Adaptive Model Predictive Control of a Two-wheeled Robot Manipulator with Varying Mass

Önkol, Mert; Kasnakoğlu, Coşku

doi:10.1177/0020294018758527

Cited by 26 publications

(23 citation statements)

References 54 publications

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“…5 The mathematical model is improved by applying a Adaptive Model Predictive Control (AMPC) and varying the longitudinal velocity, v y of the vehicle by representing the driver behavior in order to determinate the optimal value of the steering angle, to pass the test successfully. 27–29…”

Section: Mathematical Modelsmentioning

confidence: 99%

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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

Acuña

Rodrigues

Queiroz

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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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.

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Section: Mathematical Modelsmentioning

confidence: 99%

“…Among these variables n y , s j y , p , and w i , j j are determinate during the controller design and stay constant. 27…”

Section: Mathematical Modelsmentioning

confidence: 99%

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

Acuña

Rodrigues

Queiroz

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…Energy efficiency of the digital hydraulic system was studied for position control with high-speed switching by Saeedzadeh et al, 9 and Payne et al 10 studied variable speed control in the digital hydraulic system. Previous studies [11][12][13][14] give an understanding about the fluid power system and its control. Brandstetter et al 15 presented the feasibility and connective of digital hydraulic system with other controllers in industries.…”

Section: Review Of Related Workmentioning

confidence: 99%

“…An algebraic equation is proposed for converting angular movement of trajectory into linear form, which is presented in Equation (13). This equation gives equivalent linear stroke of cylinder for the desired angular movement of link.…”

Section: Relating Polynomial Trajectory To the Cylinder Strokementioning

confidence: 99%

Digital hydraulic single-link trajectory tracking control through flow-based control

Kalaiarassan

Krishnamurthy

2019

Measurement and Control

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Recent advancement in controllability of digital hydraulic is similar to the performance of a proportional/servo hydraulic system, and several studies show that digital hydraulic will be an alternative for proportional/servo hydraulic. In this paper, tip point tracking of a single-link arm is taken as the subject of the study. Here, the single-link arm is controlled by a digital hydraulic system, which is established with parallel-connected on/off valves. In order to attain stepwise flow control, the pulse code modulation technique is used. By referring to the previous work, the control signal for the trajectory tracking is calculated by taking account of cylinder chamber pressure and velocity. But in this study, the required volume flow rate for trajectory tracing is taken into account for generating control signal. This approach improves the performance of the digital hydraulic system at lower velocity tracking and also reduces the computational complexity. The analysis is conducted with the proposed algorithm for 4-bit and 5-bit digital flow control units and tip point response of single link is presented. The results show that the 5-bit system has significantly better performance than the 4-bit system. In addition, the analysis is conducted with different stroke lengths such as 200, 100 and 50 mm for studying the behaviour of the system at lower velocity tracking. Better controllability is achieved at lower velocity tracking, and the results obtained with the proposed algorithm have nearly 2% tracking error.

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“…Model predictive control (MPC) is the most popular advanced control method in industrial control technology and academics, which can effectively overcome the disturbance and uncertainty and easily handle the constrain of controlled variables and manipulated variables. [1][2][3][4][5] It is a kind of model-based closed-loop optimization control strategy. The principle of the method is to predict the dynamic behavior of the system and to proceed rolling optimization and to achieve feedback correction of model error.…”

Section: Introductionmentioning

confidence: 99%

Double-layered nonlinear model predictive control based on Hammerstein–Wiener model with disturbance rejection

Cai

et al. 2018

Measurement and Control

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This paper presents the double-layered nonlinear model predictive control method for a continuously stirred tank reactor and a pH neutralization process that are subject to input disturbances and output disturbances at the same time. The nonlinear systems can be described as a Hammerstein -Wiener model. Furthermore, two nonlinear parts of the Hammerstein -Wiener model should be transformed into linear combination of known input and unknown disturbances, respectively. By taking advantage of Kalman filter, disturbances and states can be estimated. The estimated disturbances and states can be considered to calculate steady-state target in steady-state target calculation layer. Moreover, the state feedback control law can be obtained in dynamic control layer. A simple proof for offset-free control is given in the proposed method. The simulation results show that the controlled variable can achieve the offset-free control. It can be seen that the proposed method has better disturbance rejection performance, strong robustness and practical value.

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Adaptive Model Predictive Control of a Two-wheeled Robot Manipulator with Varying Mass

Cited by 26 publications

References 54 publications

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

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

Digital hydraulic single-link trajectory tracking control through flow-based control

Double-layered nonlinear model predictive control based on Hammerstein–Wiener model with disturbance rejection

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