Data based predictive control: Application to water distribution networks

In this thesis, the main goal is to propose novel data based predictive controllers to cope with complex industrial infrastructures such as water distribution networks. This sort of systems have several inputs and out- puts, complicate nonlinear dynamics, binary actuators and they are usually perturbed by disturbances and noise and require real-time control implemen- tation. The proposed controllers have to deal successfully with these issues while using the available information, such as past operation data of the process, or system properties as fading dynamics. To this end, the control strategies presented in this work follow a predic- tive control approach. The control action computed by the proposed data- driven strategies are obtained as the solution of an optimization problem that is similar in essence to those used in model predictive control (MPC) based on a cost function that determines the performance to be optimized. In the proposed approach however, the prediction model is substituted by an inference data based strategy, either to identify a model, an unknown control law or estimate the future cost of a given decision. As in MPC, the proposed strategies are based on a receding horizon implementation, which implies that the optimization problems considered have to be solved online. In order to obtain problems that can be solved e ciently, most of the strategies proposed in this thesis are based on direct weight optimization for ease of implementation and computational complexity reasons. Linear convex combination is a simple and strong tool in continuous domain and computational load associated with the constrained optimization problems generated by linear convex combination are relatively soft. This fact makes the proposed data based predictive approaches suitable to be used in real time applications. The proposed approaches selects the most adequate information (similar to the current situation according to output, state, input, disturbances,etc.), in particular, data which is close to the current state or situation of the system. Using local data can be interpreted as an implicit local linearisation of the system every time we solve the model-free data driven optimization problem. This implies that even though, model free data driven approaches presented in this thesis are based on linear theory, they can successfully deal with nonlinear systems because of the implicit information available in the database. Finally, a learning-based approach for robust predictive control design for multi-input multi-output (MIMO) linear systems is also presented, in which the effect of the estimation and measuring errors or the effect of unknown perturbations in large scale complex system is considered

특정 점의 추적을 위한 모델예측제어가 결합된 새로운 반복학습제어 기법

학위논문 (박사)-- 서울대학교 대학원 : 화학생물공학부, 2017. 2. 이종민.본 논문에서는 제약조건이 있는 다변수 회분식 공정의 제어를 위해 반복학습제어(Iterative learning control, ILC)와 모델예측제어(Model predictive control, MPC)를 결합한 반복학습 모델예측제어(Iterative learning model predictive control, ILMPC)를 다룬다. 일반적인 ILC는 모델의 불확실성이 있더라도 이전 회분의 정보를 이용해 학습하기 때문에 출력을 기준궤적에 수렴시킬 수 있다. 하지만 기본적으로 개루프 제어이기 때문에 실시간 외란을 제거할 수 없다. MPC는 이전 회분의 정보를 이용하지 않기 때문에 모든 회분에서 동일한 성능을 보이며 모델의 정확도에 크게 의존한다. 본 논문에서 ILC와 MPC의 모든 장점을 포함하는 ILMPC를 제안한다. 많은 회분식 또는 반복 공정에서 출력은 모든 시간에서의 기준궤적을 추적할 필요가 없다. 따라서 본 논문에서는 원하는 점에만 수렴할 수 있는 새로운 ILMPC 기법을 제안한다. 제안한 기법을 사용할 경우 원하는 점을 지나는 기준궤적을 만드는 과정이 필요 없게 된다. 또한 본 논문은 점대점 추적, 반복 학습, 제약조건, 실시간 외란 제거 등의 성능을 보이기 위한 다양한 예제를 제공한다.In this thesis, we study an iterative learning control (ILC) technique combined with model predictive control (MPC), called the iterative learning model predictive control (ILMPC), for constrained multivariable control of batch processes. Although the general ILC makes the outputs converge to reference trajectories under model uncertainty, it uses open-loop control within a batchthus, it cannot reject real-time disturbances. The MPC algorithm shows identical performance for all batches, and it highly depends on model quality because it does not use previous batch information. We integrate the advantages of the two algorithms. In many batch or repetitive processes, the output does not need to track all points of a reference trajectory. We propose a novel ILMPC method which can only consider the desired reference points, not an entire reference trajectory. It does not require to generate a reference trajectory which passes through the specific desired points. Numerical examples are provided to demonstrate the performances of the suggested approach on point-to-point tracking, iterative learning, constraints handling, and real-time disturbance rejection.1. Introduction 1 1.1 Background and Motivation 1 1.2 Literature Review 4 1.2.1 Iterative Learning Control 4 1.2.2 Iterative Learning Control Combined with Model Predictive Control 15 1.2.3 Iterative Learning Control for Point-to-Point Tracking 17 1.3 Major Contributions of This Thesis 18 1.4 Outline of This Thesis 19 2. Iterative Learning Control Combined with Model Predictive Control 22 2.1 Introduction 22 2.2 Prediction Model for Iterative Learning Model Predictive Control 25 2.2.1 Incremental State-Space Model 25 2.2.2 Prediction Model 30 2.3 Iterative Learning Model Predictive Controller 34 2.3.1 Unconstrained ILMPC 34 2.3.2 Constrained ILMPC 35 2.3.3 Convergence Property 37 2.3.4 Extension for Disturbance Model 42 2.4 Numerical Illustrations 44 2.4.1 (Case 1) Unconstrained and Constrained Linear SISO System 45 2.4.2 (Case 2) Constrained Linear MIMO System 49 2.4.3 (Case 3) Nonlinear Batch Reactor 53 2.5 Conclusion 59 3. Iterative Learning Control Combined with Model Predictive Control for Non-Zero Convergence 60 3.1 Iterative Learning Model Predictive Controller for Nonzero Convergence 60 3.2 Convergence Analysis 63 3.2.1 Convergence Analysis for an Input Trajectory 63 3.2.2 Convergence Analysis for an Output Error 65 3.3 Illustrative Example 71 3.4 Conclusions 75 4. Iterative Learning Control Combined with Model Predictive Control for Tracking Specific Points 77 4.1 Introduction 77 4.2 Point-to-Point Iterative Learning Model Predictive Control 79 4.2.1 Extraction Matrix Formulation 79 4.2.2 Constrained PTP ILMPC 82 4.2.3 Iterative Learning Observer 86 4.3 Convergence Analysis 89 4.3.1 Convergence of Input Trajectory 89 4.3.2 Convergence of Error 95 4.4 Numerical Examples 98 4.4.1 Example 1 (Linear SISO System with Disturbance) 98 4.4.2 Example 2 (Linear SISO System) 104 4.4.3 Example 3 (Comparison between the Proposed PTP ILMPC and PTP ILC) 107 4.4.4 Example 4 (Nonlinear Semi-Batch Reactor) 113 4.5 Conclusion 119 5. Stochastic Iterative Learning Control for Batch-varying Reference Trajectory 120 5.1 Introduction 121 5.2 ILC for Batch-Varying Reference Trajectories 123 5.2.1 Convergence Property for ILC with Batch-Varying Reference Trajectories 123 5.2.2 Iterative Learning Identification 126 5.2.3 Deterministic ILC Controller for Batch-Varying Reference Trajectories 129 5.3 ILC for LTI Stochastic System with Batch-Varying Reference Trajectories 132 5.3.1 Approach1: Batch-Domain Kalman Filter-Based Approach 133 5.3.2 Approach2: Time-Domain Kalman Filter-Based Approach 137 5.4 Numerical Examples 141 5.4.1 Example 1 (Random Reference Trajectories 141 5.4.2 Example 2 (Particular Types of Reference Trajectories 149 5.5 Conclusion 151 6. Conclusions and Future Works 156 6.1 Conclusions 156 6.2 Future work 157 Bibliography 158 초록 170Docto

Online Interval Type-2 Fuzzy Extreme Learning Machine Applied to 3D Path Following for Remotely Operated Underwater Vehicles

In marine missions that involve 3D path following tasks, the overall goal of Underwater Vehicles (UVs) is the successful completion of a path previously specified by the operator. This implies that the path must be followed by the UV as closely as possible and arrive at a location for collection by a vessel. In this paper, an Online Interval Type-2 Fuzzy Extreme Learning Machine (OIT2-FELM) is suggested to achieve a robust following behaviour along a predefined 3D path using a Remotely Operated Underwater Vehicle (ROV). The proposed machine is a fast sequential learning scheme to the training of a more generalised model of TSK Interval Type-2 Fuzzy Inference Systems (TSK IT2 FISs) equivalent to Single Layer Feedforward Neural Networks (SLFNs). Learning new input data in the OIT2-FELM can be done one-by-one or chunk-by-chunk with a fixed or varying size. The OIT2-FELM is implemented in a hierarchical navigation strategy (HNS) as the main guidance mechanism to infer local control motions and to provide the ROV with the necessary autonomy to complete a predefined 3D path. For local path-planning, the OIT2-FELM performs signal classification for obstacle avoidance and target detection based on data collected by an on-board scan sonar. To evaluate the performance of the proposed OIT2-FELM, two different experiments are suggested. First, a number of benchmark problems in the field of non-linear system identification, regression and classification problems are used. Secondly, a number of experiments to the completion of a predefined 3D path using an ROV is implemented. Compared to other fuzzy strategies, the OIT2-FELM offered two significant capabilities. On the one hand, the OIT2-FELM provides a better treatment of uncertainty and noisy signals in underwater environments while improving the ROV's performance. Secondly, online learning in OIT2-FELM allows continuous knowledge discovery from survey data to infer the surroundings of the ROV. Experiment results to the completion of 3D paths show the effectiveness of the proposed approach to handle uncertainty and produce reasonable classification predictions (∼90.5% accuracy in testing data).</p

Statistically Evolving Fuzzy Inference System for Non-Gaussian Noises

Non-Gaussian noises always exist in the nonlinear system, which usually lead to inconsistency and divergence of the regression and identification applications. The conventional evolving fuzzy systems (EFSs) in common sense have succeeded to conquer the uncertainties and external disturbance employing the specific variable structure characteristic. However, non-Gaussian noises would trigger the frequent changes of structure under the transient criteria, which severely degrades performance. Statistical criterion provides an informed choice of the strategies of the structure evolution, utilizing the approximation uncertainty as the observation of model sufficiency. The approximation uncertainty can be always decomposed into model uncertainty term and noise term, and is suitable for the non-Gaussian noise condition, especially relaxing the traditional Gaussian assumption. In this paper, a novel incremental statistical evolving fuzzy inference system (SEFIS) is proposed, which has the capacity of updating the system parameters, and evolving the structure components to integrate new knowledge in the new process characteristic, system behavior, and operating conditions with non-Gaussian noises. The system generates a new rule based on the statistical model sufficiency which gives so insight into whether models are reliable and their approximations can be trusted. The nearest rule presents the inactive rule under the current data stream and further would be deleted without losing any information and accuracy of the subsequent trained models when the model sufficiency is satisfied. In our work, an adaptive maximum correntropy extend Kalman filter (AMCEKF) is derived to update the parameters of the evolving rules to cope with the non-Gaussian noises problems to further improve the robustness of parameter updating process. The parameter updating process shares an estimate of the uncertainty with the criteria of the structure evolving process to make the computation less of a burden dramatically. The simulation studies show that the proposed SEFIS has faster learning speed and is more accurate than the existing evolving fuzzy systems (EFSs) in the case of noise-free and noisy conditions