Modeling and dynamic analysis of a 6 x 6 heavy military truck by adaptive model predictive control with application to NATO lane change test course

2020 ◽  
Vol 234 (13) ◽  
pp. 3128-3144
Author(s):  
Marcelo Andrés Acuña ◽  
Gustavo Simão Rodrigues ◽  
Rafael Vitor Guerra Queiroz ◽  
Elias Dias Rossi Lopes
Keyword(s):  
Model Predictive Control ◽  
Dynamic Analysis ◽  
Predictive Control ◽  
Degree Of Freedom ◽  
Adaptive Model ◽  
Double Line ◽  
Matlab Simulink ◽  
Longitudinal Dynamic ◽  
Adaptive Model Predictive Control ◽  
Lane Change Test

In this paper, the computer-aided vehicle dynamic analysis of a 6x6 heavy military truck is presented and examined. For the analysis, a MATLAB/Simulink® platform is used to design and model a truck. The vehicle configuration taken into account for the analysis is the powertrain (engine, gear box, transfer gear, differential), suspension, steering system and tire model according to the Pacekja 89’ formulation. In addition, the effect of the rolling resistance and drag is considered, in order to represent the vehicle behavior as real as possible. The longitudinal dynamic and lateral dynamic are formulated. First, the longitudinal dynamic model is established by means of implementation of the weight transfer function. The vehicles are considered as rigid bodies with 1 degree of freedom. Second, the vehicular planar model with three wheels, well known as bicycle model, is applied following the North Atlantic Treaty Organization double line change maneuver test reaching 3 degree of freedom. The driver behavior is represented by using an adaptive model predictive control varying the longitudinal velocity. The forces for braking, inertia of the rotating components, the energy lost in the powertrain, and the effect of dive squat and rollover. The numerical simulation results are shown and compared with a full-vehicle model formed by using Mechanical Simulation Corporation’s truckSIM®. There were chosen simulation scenarios applied to the model to observe the effects of different parameters concerning the dynamic behavior, and also prepared in truckSIM® environment. The main contributions of this article are the development of the vehicular model, through the use of block diagrams in a reliable and relatively simple programming code such as MATLAB/Simulink®, with innovative tools used in the control of autonomous vehicle driving and the flexibility to adapt said model to different environmental conditions and different vehicle parameters.

scholarly journals Data–Driven Indirect Adaptive Model Predictive Control

2012 ◽  
Author(s):  
Norhaliza Wahab ◽  
Mohamed Reza Katebi ◽  
Mohd Fua’ad Rahmat ◽  
Salinda Bunyamin
Keyword(s):  
Model Predictive Control ◽  
Activated Sludge ◽  
State Space ◽  
Predictive Control ◽  
Sliding Window ◽  
Subspace Identification ◽  
Activated Sludge Process ◽  
Adaptive Model ◽  
Time Step ◽  
Adaptive Model Predictive Control

Kertas kerja ini membincangkan tentang reka bentuk Pengawal Ramalan Model Suai menggunakan kaedah Pengenalpastian Model Keadaan Ruang Sub–ruang bagi proses enapcemar teraktif. Penggunaan teknik Pengenalpastian Model Keadaan Ruang Sub–ruang di dalam kaedah kawalan tingkat gelangsar suai dibincangkan di mana pengenalpastian sub–ruang dalam talian menggunakan algoritma N4SID di perkenalkan bersama dengan rekabentuk Pengawal ramalan model. Pembangunan N4SID dalam talian di dalam kertas kerja ini menggunakan pengemaskini QR di mana gabungan di antara teknik kemaskini dan kemasbawah membolehkan pengadaptasi tingkap gelangsar. Di sini, untuk setiap langkah masa, bagi setiap data baru akan dimasukkan ke faktor R manakala data yang lama dibuang. Begitu juga, strategi bagi uraian nilai tunggal diperkenalkan ke dalam Pengawal Ramalan Model Suai tak langsung untuk masukan tambahan kawalan bagi sistem terkekang tak lelurus. Beberapa kajian simulasi bagi parameter kawalan berlainan di dalam pengawal/pengenalpastian algoritma dilaksanakan. Bagi reka bentuk Pengawal Ramalan Model Suai tak langsung, pengiraan masa yang terlibat dengan menggunakan pendekatan uraian nilai tunggal kurang berbanding dengan kaedah perancangan kuadratik dan keputusan yang memberangsangkan ini adalah sumbangan utama di dalam kertas kerja ini. Kata kunci: Pengawal suai; proses enapcemar teraktif; pengawal ramalan model; pengenalpastian sub–ruang This paper explores the design of Adaptive Model Predictive Control (AMPC) using Subspace State–space Model Identification (SMI) techniques for an activated sludge process. The implementation of SMI techniques in the adaptive sliding window control methods are discussed where the online subspace identification using Numerical State–space Subspace System Identification (N4SID) algorithm is proposed along with Model Predictive Control (MPC) design method. The online N4SID algorithm developed in this study makes use of the QR–updating where the combination of update and down date techniques enables sliding window adaptation. Here, at each time step, for the new experimental data added into R factor, the oldest data are removed. Also, the Singular Value Decomposition (SVD–based) strategy is proposed into Indirect AMPC (IAMPC) for the control increment input constrained nonlinear system. Several simulation studies for different control parameters in control/identification algorithm are performed. For the IAMPC control design, the computational times involved using an SVD approach shows less burdensome compared to Quadratic Programming (QP) method and such an interesting result is considered as one of the main contribution in this paper. Key words: Adaptive control; activated sludge process; model predictive control; subspace identification

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