Browsing by Subject "Parameter identification"

Now showing 1 - 3 of 3

Open Access
Development of a fault-tolerant model predictive controller for vehicle lateral stability
(2023-08-01) Köysüren, Muhammed Kemal
Recently, there has been an increased interest in the automotive industry using scaled test vehicles to test the real-time performance of modeling and control algorithms. A scaled prototype, specifically developed and instrumented for enhancing vehicle lateral stability, offers distinct advantages in terms of cost reduction and the ability to repeat tests under various vehicle maneuvering scenarios rapidly. First, the mechatronic design of a 1:8 scaled electric vehicle with 4-wheel-drive and 4-wheel-independent-steering was done and the prototype vehicle was built. Plant model parameters such as the cornering coefficients of the tires are estimated using various methods such as traditional neural network training, a Physics Informed Deep Learning (PIDL) algorithm, and Pacejka’s tire modeling procedure. Secondly, a fault-tolerant reconfigurable model predictive controller (MPC) is proposed to enhance reference tracking for four-wheel-drive and four-wheel-steering vehicles under concurrent steering actuator faults. The method detects, isolates, and estimates fault magnitudes, which inform adjustments to the MPC formula. Performance validation is conducted through obstacle avoidance maneuvers with a control-oriented vehicle model and real-time applicability tests with a Processor-in-the-Loop system using a high-fidelity vehicle model. The test results confirm the proposed algorithm’s superior performance over the conventional MPC. Lastly, a computationally efficient two-path optimal control allocation method is proposed to reduce controller block execution time in vehicle ECU. High-fidelity results prove the computational cost reduction of the proposed algorithm over the conventional allocation method.
Open Access
Identification of hydrodynamic coefficients of AUV in the presence of measurement biases
(Sage Publications, 2021-11-23) Dinç, Mustafa; Hajiyev, C.
This paper mainly presents the parameter identification method developed from a Least Square Estimation (LSE) algorithm to estimate hydrodynamic coefficients of Autonomous Underwater Vehicle (AUV) in the presence of measurement biases. LSE based parameter determination method is developed to obtain unbiased estimated values of hydrodynamic coefficients of AUV from biased Inertial Navigation System (INS) measurements. The proposed parameter identification method consists of two phases: in the first phase, high precision INS and its auxiliary instrument including compass, pressure depth sensor, and Doppler Velocity Log (DVL) are designed as Integrated Navigational System coupled with Complementary Kalman Filter (CKF) to determine hydrodynamic coefficients of AUV by removing the INS measurement biases; in the second phase, LSE based parameter identification method is applied to the model in the first phase for obtaining unbiased estimated values of hydrodynamic coefficients of AUV. In this paper, a method for identifying the yaw and sway motion dynamic parameters of an AUV is given. Various maneuvering scenarios are verified to assess the parameter identification method employed. The simulation results indicate that using the CKF based Integrated Navigation System together with unbiased measurement conversion could produce better results for estimating the hydrodynamic coefficients of AUV.
Open Access
Parameter identification for partially observed diffusions
(Kluwer Academic Publishers-Plenum Publishers, 1992) Dabbous, T.E.; Ahmed, N.U.
In this paper, we consider the identification problem of drift and dispersion parameters for a class of partially observed systems governed by Ito equations. Using the pathwise description of the Zakai equation, we formulate the original identification problem as a deterministic control problem in which the unnormalized conditional density (solution of the Zakai equation) is treated as the state, the unknown parameters as controls, and the likelihood ratio as the objective functional. The question of existence of elements in the parameter set that maximize the likelihood ratio is discussed. Further, using variational arguments and the Gateaux differentiability of the unnormalized density on the parameter set, we obtain the necessary conditions for optimal identification. © 1992 Plenum Publishing Corporation.