Математическая модель модулятора тормозного привода карьерного самосвала с гидравлической антиблокировочной системой

Применение различных методов получения математических моделей позволяет значительно сократить сроки на проектно-конструкторские и исследовательские работы в ходе разработки гидравлических приводов. В настоящее время применение мощных персональных компьютеров дает возможность существенно повысить качество получаемых результатов разрабатываемых математических моде- лей благодаря более детальному описанию процессов, определить структуру разрабатываемого гидравлического привода либо его отдельных элементов и выбрать параметры, оказывающие наибольшее влияние на динамические процессы, протекающие в исследуемом объекте. В данной работе получена математическая модель модулятора антиблокировочной тормозной системы автомобиля особо большой грузоподъемности, представленного в виде системы с сосредоточенными параметрами. Также в работе рассмотрены основные методы составления математических моделей гидроприводов и проанализированы работы, посвященные разработке различных математических моделей, позволяющих описать гидравлический привод с различной степенью точности, что дает возможность выбрать наиболее рациональный способ составления математической модели модулятора гидравлической антиблокировочной тормозной системы автомобиля особо большой грузоподъемности с учетом принятых допущений. Решение системы дифференциальных уравнений, описывающих полученную математическую модель, при помощи численных методов или различных специализированных программных сред, например, таких как Matlab с расширением Simulink, в дальнейшем позволяет исследовать влияние внутренних параметров на динамические процессы в предложенном модуляторе гидравлической антиблокировочной системы и выбирать их наиболее оптимальные значения

Mathematical model of the modulator of the brake drive of a dump truck with a hydraulic anti-lock system

The use of various methods for obtaining mathematical models can significantly reduce the time for design and research work during the development of hydraulic drives. Currently, the use of powerful personal computers makes it possible to significantly improve the quality of the results of the mathematical models being developed due to a more detailed description of the processes, determine the structure of the hydraulic drive being developed or its individual elements and select the parameters that have the greatest impact on the dynamic processes occurring in the object under study. In this paper, a mathematical model of the modulator of the anti-lock braking system of a particularly heavy-duty vehicle, presented in the form of a system with concentrated parameters, is obtained. The paper also discusses the main methods of compiling mathematical models of hydraulic drives and analyzes the work devoted to the development of various mathematical models that describe the hydraulic drive with varying degrees of accuracy, which makes it possible to choose the most rational way to compile a mathematical model of the modulator of the hydraulic anti-lock braking system of a particularly heavy-duty vehicle, taking into account the assumptions made. Solving a system of differential equations describing the resulting mathematical model using numerical methods or various specialized software environments, for example, such as Matlab with the Simulink extension, further allows us to investigate the influence of internal parameters on dynamic processes in the proposed hydraulic anti-lock system modulator and select their most optimal values

Modelling of an electro-hydraulic actutor using extended adaptive distance gap statistic approach

The existence of high degree of non-linearity in Electro-Hydraulic Actuator (EHA) system has imposed a challenging task in developing its model so that effective control algorithm can be proposed. In general, there are two modelling approaches available for EHA system, which are the dynamic equation modelling method and the system identification modelling method. Both approaches have disadvantages, where the dynamic equation modelling is hard to apply and some parameters are difficult to obtain, while the system identification method is less accurate when the system’s nature is complicated with wide variety of parameters, nonlinearity and uncertainties. This thesis presents a new modelling procedure of an EHA system by using fuzzy approach. Two sets of input variables are obtained, where the first set of variables are selected based on mathematical modelling of the EHA system. The reduction of input dimension is done by the Principal Component Analysis (PCA) method for the second set of input variables. A new gap statistic with a new within-cluster dispersion calculation is proposed by introducing an adaptive distance norm in distance calculation. The new gap statistic applies Gustafson Kessel (GK) clustering algorithm to obtain the optimal number of cluster of each input. GK clustering algorithm also provides the location and characteristic of every cluster detected. The information of input variables, number of clusters, cluster’s locations and characteristics, and fuzzy rules are used to generate initial Fuzzy Inference System (FIS) with Takagi-Sugeno type. The initial FIS is trained using Adaptive Network Fuzzy Inference System (ANFIS) hybrid training algorithm with an identification data set. The ANFIS EHA model and ANFIS PCA model obtained using proposed modelling procedure, have shown the ability to accurately estimate EHA system’s performance at 99.58% and 99.11% best fitting accuracy compared to conventional linear Autoregressive with External Input (ARX) model at 94.97%. The models validation result on different data sets also suggests high accuracy in ANFIS EHA and ANFIS PCA model compared to ARX model

Header height control of combine harvester via robust feedback linearization

Studies have shown that feedback linearization can provide an effective controller for many types of nonlinear systems. It is known, however, that these controllers are not robust, in particular to model uncertainties as the feedback linearization process involves canceling of nonlinearities in the dynamics using an exact model which is seldom available. Although there are several strategies to add robustness, recent work on sensitivity theory has shown that it can provide the least conservative design for robust feedback linearization. This is achieved by adjusting the control input to minimize the sensitivity. The work in this thesis develops the robust feedback linearization (RFL) methodology further by extending it to a new class of non-linear systems. This research presents a methodology for designing a RFL controller in conjunction with previous work on integrated robust optimal design (IROD) for hydraulically controlled multibody systems. With growing world populations the total output of the agricultural industry will need to increase with it. It has been shown that a significant portion of yield losses occur during harvest, and specifically at the header of the combine harvester. One way to improve this is by improved header height tracking. Promising research has shown that integrated mechanical plant and controller design can provide a better optimal controller than previously possible, but those techniques focus on the mechanical system only and do not account for hydraulic actuator dynamics. However, in practice, hydraulic systems pose control challenges because they are highly nonlinear and the system parameters can vary significantly. The proposed RFL methodology offers an ideal solution to this problem and the work in this thesis is dedicated to developing this methodology. Details are given about the mechanical and hydraulic plants as well as the development of a nominal feedback linearization controller. Then the controller is rendered robust to uncertainties in the bulk modulus by deriving the sensitivity dynamics and control adjustment. Finally, the controller performance is tested over a variety of simulated conditions and is compared to the current industry standard, the PID controller. The results show that the RFL controller greatly improves header height tracking with reduced input power and is robust to bulk modulus uncertainties

Транспорт и транспортные системы: конструирование, эксплуатация, технологии. Вып. 5

Статьи данного выпуска содержат материалы теоретических и экспериментальных исследований автомобилей, тракторов и их двигателей. Рассматриваются вопросы эксплуатации и ремонта автомобильной техники, безопасности дорожного движения. Значительное место уделяется методике подготовки специалистов в сфере транспорта. Сборник рассчитан на инженерно-технических работников заводов и научно-исследовательских лабораторий, преподавателей и аспирантов УВО. Сборник включен в Перечень научных изданий Республики Беларусь для опубликования результатов диссертационных исследований по техническим наукам. Публикуемые материалы рецензируются

Controle em cascata de um atuador hidráulico utilizando redes neurais

No presente trabalho, é realizada a modelagem e identificação de um serovoposicionador hidráulico de uma bancada de testes. As expressões analíticas tradicionalmente utilizadas em uma estratégia em cascata aplicada ao controle de trajetória de posição são obtidas. A estratégia em questão utiliza, conjuntamente, a linearização por realimentação como lei de controle do subsistema hidráulico e a lei de controle de Slotine e Li no subsistema mecânico. Com base na mesma estratégia, um controlador em cascata neural é proposto. Em tal controlador, a função analítica que representa o mapa inverso, presente na linearização por realimentação, e a função de compensação de atrito utilizada na lei de Slotine e Li são substituídas por funções constituidas por meio de redes neurais de perceptrons de múltiplas camadas. Essas redes neurais têm como entradas os estados do sistema e também a temperatura do fluido hidráulico. O novo controlador é apresentado em uma versão onde as redes neurais são aplicadas sem modificações on-line e em outra, onde são apresentadas leis de controle adaptativo para as mesmas. A prova de estabilidade do sistema em malha fechada é apresentada em ambos os casos. Resultados experimentais do controle de seguimento de trajetórias de posição em diferentes temperaturas do fluido hidráulico são apresentados. Esses resultados demonstram a maior efetividade do controlador proposto em relação aos controladores clássicos PID e PID+feefforward e ao controlador em cascata com funções analíticas fixas. Os experimentos são realizados em duas situações: quando não ocorrem variações paramétricas importantes no sistema, onde é utilizado o controlador em cascata neural fixo e quando ocorrem essas variações, onde se utiliza o controlador em cascata neural adaptativo.In this work, the modeling and identification of a hydraulic actuator testing setup are performed and the analytical expressions that are used in a cascade control strategy applyied in a position trajectory tracking control are designed. Such cascade strategy uses the feedback linearization control law in the hydraulical subsystem and the Slotine and Li control law in the mechanical one. Based on this cascade strategy, a neural cascade controller is proposed, for which the analytical function used as inversion set in the feedback linearization control law and the friction function compensation of the Slotine and Li control law are replaced by multi layer perceptrons neural networks where the inputs are the states of the system and the hydraulic fluid temperature. The novel controller is introduced in two different aproachs: the first one where the neural networks do not have on-line modifications and the second one where adaptive control laws are proposed. For both of them the stability proof of the closed-loop system is presented. Experimental results about some position tracking controls performed in different fluid temperature are showed. The results show that the novel controller is more efective than the classical PID, PID+feedforward and the traditional analytical cascade controller. The experiments are performed in two different setups: considering the system without importants parametric variations where is applied the non adaptive cascade neural controller and in the presence of parametric variations where is applied the adaptive cascade neural controller