Robust online roll dynamics identification of a vehicle using sliding mode concepts

Tafner, Robert; Reichhartinger, Markus; Horn, Martin

doi:10.1016/j.conengprac.2014.03.002

Cited by 17 publications

(12 citation statements)

References 26 publications

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“…In order to analyze the stability of (10) and (11), the system needs to be rewritten as the form of system (12)- (13). The inputs of system (10) are S and R. Output (11) is just a function of S and the weights W and V. Because the output has to be a function of all inputs and weights of the system, without any change in the model structure, let augmented weights ]…”

Section: Belm Stabilitymentioning

confidence: 99%

“…An actual system is considered as a reference model, while a dynamics model of system with known equations of motion but unknown parameters is chosen as an adjustable model. Tafner et al 13 have proposed a robust and online method for system identification using sliding mode concepts. Tang et al have made use of this method for estimating the parameters of a motor.…”

Section: Introductionmentioning

confidence: 99%

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Online and stable parameter estimation based on normalized brain emotional learning model (NBELM)

Akhormeh

Roshanian

MoradiMaryamnegari

et al. 2019

Adaptive Control & Signal

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Recently, a computational model of Amygdala based on the brain emotional learning is presented by psychologists. This brain emotional learning model (BELM) that has a neuro-inspired architecture is utilized to train the weights which are in Amygdala and Orbitofrontal. In this paper, unknown parameters of dynamic systems are estimated by developing the normalized BELM (NBELM).To this end, after proving the stability of the model output, the sufficient condition for weights convergence is extracted while the sensitivity analysis is applied for this model. In order to evaluate the performance of NBELM, in the first example, the matrices of a twin rotor MIMO system are estimated and compared with the equation error method (EEM). In the second example, the nonlinear model of a servomotor is utilized as a case study. In the third example, the performance of the NBELM in experimental systems is validated using a reaction wheel with a DC motor. An important feature of the brain emotional system is its fast response, leading the NBELM to have a high speed performance in estimating the parameters of dynamic systems. A few number of adjustable parameters and low computing complexity also cause the NBELM to be an appropriate method for online estimation of the unknown parameters of dynamic systems. KEYWORDSAmygdala computing model, BEL model, dynamic system parameter estimation, online and stable estimation Parameters of a large number of industrial systems need to be monitored online for various reasons, ranging from adaptive controls 1 to fault detection systems. 2,3 As a consequence, many research studies have been devoted to introducing novel parameters identification algorithms able to insure the stability of the system. Additionally, the proposed techniques must converge to real values as fast as possible in order to be applicable to engineering systems while high accuracy in the converging process is an evitable need in any novel method. The low complexity of the new algorithm is another challenge, leading the system identification method to be implemented with less effort and become wider. For mentioned challenges to be addressed, various investigations create new frameworks in terms of problem formulation, methodology development, theoretical foundation, software tools, and hardware modules. 4Int J Adapt Control Signal Process. 2019;33:1047-1065.wileyonlinelibrary.com/journal/acs

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Section: Belm Stabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Online and stable parameter estimation based on normalized brain emotional learning model (NBELM)

Akhormeh

Roshanian

MoradiMaryamnegari

et al. 2019

Adaptive Control & Signal

View full text Add to dashboard Cite

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“…As reported in the literature, the velocity of the vehicle can be estimated through Kalman filters, fuzzy logic, or nonlinear observers of the vehicle acceleration combined with wheel speed measurements. In recent years, in the automotive field, SM observers have also been successfully used . As for the control algorithms, the conventional ones are mainly based on PID controllers, often with feedforward components, whereas more advanced and robust controllers, relying on SMC theory, have been proposed and analyzed in the literature …”

Section: Introductionmentioning

confidence: 99%

“…In recent years, in the automotive field, SM observers have also been successfully used. [23][24][25][26][27][28][29][30][31] As for the control algorithms, the conventional ones are mainly based on PID controllers, often with feedforward components, whereas more advanced and robust controllers, relying on SMC theory, have been proposed and analyzed in the literature. [32][33][34][35][36] Sliding mode control has many positive features, such as finite-time convergence, which is beneficial in terms of tracking performance and robustness.…”

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confidence: 99%

Optimization‐based adaptive sliding mode control with application to vehicle dynamics control

Ferrara

Incremona

Regolin

2018

Intl J Robust & Nonlinear

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Summary This paper presents the design of a new adaptive optimization‐based second‐order sliding mode control algorithm for uncertain nonlinear systems. It is designed on the basis of a second‐order sliding mode control with optimal reaching, with the aim of reducing the control effort while maintaining all the positive aspects in terms of finite‐time convergence and robustness in front of matched uncertainties. These features are beneficial to guarantee good performance in case of vehicle dynamics control, a crucial topic in the light of the increasing demand of semiautonomous and autonomous driving capabilities in commercial vehicles. The new proposal is theoretically analyzed, as well as verified relying on an extensive comparative study, carried out on a realistic simulator of a 4‐wheeled vehicle, in the case of a lateral stability control system.

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“…The vehicle roll angle can be directly measured by a dual antenna global positioning system (GPS), but it is expensive. For this reason, many researchers have focused on vehicle roll angle estimation [3,4,5,6,7]. One of the main techniques employed to estimate roll angle is through sensor fusion.…”

Section: Introductionmentioning

confidence: 99%

Sensor Fusion Based on an Integrated Neural Network and Probability Density Function (PDF) Dual Kalman Filter for On-Line Estimation of Vehicle Parameters and States

Vargas-Meléndez

Boada

Gauchía

et al. 2017

Sensors

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Vehicles with a high center of gravity (COG), such as light trucks and heavy vehicles, are prone to rollover. This kind of accident causes nearly 33% of all deaths from passenger vehicle crashes. Nowadays, these vehicles are incorporating roll stability control (RSC) systems to improve their safety. Most of the RSC systems require the vehicle roll angle as a known input variable to predict the lateral load transfer. The vehicle roll angle can be directly measured by a dual antenna global positioning system (GPS), but it is expensive. For this reason, it is important to estimate the vehicle roll angle from sensors installed onboard in current vehicles. On the other hand, the knowledge of the vehicle’s parameters values is essential to obtain an accurate vehicle response. Some of vehicle parameters cannot be easily obtained and they can vary over time. In this paper, an algorithm for the simultaneous on-line estimation of vehicle’s roll angle and parameters is proposed. This algorithm uses a probability density function (PDF)-based truncation method in combination with a dual Kalman filter (DKF), to guarantee that both vehicle’s states and parameters are within bounds that have a physical meaning, using the information obtained from sensors mounted on vehicles. Experimental results show the effectiveness of the proposed algorithm.

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Robust online roll dynamics identification of a vehicle using sliding mode concepts

Cited by 17 publications

References 26 publications

Online and stable parameter estimation based on normalized brain emotional learning model (NBELM)

Online and stable parameter estimation based on normalized brain emotional learning model (NBELM)

Optimization‐based adaptive sliding mode control with application to vehicle dynamics control

Sensor Fusion Based on an Integrated Neural Network and Probability Density Function (PDF) Dual Kalman Filter for On-Line Estimation of Vehicle Parameters and States

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