pdf vehicle engine torque estimation via unknown input observer and adaptive parameter estimation

Pdf Vehicle Engine Torque Estimation Via Unknown Input Observer And Adaptive Parameter Estimation

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Published: 25.05.2021

The present article deals with an observer design for nonlinear vehicle lateral dynamics.

This paper presents two torque estimation methods for vehicle engines: unknown input observer UIO and adaptive parameter estimation. We first propose a novel yet simple unknown input observer based on the crankshaft rotation dynamics only. For this purpose, an invariant manifold is derived by defining auxiliary variables in terms of first-order low-pass filters, where only one constant filter coefficient needs to be tuned. These filtered variables are used to calculate the estimated torque.

Vehicle Engine Torque Estimation via Unknown Input Observer and Adaptive Parameter Estimation

Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. We first propose a novel yet simple unknown input observer based on the crankshaft rotation dynamics only. For this purpose, an invariant manifold is derived by defining auxiliary variables in terms of first-order low-pass filters, where only one constant filter coefficient needs to be tuned. These filtered variables are used to calculate the estimated torque.

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Na and Anthony Siming Chen and G. Herrmann and Richard D.

We present a modified PMSM model considering the hysteresis loss and then transform the new PMSM model to a canonical form to simplify the controller design. In order to deal with the hysteresis loss, an ESN is utilized to estimate the nonlinearity. The stabilities of the observer and the controller are all guaranteed by Lyapunov functions. Finally, simulations are presented to verify the validity of the echo state network for extended state observer and the neural network sliding mode control. Electric vehicles are the most important new energy resource vehicles that attract much attention in these years. Many researchers and factories have investigated the electric vehicles and got some well results. Different electric vehicles have been produced, and some have been accepted in the market.

In order to improve handling stability performance and active safety of a ground vehicle, a large number of advanced vehicle dynamics control systems—such as the direct yaw control system and active front steering system, and in particular the advanced driver assistance systems—towards connected and automated driving vehicles have recently been developed and applied. However, the practical effects and potential performance of vehicle active safety dynamics control systems heavily depend on real-time knowledge of fundamental vehicle state information, which is difficult to measure directly in a standard car because of both technical and economic reasons. This paper presents a comprehensive technical survey of the development and recent research advances in vehicle system dynamic state estimation. Different aspects of estimation strategies and methodologies in recent literature are classified into two main categories—the model-based estimation approach and the data-driven-based estimation approach. Each category is further divided into several sub-categories from the perspectives of estimation-oriented vehicle models, estimations, sensor configurations, and involved estimation techniques. The principal features of the most popular methodologies are summarized, and the pros and cons of these methodologies are also highlighted and discussed.

The paper presents an adaptive identification algorithm via data filtering and improved prescribed performance function for Sandwich systems with hysteresis nonlinearity. By developing a filter in which the filter is simple and easy to realize online and several variables, the estimation error vector can be derived. To improve the transient performance of estimator, a modified prescribed performance function is proposed to constrain the estimation error data through the usage of the predefined domain. For the constrained estimation error condition, the error transformation technique is utilized to simplify the design of the estimator thanks to that the restricted condition is transformed into unconstrained condition. To achieve the convergence of the parameter estimation and assure the predetermined property, a fresh adaptive law is developed. Moreover, the theoretical analysis indicates that the error can converge to a small region based on martingale difference theorem.

With the rapid development of automated driving [ 1 ], [ 2 ], parallel unmanned systems [ 3 ]-[ 5 ], control and computer science [ 6 ], [ 7 ], intelligent transportation systems ITS [ 8 ], [ 9 ], advanced driver assistance systems ADAS , vehicle handling stability and active safety have increasingly been promoted since the past century. As a result, various ADAS and vehicle stability control systems have been developed, such as the anti-lock braking system ABS [ 10 ], [ 11 ], adaptive cruise system [ 12 ] and traction control system TCS [ 13 ], [ 14 ], which are based on vehicle longitudinal control; the electronic stability program ESP [ 15 ], [ 16 ] and active front steering AFS [ 17 ], which are concerned with lateral stability; and active suspension control ASC [ 18 ], [ 19 ] and active body control ABC [ 20 ], which emphasizes vehicle vertical control. Vehicle handling stability and active safety are effectively improved with the help of these systems, and consequently, vehicles have become safer to drive and the number of fatal accidents has declined [ 21 ]. However, the implementation of those automotive stability control systems, especially for connected vehicles and automated driven vehicles, depends on accurate vehicle dynamic state information [ 22 ]. Conventionally, vehicle dynamic state information is directly measured by onboard sensors that are sufficiently inexpensive for mass-production vehicles.

Джабба посмотрел на таблицу, что стояла на мониторе, и всплеснул руками. - Здесь около сотни пунктов. Мы не можем вычесть их все одно из другого. - Многие пункты даны не в числовой форме, - подбодрила людей Сьюзан.

 Если Стратмор не забил тревогу, то зачем тревожиться. - Да в шифровалке темно как в аду, черт тебя дери. - Может быть, Стратмор решил посмотреть на звезды.

 - Мидж, - сказал.  - Говорит Лиланд Фонтейн. Слушайте меня внимательно… ГЛАВА 112 - Надеюсь, вы знаете, что делаете, директор, - холодно сказал Джабба.  - Мы упускаем последнюю возможность вырубить питание. Фонтейн промолчал. И словно по волшебству в этот момент открылась дверь, и в комнату оперативного управления, запыхавшись, вбежала Мидж. Поднявшись на подиум, она крикнула: - Директор.

ГЛАВА 127 Собравшиеся на подиуме тотчас замолчали, словно наблюдая за солнечным затмением или извержением вулкана - событиями, над которыми у них не было ни малейшей власти. Время, казалось, замедлило свой бег. - Мы терпим бедствие! - крикнул техник.  - Все линии устремились к центру. С левого экрана в камеру неотрывно смотрели Дэвид и агенты Смит и Колиандер.

 Коммандер, не думаете же вы… - Сьюзан расхохоталась. Но Стратмор не дал ей договорить. - Сьюзан, это же абсолютно ясно. Танкадо выгравировал ключ Цифровой крепости на кольце. Золото долговечно. Что бы он ни делал - спал, стоял под душем, ел, - ключ всегда при нем, в любую минуту готовый для опубликования.

 - Получите удовольствие, профессор. Вы летали когда-нибудь на Лирджете-60. Беккер усмехнулся: - Давненько не летал.

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