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A hybrid control strategy for a dynamic scheduling problem in transit networks

Liu Zhongshan, Yu Bin, Zhang Li, Wang Wensi

International Journal of Applied Mathematics and Computer Science

2022

Vol. 32, no. 4

553--567

Public transportation is often disrupted by disturbances, such as the uncertain travel time caused by road congestion. Therefore, the operators need to take real-time measures to guarantee the service reliability of transit networks. In this paper, we investigate a dynamic scheduling problem in a transit network, which takes account of the impact of disturbances on bus services. The objective is to minimize the total travel time of passengers in the transit network. A two-layer control method is developed to solve the proposed problem based on a hybrid control strategy. Specifically, relying on conventional strategies (e.g., holding, stop-skipping), the hybrid control strategy makes full use of the idle standby buses at the depot. Standby buses can be dispatched to bus fleets to provide temporary or regular services. Besides, deep reinforcement learning (DRL) is adopted to solve the problem of continuous decision-making. A long short-term memory (LSTM) method is added to the DRL framework to predict the passenger demand in the future, which enables the current decision to adapt to disturbances. The numerical results indicate that the hybrid control strategy can reduce the average headway of the bus fleet and improve the reliability of bus service.

Disturbance observer-assisted hybrid control for autonomous manipulation in a robotic backhoe

CS Meera, Gupta Mukul Kumar, Santhakumar Mohan

Archive of Mechanical Engineering

2019

Vol. LXVI, nr 2

153--169

Automation of earth moving machineries is a widely studied problem. This paper focusses on one of the main challenges in automation of the earth moving industry, estimation of loading torque acting on the machinery. Loading torque acting on the excavation machinery is a very significant aspect in terms of both machine and operator safety. In this study, a disturbance observer-assisted control system for the estimation of loading torque acting on a robotic backhoe during excavation process is presented. The proposed observer does not use any acceleration measurements, rather, is proposed as a function of joint velocity. Numerical simulations are performed to demonstrate the effectiveness of the proposed control scheme in tracking the reaction torques for a given dig cycle. Co-simulation experiments demonstrate robust performance and accurate tracking of the proposed control in both disturbance torque and position tracking. Further, the performance and sensitivity of the proposed control are also analyzed through the help of performance error quantifiers, the root-mean-square (RMS) values of the position and disturbance tracking errors.