Simulasi Kontrol Penjejak Lintasan pada Traktor Roda Dua untuk Lintasan Multi Segmen

AbstrakSebagian besar mata pencaharian penduduk Indonesia adalah petani. Pada umumnya petani di Indonesia masih menggunakan traktor roda dua atau traktor tangan untuk mengolah lahan pertanian. Hadirnya mesin otonom khususnya traktor roda dua menjadi solusi dalam meningkatkan produktivitas pertanian. Traktor otonom roda dua memerlukan keakuratan menyetir dalam mengolah lahan pertanian agar dapat mengikuti lintasan.Tujuan penelitian adalah untuk mensimulasikan pengontrolan penjejak lintasan pada traktor roda dua dengan sinyal kontrol kecepatan (v) dan kecepatan sudut(ω). Parameter kontrol dicari dengan melakukan tuning terhadap parameter gain kontrol kecepatan (Kv), gain kontrol kecepatan sudut (Kω)dan jarak titik pusat ke referensi (b). Penelitian ini juga mengajukan metode untuk membuat lintasan traktor multisegmen secara otomatis berdasarkan masukan data panjang dan lebar lahan pertanian serta jarak atau interval setiap alur. Hasil tuningmemberikan nilai IAE (integral absolute error) minimal 9,7971 dengan nilai parameterb = 0,1, Kv = 10 dan Kω = 10.Penerapan nilai parameter pada simulasi multi segmen menunjukkan hasiltrayectori tracking yang cukup baik yaitu tercapainya errorkeseluruhan yang cukup kecil.

Comparison of Modern Controls and Reinforcement Learning for Robust Control of Autonomously Backing Up Tractor-Trailers to Loading Docks

Two controller performances are assessed for generalization in the path following task of autonomously backing up a tractor-trailer. Starting from random locations and orientations, paths are generated to loading docks with arbitrary pose using Dubins Curves. The combination vehicles can be varied in wheelbase, hitch length, weight distributions, and tire cornering stiffness. The closed form calculation of the gains for the Linear Quadratic Regulator (LQR) rely heavily on having an accurate model of the plant. However, real-world applications cannot expect to have an updated model for each new trailer. Finding alternative robust controllers when the trailer model is changed was the motivation of this research. Reinforcement learning, with neural networks as their function approximators, can allow for generalized control from its learned experience that is characterized by a scalar reward value. The Linear Quadratic Regulator and the Deep Deterministic Policy Gradient (DDPG) are compared for robust control when the trailer is changed. This investigation quantifies the capabilities and limitations of both controllers in simulation using a kinematic model. The controllers are evaluated for generalization by altering the kinematic model trailer wheelbase, hitch length, and velocity from the nominal case. In order to close the gap from simulation and reality, the control methods are also assessed with sensor noise and various controller frequencies. The root mean squared and maximum errors from the path are used as metrics, including the number of times the controllers cause the vehicle to jackknife or reach the goal. Considering the runs where the LQR did not cause the trailer to jackknife, the LQR tended to have slightly better precision. DDPG, however, controlled the trailer successfully on the paths where the LQR jackknifed. Reinforcement learning was found to sacrifice a short term reward, such as precision, to maximize the future expected reward like reaching the loading dock. The reinforcement learning agent learned a policy that imposed nonlinear constraints such that it never jackknifed, even when it wasn\u27t the trailer it trained on

Business Law and the Legal Environment

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