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Fault-tolerant control of active trailer steering systems for multi-trailer articulated heavy vehicles
Published in Maksym Spiryagin, Timothy Gordon, Colin Cole, Tim McSweeney, The Dynamics of Vehicles on Roads and Tracks, 2018
Saurabh Kapoor, Tushita Sikder, Yuping He
As explained above, H∞ controller controls the yaw rates of vehicle units by minimizing the errors between the actual and desired yaw rate. Thus, obtaining an accurate measurement of the vehicle’s yaw rate is essential. Generally, a yaw rate sensor would provide the necessary measurements. However, if the sensor malfunctions, the error signal sent to the controller will be large. The controller will try to compensate and reduce this error by applying ATS angles, which will degrade system performance. Yaw rate of a vehicle can be estimated from left and right wheel speeds (Venhovens and Naab, 1999). The kinematic relationship between the yaw rate and wheel speeds is defined in Equation (1). In most vehicles, the ABS module already tracks these signals, which makes this a practical method. () ryaw=(Ωfr−Ωfl)RdynTwfcosδ
Anti-lock brakes and traction control
Published in M.J. Nunney, Light and Heavy Vehicle Technology, 2007
Since determining the actual course of the vehicle is a relatively complicated business, a vehicle dynamics control system naturally requires components additional to those already associated with ABS/ASR systems (Figure 29.10). In particular a lateral accelerometer or G sensor is required that provides a sensitive response to the forces generated during cornering, and a yaw rate sensor that measures the speed at which the car rotates about its vertical axis. The latter sensor may be regarded as being at the heart of the VDC system, and is in fact a complex instrument that has been derived from aviation practice and adapted to the extreme environmental conditions of the motor vehicle. Two further sensors are also involved, these being a steering angle sensor that signals the intended course steered by the driver, and a braking pressure sensor.
Manoeuvring test for a self-running ship model in various water depth conditions
Published in Petar Georgiev, C. Guedes Soares, Sustainable Development and Innovations in Marine Technologies, 2019
M.A. Hinostroza, H.T. Xu, C. Guedes Soares
The hardware structure consists of all the sensors and actuators that are used in the self-running guidance and control platform, (Figure 5). The hardware is further divided into two groups: On board and on shore. The on board system is composed by a set of sensors, internal measurement unit, yaw rate sensor, electrical motors and industrial Wi-fi unit where all the signals are synchronized using a Compact-RIO and stored in a laptop. The on shore system is composed by a laptop and WiFi unit used to control the self-running model. (Perera et al., 2015; 2017). The system is powered using a 24V Lithium battery, a NI 9505 C# module from National Instrument is used to control the propeller and rudder motors.
A data-driven fault detection and diagnosis method via just-in-time learning for unmanned ground vehicles
Published in Automatika, 2023
Changxin Zhang, Xin Xu, Xinglong Zhang, Xing Zhou, Yang Lu, Yichuan Zhang
From the collected data of 10,800 samples, the first 6000 samples were chosen to construct the fault-free dataset, which supported the online JITGP algorithm to build local models. The training dataset of fault classification was constructed from the 6001st to 10,000th sample, which consisted of 4000 samples. These 4000 samples were divided into four categories, with 1000 samples in each category. The categories' labels were the fault statuses, which were fault-free, fault 1 (yaw rate sensor fault), fault 2 (lateral acceleration sensor fault), and fault 3 (steering wheel angle sensor fault), respectively. Finally, the remaining 800 samples were used to form a test set for algorithm testing, which was also divided into four groups with 200 samples in each group, respectively corresponding to the above four fault statuses.
A feedback-feedforward steering controller designed for vehicle lane keeping in hard-braking manoeuvres on split-μ roads
Published in Vehicle System Dynamics, 2022
Liangyao Yu, Sheng Zheng, Yaqi Dai, Lanie Abi, Xiaohui Liu, Shuo Cheng
Considering that the fluctuation of the yaw moment disturbance is unpredictable and that the sensors including yaw rate sensor, acceleration sensor and vision systems equipped in a production vehicle are fairly common, a state feedback controller is designed in this paper. LQR control is widely used because, unlike most constrained minimisation problems, this particular structure has an algebraic solution [12]. The goal of the LQR is to drive all of the state variables to zero over some prediction horizon. Considering that the parameters in the controller reference model are time-varying, a time-variant receding horizon LQR controller is designed to calculate the optimal feedback gains in a finite time domain of time steps. A quadratic cost function can be evaluated over future time steps as follows: where is the state variable weighting matrix, is the terminal state variable weighting matrix and is the control input weighting matrix. They are all positive semidefinite weighting matrices. By choosing and appropriately, we can adjust the speed at which state variables are driven to zero to adjust the controller performance.
Fault estimation for descriptor linear systems based on the generalised dynamic observer
Published in International Journal of Systems Science, 2018
Gloria Osorio-Gordillo, Carlos Astorga-Zaragoza, Abraham Pérez Estrada, Rodolfo Vargas-Méndez, Mohamed Darouach, Latifa Boutat-Baddas
Fault estimation represents an important information source about the health condition of a process and it can be useful to prevent dangerous conditions through the correct design and development of monitoring or fault tolerant control systems. For instance, in crude oil pipelines, a fault detection and estimation scheme can be useful to develop a monitoring system in order to detect the location of a leak and estimate the amount of the lost product. Another interesting application of fault estimation can be found in Chen, Patton, and Goupil (2016), where the authors propose a method to estimate the yaw rate sensor faults in the AIRBUS Air Data Inertial Reference System. This kind of systems are useful in the attempt to avoiding aircraft accidents such as the Air France Flight 447 which fell in the Atlantic Ocean in 2009 (Bureau d'Enquêtes et d'Analyses, n.d.) as a consequence of erroneous decisions due to sensor failures (obstruction of the Pitot probes by ice crystals).