Retrospective Cost Adaptive Control of Uncertain Hammerstein Systems.

This dissertation extends retrospective cost adaptive control (RCAC) by broadening its applicability to nonlinear systems. Specifically, we consider command following and disturbance rejection for uncertain Hammerstein systems. All real-world control systems must operate subject to constraints on the allowable control inputs. We use convex optimization to perform the retrospective input optimization, provided the saturation levels are known. The use of convex optimization bounds the magnitude of the retrospectively optimized input and thereby influences the controller update to satisfy the control bounds. We demonstrate this technique on illustrative numerical examples involving single and multiple inputs. In particular, this technique is applied to a multi-rotor helicopter with constraints on the total thrust magnitude and inclination of the rotor plane. We develop RCAC for uncertain Hammerstein systems with odd, even, or arbitrary nonlinearities by constructing auxiliary nonlinearities to account for the non-monotonic input nonlinearities. The purpose of the auxiliary nonlinearities is to ensure that RCAC is applied to a Hammerstein system with a globally nondecreasing composite input nonlinearity. We assume that the linear plant is either asymptotically stable or minimum-phase, and only one Markov parameter of the linear plant is known. The input nonlinearity is uncertain. The required modeling information for the input nonlinearity includes the intervals of monotonicity as well as values of the nonlinearity that determine overlapping segments of the range of the nonlinearity within each interval of monotonicity. Although RCAC is able to tune the linear controller to the command signal and nonlinear characteristics of the plant, the ability of the linear controller to produce accurate command following is limited by the distortion introduced by the nonlinearities. The linear controller structure of RCAC is replaced by a NARMAX (nonlinear ARMAX) controller structure, where the basis functions in the NARMAX controller are chosen by the user, and the controller coefficients appear linearly. To account for the case in which the input nonlinearity is uncertain, we investigate the performance of retrospective cost adaptive NARMAX control (RCNAC) in the case of uncertainty, an approximate input nonlinearity, called the ersatz nonlinearity, can be used by RCANC for adaptation.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/100042/1/yanjin_1.pd

Retrospective Cost Optimization for Adaptive State Estimation, Input Estimation, and Model Refinement

AbstractRetrospective cost optimization was originally developed for adaptive control. In this paper, we show how this technique is applicable to three distinct but related problems, namely, state estimation, input estimation, and model refinement. To illustrate these techniques, we give two examples. In the first example, retrospective cost model refinement is used with synthetic data to estimate the cooling dynamics that are missing from a model of the ionosphere-thermosphere. In the second example, retrospective cost adaptive state estimation is used with data from a satellite to estimate a solar driver in the ionosphere- thermosphere, with performance gauged by using data from a second satellite

Predictor-based robust control of dead-time processes

Tese (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2015.Esta tese trata do problema de controle robusto de sistemas não-lineares com atraso utilizando estruturas de compensação de atraso. Como já descrito na literatura, três são os problemas causados pela presença de atraso de transporte: (i) os efeitos das perturbações não são notados até se passar o tempo do atraso, (ii) o efeito da ação de controle demora para ser notado na variável controlada, e (iii) a ação de controle que é aplicada no instante atual tenta corrigir uma situação que se originou tempos atrás. Uma das mais utilizadas soluções para evitar (ou atenuar) esses efeitos é o uso do Preditor de Smith (SP - Smith Predictor). Preditores são estruturas que permitem o controle de processos com atraso a partir de um modelo sem atraso, o que simplifica o ajuste do controlador. Uma importante propriedade do Preditor de Smith vem do fato de que a robustez do sistema de malha fechada resultante não depende do valor nominal do atraso. Esta propriedade, no entanto, não é válida para qualquer preditor. Por exemplo, algoritmos de controle preditivo (MPC - Model Based Predictive Controllers) definem implicitamente estruturas preditoras, mas, como já foi mostrado na literatura, no caso específico do GPC (Generalized Predictive Control), o preditor ótimo definido implicitamente faz com que a robustez do sistema dependa do valor nominal do atraso. Também já havia sido mostrado que, substituindo este preditor implícito por um Preditor de Smith Filtrado (FSP - Filtered Smith Predictor), resulta em um controlador mais robusto que herda as características do SP. Assim, os objetivos desta tese são: (i) Estudo do algoritmo preditivo Dynamic Matrix Control (DMC), através de uma estrutura FSP, e propor modificações que permitam melhorar a rejeição de perturbações e/ou aumentar a robustez do sistema; (ii) análise e implementação de uma estrutura baseada no FSP para sistemas não-lineares. Os algoritmos de controle preditivo, ou MPC, emergiram durante as últimas três décadas como uma poderosa solução de controle, e obtiveram um impacto significativo na indústria, como já mostrado em diversos trabalhos. No entanto, apesar de grandes avanços teóricos e do fato de que os processos industriais são, em geral, não lineares, a maioria das técnicas de controle aplicadas na indústria são baseadas em modelos lineares. Algoritmos MPC simples baseados em modelos de resposta ao degrau (ou impulsiva) sem garantia de estabilidade são os mais comuns na indústria, principalmente em refinarias e plantas petroquímicas. Algumas razões para isso são: (i) os processos possuem comportamento estável em malha aberta e ajustando adequadamente os parâmetros do controlador é possível obter a estabilidade do sistema em malha fechada, e (ii) modelos lineares são suficientes quando o processo está operando próximo de um ponto de operação. Desta forma, a análise das propriedades de malha fechada desses controladores, como velocidade de rejeição de perturbação e robustez, é muito importante para a indústria de processos, já que é possível obter modificações simples e úteis que melhoram o desempenho de aplicações reais. Assim, neste trabalho, o algoritmo preditivo DMC será interpretado através da estrutura FSP de forma que os efeitos do atraso no sistema de malha fechada possam ser entendidos. Esta abordagem foi escolhida por permitir que várias técnicas de sintonia já desenvolvidas para o FSP possam ser aplicadas ao DMC. Será mostrado que o algoritmo DMC precisa apenas de pequenas modificações para adquirir as vantagens fornecidas pela estrutura FSP. O segundo tópico deste trabalho trata de estruturas preditoras para sistemas não-lineares. Seguindo as ideias propostas para o caso linear, neste trabalho será proposto o Preditor de Smith Filtrado para Sistemas Não-Lineares (NLFSP - Nonlinear Filtered Smith Predictor), que permitirá melhorar as características de robustez e rejeição de perturbação de sistemas não lineares. Já há trabalhos evidenciando algumas vantagens do FSP para sistemas não-lineares, no entanto não há provas nem uma análise formal de suas propriedades. O FSP linear possui as seguintes características: (i) a resposta nominal para mudanças de referência não é afetada pela inserção do filtro de predição; (ii) a robustez pode ser melhorada ajustando o filtro adequadamente; (iii) o filtro de predição pode ser ajustado para acelerar a rejeição de perturbações. Vários exemplos de simulação são apresentados no documento para ilustrar os resultados teóricos apresentados. Em particular, se aplicam os resultados a processos da indústria do petróleo e petroquímica onde os controladores preditivos têm um grande impacto.Abstract : This thesis deals with the analysis and design of predictor-based robust controllers for processes with dead time. The main objectives are: (i) to analyze the effect of the predictor structure in the closed-loop behavior and robustness of linear and nonlinear controllers; (ii) to propose better predictor structures to improve robustness and performance of control loops; (iii) to apply the results in simulated and real industrial processes, mainly for the petroleum industry. The results of this thesis are: an improvement on the well-known Dynamic Matrix Control (DMC) algorithm, from the Model Predictive Control (MPC) family, and a predictor for nonlinear systems with time delay based on the Smith Predictor. Concerning the MPC, in this work, an improved industrial MPC controller based on the widely used DMC approach is presented. A MIMO filter is included in the prediction model of the controller in order to achieve two important advantages when compared to traditional industrial DMC: (i) disturbance rejection response can be speeded up and (ii) robustness can be improved, mainly when errors in the estimation of the delays are considered. The filter properties are demonstrated by means of an equivalent analysis of the unconstrained DMC using a dead time compensation (DTC) approach, namely the Filtered Smith Predictor. Moreover, implementation and tuning of the filter is simple and intuitive. Simulation results using a water-methanol distillation column are presented to illustrate the advantages of the proposed approach. For the case of nonlinear processes with time delay, a Nonlinear Filtered Smith Predictor (NLFSP) structure is proposed for nonlinear systems. It will be shown that the NLFSP maintains the characteristics of the linear Smith Predictor and that, with appropriate tuning, it can increase the robustness of the closed-loop system. The NLFSP is applied to various examples and case studies to demonstrate these characteristics

Modeling and Control of Magnetostrictive-actuated Dynamic Systems

Magnetostrictive actuators featuring high energy densities, large strokes and fast responses appear poised to play an increasingly important role in the field of nano/micro positioning applications. However, the performance of the actuator, in terms of precision, is mainly limited by 1) inherent hysteretic behaviors resulting from the irreversible rotation of magnetic domains within the magnetostrictive material; and 2) dynamic responses caused by the inertia and flexibility of the magnetostrictive actuator and the applied external mechanical loads. Due to the presence of the above limitations, it will prevent the magnetostrictive actuator from providing the desired performance and cause the system inaccuracy. This dissertation aims to develop a modeling and control methodology to improve the control performance of the magnetostrictive-actuated dynamic systems. Through thorough experimental investigations, a dynamic model based on the physical principle of the magnetostrictive actuator is proposed, in which the nonlinear hysteresis effect and the dynamic behaviors can both be represented. Furthermore, the hysteresis effect of the magnetostrictive actuator presents asymmetric characteristics. To capture these characteristics, an asymmetric shifted Prandtl-Ishlinskii (ASPI) model is proposed, being composed by three components: a Prandtl-Ishlinskii (PI) operator, a shift operator and an auxiliary function. The advantages of the proposed model are: 1) it is able to represent the asymmetric hysteresis behavior; 2) it facilitates the construction of the analytical inverse; 3) the analytical expression of the inverse compensation error can also be derived. The validity of the proposed ASPI model and the entire dynamic model was demonstrated through experimental tests on the magnetostrictive-actuated dynamic system. According to the proposed hysteresis model, the inverse compensation approach is applied for the purpose of mitigating the hysteresis effect. However, in real systems, there always exists a modeling error between the hysteresis model and the true hysteresis. The use of an estimated hysteresis model in deriving the inverse compensator will yield some degree of hysteresis compensation error. This error will cause tracking error in the closed-loop control system. To accommodate such a compensation error, an analytical expression of the inverse compensation error is derived first. Then, a prescribed adaptive control method is developed to suppress the compensation error and simultaneously guaranteeing global stability of the closed loop system with a prescribed transient and steady-state performance of the tracking error. The effectiveness of the proposed control scheme is validated on the magnetostrictive-actuated experimental platform. The experimental results illustrate an excellent tracking performance by using the developed control scheme

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

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

Adaptive Control

Adaptive control has been a remarkable field for industrial and academic research since 1950s. Since more and more adaptive algorithms are applied in various control applications, it is becoming very important for practical implementation. As it can be confirmed from the increasing number of conferences and journals on adaptive control topics, it is certain that the adaptive control is a significant guidance for technology development.The authors the chapters in this book are professionals in their areas and their recent research results are presented in this book which will also provide new ideas for improved performance of various control application problems