádruplos, bem como em experimentos conduzidos em uma planta de neutralização de pHAbstract: Modern industrial plants are susceptible to faults in process equipment and in instruments and components of the control loop. Such abnormal events can lead to equipment damage, degradation of process performance and even extreme scenarios such as plant shutdown and serious accidents. Thus, the use of fault-tolerant control systems aims to increase process reliability and safety by treating and mitigating abnormal events, preventing them from evolving to critical situations. In this sense, this work aims to develop a reconfigurable fault tolerant control technique for processes subject to actuator faults. The present proposal is based on the virtual actuator and fault hiding approaches. These techniques consist of recomputing control actions and hiding the fault from the nominal controller perspective, allowing it to be maintained after the control loop reconfiguration. We propose a virtual actuator based on the two-layer model predictive control structure. One layer consists of calculating references for input variables and for the predicted deviation between the nominal and faulty plant behaviors. The other layer, in turn, is responsible for driving process variables to the references calculated in the previous step. Both layers are based on quadratic programming problems and take into account process constraints such as actuator limits and permissible deviations from the nominal plant behavior. This technique allows the consideration of fault scenarios in which there are not enough degrees of freedom for the maintenance of controlled variables in desired values. Thus, disturbance estimation allows the calculation of new achievable references, even though there are identification errors in the post-fault model. Finally, the proposed control structure has been applied to an experimental pH neutralization plant. Finally, the proposed control structure was applied in simulations to a quadruple-tank process as well as in experiments conducted in a pH neutralization plantMestradoSistemas de Processos Quimicos e InformaticaMestre em Engenharia Química130952/2015-0CNP

Combined design of disturbance model and observer for offset-free model predictive control

This note presents a method for the combined design of an integrating disturbance model and of the observer (for the augmented system) to be used in offset-free model predictive controllers. A dynamic observer is designed for the original (nonaugmented) system by solving an Hprop control problem aimed at minimizing the effect of unmeasured disturbances and plant/model mismatch on the output prediction error. It is shown that, when offset-free control is sought, the dynamic observer is equivalent to choosing an integrating disturbance model and an observer for the augmented system. An example of a chemical reactor shows the main features and benefits of the proposed method

이동블록 및 잔류편차 제거 모델예측제어 기법의 최적성 향상

학위논문(박사)--서울대학교 대학원 :공과대학 화학생물공학부,2020. 2. 이종민.Model predictive control (MPC) is a receding horizon control which derives finite-horizon optimal solution for current state on-line by solving an optimal control problem. MPC has had a tremendous impact on both industrial and control research areas. There are several outstanding issues in MPC. MPC has to solve the optimization problem within a sampling period so that the reduction of on-line computational complexity is a one of the main research subject in MPC. Another major issue is model-plant mismatch due to the model based predictive approach so that offset-free tracking schemes by compensating model-plant mismatch or unmeasured disturbance has been developed. In this thesis, we focused on the optimality performance of move blocking which fixes the decision variables over arbitrary time intervals to reduce computational load for on-line optimization in MPC and disturbance estimator approach based offset-free MPC which is the most standardly used method to accomplish offset-free tracking in MPC. We improve the optimality performance of move blocked MPC in two ways. The first scheme provides a superior base sequence by linearly interpolating complementary base sequences, and the second scheme provides a proper time-varying blocking structure with semi-explicit approach. Moreover, we improve the optimality performance of offset-free MPC by exploiting learned model-plant mismatch compensating signal from estimated disturbance data. With the proposed schemes, we efficiently improve the optimality performance while guaranteeing the recursive feasibility and closed-loop stability.모델예측제어는 현재 시스템 상태에 대한 유한 구간 최적해를 도출하는 온라인 이동 구간 제어 방식이다. 모델예측제어는 피드백을 통한 공정 동특성과 제약 조건을 효과적으로 반영하는 장점으로 인해 산업 및 제어 연구 분야에 큰 영향을 미쳤다. 이러한 모델예측제어에는 몇 가지 해결되어야 할 문제가 있다. 모델예측제어에서는 샘플링 기간 내에 최적화 문제를 풀어내야 하기 때문에, 온라인 계산 복잡성의 감소가 주요 연구 주제 중 하나로 활발히 연구되고 있다. 또 다른 주요 문제는 모델에 기반한 예측을 이용하는 접근 방식으로 인해 모델-플랜트 불일치로 인한 오차를 해결해야 한다는 점이며, 모델 플랜트 불일치 또는 측정되지 않은 외란을 보상하여 잔류편차 없이 참조신호를 추적하는 연구가 활발히 이루어지고 있다. 이 논문에서는 모델예측제어에서의 온라인 최적화를 위한 계산 부하를 줄이기 위해 임의의 시간 간격에 걸쳐 결정 변수를 고정시키는 이동 블록 전략의 최적성 향상에 중점을 두었으며, 또한 잔류편차를 제거하기 위해 가장 표준적으로 사용되는 외란 추정기를 이용한 잔류편차-제거 모델예측제어 기법의 최적성 향상에 중점을 두었다. 이 논문에서는 이동 블록 모델예측제어의 최적 성능을 향상시키기 위한 두 가지 전략을 제시한다. 첫 번째 전략은 이동 블록 전략에서 일반적으로 고정된 채로 사용되는 기반 시퀀스를 상호 보완적인 두 기반 시퀀스의 선형 보간으로 대체함으로써 보다 우수한 기반 시퀀스를 제공하며, 두 번째 전략은 준-명시적 접근법을 활용하여 현재 시스템 상태에 적절한 시변 블록 구조를 온라인에서 제공한다. 또한, 잔류편차-제거 모델예측제어 기법의 최적 성능을 향상시키기 위해 추정 외란 데이터로부터 학습된 모델-플랜트 불일치 보상 신호를 온라인에서 이용하는 전략을 제안하였다. 제안된 세 가지 기법을 통해 모델예측제어의 반복적 실현가능성과 폐쇄-루프 안정성을 보장하면서 최적 성능을 효율적으로 개선 하였다.1. Introduction 1 2. Move-blocked model predictive control with linear interpolation of base sequences 5 2.1 Introduction 5 2.2 Preliminaries 9 2.2.1 MPC formulation 9 2.2.2 Move blocking 12 2.2.3 Move blocked MPC (MBMPC) 15 2.3 Move blocking schemes 16 2.3.1 Previous solution based offset blocking 17 2.3.2 LQR solution based offset blocking 18 2.4 Interpolated solution based move blocking 20 2.4.1 Interpolated solution based MBMPC 20 2.4.2 QP formulation 26 2.5 Numerical examples 29 2.5.1 Example 1 (Feasible region) 30 2.5.2 Example 2 (Performance in regulation problem) 33 2.5.3 Example 3 (Performance in tracking problem) 36 3. Move-blocked model predictive control with time-varying blocking structure by semi-explicit approach 43 3.1 Introduction 43 3.2 Problem formulation 46 3.3 Move blocked MPC 48 3.3.1 Move blocking scheme 48 3.3.2 Implementation of move blocking 51 3.4 Semi-explicit approach for move blocked MPC 53 3.4.1 Off-line generation of critical region 56 3.4.2 On-line MPC scheme with critical region search 60 3.4.3 Property of semi-explicit move blocked MPC 62 3.5 Numerical examples 70 3.5.1 Example 1 (Regulation problem) 71 3.5.2 Example 2 (Tracking problem) 77 4. Model-plant mismatch learning offset-free model predictive control 83 4.1 Introduction 83 4.2 Offset-free MPC: Disturbance estimator approach 86 4.2.1 Preliminaries 86 4.2.2 Disturbance estimator and controller design 87 4.2.3 Offset-free tracking condition 89 4.3 Model-plant mismatch learning offset-free MPC 91 4.3.1 Model-plant mismatch learning 92 4.3.2 Application of learned model-plant mismatch 97 4.3.3 Robust asymptotic stability of model-plant mismatch learning offset-free MPC 102 4.4 Numerical example 117 4.4.1 System with random set-point 120 4.4.2 Transformed system 125 4.4.3 System with multiple random set-points 128 5. Concluding remarks 134 5.1 Move-blocked model predictive control with linear interpolation of base sequences 134 5.2 Move-blocked model predictive control with time-varying blocking structure by semi-explicit approach 135 5.3 Model-plant mismatch learning offset-free model predictive control 136 5.4 Conclusions 138 5.5 Future work 139 Bibliography 145Docto

Advanced Predictive Control Strategies for More Electric Aircraft

Next generation aircraft designs are incorporating increasingly complex electrical power distribution systems to address growing demands for larger and faster electrical power loads. This dissertation develops advanced predictive control strategies for coordinated management of the engine and power subsystems of such aircraft. To achieve greater efficiency, reliability and performance of a More Electric Aircraft (MEA) design static and dynamic interactions between its engine and power subsystems need to be accounted for and carefully handled in the control design. In the pursued approach, models of the subsystems and preview of the power loads are leveraged by predictive feedback controllers to coordinate subsystem operation and achieve improved performance of the MEA system while enforcing state and input constraints. More specifically, this dissertation contains the following key developments and contributions. Firstly, models representing the engine and power subsystems of the MEA, including their interactions, are developed. The engine is a dual-spool turbojet that converts fuel into thrust out of the nozzle and mechanical power at the shafts. Electrical generators extract some of this power and convert it into electricity that is supplied to a High Voltage DC bus to support connected loads, with the aid of a battery pack for smoothing voltage transients. The control objective in this MEA system is to actuate the engine and power subsystem inputs to satisfy demands for thrust and electrical power while enforcing constraints on compressor surge and bus voltage deviations. Secondly, disturbance rejection, power flow coordination, and anticipation of the changes in power loads are considered for effective MEA control. A rate-based formulation of Model Predictive Control (MPC) allowing for offset free tracking is proposed. Centralized control is demonstrated to result in better thrust tracking performance in the presence of compressor surge constraints as compared to decentralized control. Forecast of changes in the power load allows the control to act in advance and reduce bus voltage excursions. Thirdly, distributed MPC strategies are developed which account for subsystem privacy requirements and differences in subsystem controller update rates. This approach ensures coordination between subsystem controllers based on limited information exchange and exploits the Alternating Direction Method of Multipliers. Simulations demonstrate that the proposed approach outperforms the decentralized controller and closely matches the performance of a fully centralized solution. Finally, a stochastic approach to load preview based on a Markov chain representation of a military aircraft mission is proposed. A scenario based MPC is then exploited to minimized expected performance cost while enforce constraints over all scenarios. Simulation based comparisons indicate that this scenario based MPC performs similarly to an idealized controller that exploits exact knowledge of the future and outperforms a controller without preview.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/150003/1/wdunham_1.pd

Modeling, Analysis and Control of DC Hybrid Power Systems.

All electric ships are featured with integrated power systems which combine electric propulsion technology with heterogeneous power generation and distribution technologies to form one single electrical platform. The auxiliary and main power generation system form an isolated hybrid power system to feed the ship service loads and to meet the propulsion power requirement. Although for decades, the methodologies for power converter control have been explored in many publications, the modeling, analysis, and control of hybrid power systems with multiple power converters remains an interesting open problem, leading to its exclusive focus in this dissertation. Along with the opportunities introduced by hybrid power systems, the inter-connectivity and complexity represent a major system analysis, design and optimization challenge, calling for the development of effective tools. Therefore, a comprehensive testbed is developed. Moreover, component level modeling, analysis and modulation strategy development are performed to ensure system level performance. A new power flow model for the dual active bridge converter is derived. The new model provides a physical interpretation of the observed phenomena and identifies other characteristics that are validated by experiments. To overcome the drawbacks of traditional modulation strategies, a novel modulation strategy is developed for the dual active bridge converter. The experimental results verified that, if the new strategy is used to modulate the dual active bridge converter, this testbed can be used as an effective tool for optimal power management algorithm development for the hybrid power systems. The development of advanced control algorithms, together with the increased computational power of microprocessors, enables us to deal with the control problem from a new perspective. In this dissertation, the voltage regulation problem for a full bridge DC/DC converter is formulated as both a linear and a nonlinear Model Predictive Control (MPC) problem with a nonlinear constraint that captures the peak current protection requirement. The experimental results reveal that both the MPC algorithms can successfully achieve voltage regulation and peak current protection. The successful implementation of the MPC schemes on the full bridge DC/DC converter paves the way for future system-level advanced control algorithm development for hybrid power systems.Ph.D.Naval Architecture & Marine EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/75813/1/yhxie_1.pd

Automation and Control Architecture for Hybrid Pipeline Robots

The aim of this research project, towards the automation of the Hybrid Pipeline Robot (HPR), is the development of a control architecture and strategy, based on reconfiguration of the control strategy for speed-controlled pipeline operations and self-recovering action, while performing energy and time management. The HPR is a turbine powered pipeline device where the flow energy is converted to mechanical energy for traction of the crawler vehicle. Thus, the device is flow dependent, compromising the autonomy, and the range of tasks it can perform. The control strategy proposes pipeline operations supervised by a speed control, while optimizing the energy, solved as a multi-objective optimization problem. The states of robot cruising and self recovering, are controlled by solving a neuro-dynamic programming algorithm for energy and time optimization, The robust operation of the robot includes a self-recovering state either after completion of the mission, or as a result of failures leading to the loss of the robot inside the pipeline, and to guaranteeing the HPR autonomy and operations even under adverse pipeline conditions Two of the proposed models, system identification and tracking system, based on Artificial Neural Networks, have been simulated with trial data. Despite the satisfactory results, it is necessary to measure a full set of robot’s parameters for simulating the complete control strategy. To solve the problem, an instrumentation system, consisting on a set of probes and a signal conditioning board, was designed and developed, customized for the HPR’s mechanical and environmental constraints. As a result, the contribution of this research project to the Hybrid Pipeline Robot is to add the capabilities of energy management, for improving the vehicle autonomy, increasing the distances the device can travel inside the pipelines; the speed control for broadening the range of operations; and the self-recovery capability for improving the reliability of the device in pipeline operations, lowering the risk of potential loss of the robot inside the pipeline, causing the degradation of pipeline performance. All that means the pipeline robot can target new market sectors that before were prohibitive

Modeling and Intelligent Control for Spatial Processes and Spatially Distributed Systems

Dynamical systems are often characterized by their time-dependent evolution, named temporal dynamics. The space-dependent evolution of dynamical systems, named spatial dynamics, is another important domain of interest for many engineering applications. By studying both the spatial and temporal evolution, novel modeling and control applications may be developed for many industrial processes. One process of special interest is additive manufacturing, where a three-dimensional object is manufactured in a layer-wise fashion via a numerically controlled process. The material is printed over a spatial domain in each layer and subsequent layers are printed on top of each other. The spatial dynamics of the printing process over the layers is named the layer-to-layer spatial dynamics. Additive manufacturing provides great flexibility in terms of material selection and design geometry for modern manufacturing applications, and has been hailed as a cornerstone technology for smart manufacturing, or Industry 4.0, applications in industry. However, due to the issues in reliability and repeatability, the applicability of additive manufacturing in industry has been limited. Layer-to-layer spatial dynamics represent the dynamics of the printed part. Through the layer-to-layer spatial dynamics, it is possible to represent the physical properties of the part such as dimensional properties of each layer in the form of a heightmap over a spatial domain. Thus, by considering the spatial dynamics, it is possible to develop models and controllers for the physical properties of a printed part. This dissertation develops control-oriented models to characterize the spatial dynamics and layer-to-layer closed-loop controllers to improve the performance of the printed parts in the layer-to-layer spatial domain. In practice, additive manufacturing resources are often utilized as a fleet to improve the throughput and yield of a manufacturing system. An additive manufacturing fleet poses additional challenges in modeling, analysis, and control at a system-level. An additive manufacturing fleet is an instance of the more general class of spatially distributed systems, where the resources in the system (e.g., additive manufacturing machines, robots) are spatially distributed within the system. The goal is to efficiently model, analyze, and control spatially distributed systems by considering the system-level interactions of the resources. This dissertation develops a centralized system-level modeling and control framework for additive manufacturing fleets. Many monitoring and control applications rely on the availability of run-time, up-to-date representations of the physical resources (e.g., the spatial state of a process, connectivity and availability of resources in a fleet). Purpose-driven digital representations of the physical resources, known as digital twins, provide up-to-date digital representations of resources in run-time for analysis and control. This dissertation develops an extensible digital twin framework for cyber-physical manufacturing systems. The proposed digital twin framework is demonstrated through experimental case studies on abnormality detection, cyber-security, and spatial monitoring for additive manufacturing processes. The results and the contributions presented in this dissertation improve the performance and reliability of additive manufacturing processes and fleets for industrial applications, which in turn enables next-generation manufacturing systems with enhanced control and analysis capabilities through intelligent controllers and digital twins.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169635/1/baltaefe_1.pd