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Mutaz Ryalat,Dina Shona Laila,Hisham ElMoaqet 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.2
In this paper, we present two adaptive control approaches to handle uncertainties caused by parametric and modeling errors in a class of nonlinear systems with uncertainties. The methods use the Port-controlled Hamiltonian (PCH) modelling framework and the interconnection and damping assignment passivity-based control (IDA-PBC) control design methodology being the most effectively applicable method to such models. The methods explore an extension on the classical IDA-PBC by adopting the state-transformation, yielding a dynamic state-feedback controller that asymptotically stabilizes a class of underactuated mechanical systems and preserves the PCH structure of the augmented closed-loop system. The results are applied to the underactuated mechanical systems that are a class of mechanical systems with broad applications and are more interesting as well as challenging control problems within this context. The results are illustrated with numerical simulations applied to twounderactuated robotic systems; the Acrobot and non-prehensile planar rolling robotic (disk-on-disk) systems.
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Mardiati Rina,Trilaksono Bambang Riyanto,Wibowo Sony Sulaksono,Laila Dina Shona 한국자동차공학회 2021 International journal of automotive technology Vol.22 No.4
This paper proposes a novel MDP framework to deal with the accuracy of the motorcycle driving model. It proposes a weighted and unweighted Dynamical-Discretized Reward Field (DDRF) as a major contribution on modeling motorcycle maneuver in mixed traffic conditions. Other contributions of this work are the integration of a motorcycle trajectory maneuver model in the state transition function, derivation of probability functions, area of awareness (AoA) and its sectorization to perceive vehicles inside the AoA, which is used to determine actions. We conducted some simulations to evaluate the performance of the proposed model by comparing the data from the simulations with real data. In this study, we use 100 simulation data on motorcycle maneuvering, which consisted of two different scenarios, i.e., 50 data of motorcycle maneuvering to avoid other motorcycles and 50 data of motorcycle maneuvering to avoid cars. We adjusted the simulation setting to the real situation and measured the performance of the proposed model using root mean square error (RMSE). In general, the proposed method can properly model the maneuver of motorcycles in heterogeneous traffic with an RMSE value of around 0.74 meters. This model performs twice as good as an existing car-following model. Furthermore, the proposed reward function performs around 4 ~ 6 % better than the reward function in previous studies.
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