Mass transit scheme design

In consequence of the increasing of regional economic activities in Pulau Batam, a reliable transportation system is required. Decreasing road network performance as a result of increasing traffic volume needs a strategic planning to anticipate the worsening condition in the future. One of the solutions is by providing mass transit system which is expected to attract private car users. Therefore, determination of potential corridor of mass transit system need to be identified so that the system provide better accessibility. Trip pattern in Pulau Batam must be known by developing trip distribution model. The trip distribution model is calibrated using origin-destination (O-D) data that is based on home interview survey. The validated model will be used to forecast and simulate travel demand onto transport network. Result of model calibration process shows mean trip length difference between model and survey is equal 0.141 %. From simulation of trip assignment is obtained that potential corridor for mass transit system using LRT is Batu Ampar – Batu Aji via Muka Kuning. Passenger forecast in the year 2030 is 193,990 passenger/day (2 directions).

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Optimal Model Reference Control Scheme Design for Nonlinear Strict-Feedback Systems

Engineering and Technology Journal ◽

10.30684/etj.v38i9a.1339 ◽

2020 ◽

Vol 38 (9A) ◽

pp. 1342-1351

Author(s):

Musadaq A. Hadi ◽

Hazem I. Ali

Keyword(s):

Reference Model ◽

Feedback System ◽

Optimization Method ◽

Feedback Systems ◽

Model Reference ◽

Scheme Design ◽

Model Reference Control ◽

Control Scheme ◽

Lyapunov Stability Analysis ◽

Strict Feedback

In this paper, a new design of the model reference control scheme is proposed in a class of nonlinear strict-feedback system. First, the system is analyzed using Lyapunov stability analysis. Next, a model reference is used to improve system performance. Then, the Integral Square Error (ISE) is considered as a cost function to drive the error between the reference model and the system to zero. After that, a powerful metaheuristic optimization method is used to optimize the parameters of the proposed controller. Finally, the results show that the proposed controller can effectively compensate for the strictly-feedback nonlinear system with more desirable performance.

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