Adaptive Iterative Learning Control for Freeway Traffic Flow Systems Using Improved Bacterial Foraging Optimized Desired Traffic Densities Planning
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In this paper, we propose a discrete fuzzy-neural adaptive iterative learning control (AILC) for freeway traffic flow systems with random initial resetting errors, iteration-varying desired traffic densities, and random bounded off-ramp traffic volumes using traffic densities, space mean speeds, and on-ramp waiting queues design. It is assumed that the system nonlinear functions and input gains are unknown for controller design. An adaptive fuzzy-neural network controller and an adaptive robust controller are applied to compensate for the unknown system nonlinearities and input gains, respectively. Moreover, to deal with the disturbances from random bounded off-ramp traffic volumes, a dead-zone like auxiliary error with a time-varying boundary layer is introduced as a bounding parameter. This proposed auxiliary error is also utilized to construct the adaptive laws without using the bound of the input gain. The traffic density tracking error is shown to converge along the axis of learning iteration to a residual set whose level of magnitude depends on the width of boundary layer. Besides, since the nice desired traffic densities designed for the coordinated control objective of the AILC for freeway traffic flow systems are generally unknown, the improved bacterial forging optimization (IBFO) algorithm is used to optimize the fitness function, which is constructed by the coordinated control objective including (1) minimum total travel time, (2) minimum on-ramp average waiting time, and (3) minimum changes of desired traffic densities. Finally, a computer simulation example is used to verify the learning performance of the proposed fuzzy-neural AILC for freeway traffic flow systems using IBFO-based desired traffic densities planning.
KeywordsAdaptive iterative learning control Freeway traffic flow systems Fuzzy-neural network Desired traffic densities planning Improved bacterial forging optimization
This work is supported by Ministry of Science and Technology, R.O.C., under Grants NSC 102-2221-E-005-061-MY3, MOST104-2221-E-211-009, MOST104-2221-E-211-010, MOST105-2221-E-211-006, MOST-105-2221-E-005-049-MY3 and by National Science Foundation of China, under Grant No. 61374102, 61120106009, 61433002.
- 9.Zhang, Y.L., Sun, H.Q., Hou, Z.S.: A feedback-feedforward high order iterative learning control for freeway ramp metering. In: 2010 International Conference on Intelligent Control and Information Processing, pp. 133–138, 2010Google Scholar
- 10.Yan, J.W., Hou, Z.S.: Iterative learning control with internal model for freeway ramp metering Control. In: IEEE International Conference on and Automation, pp. 1128–1133, 2009Google Scholar
- 11.Jin, S.T., Hou, Z.S.: Adaptive optimal iterative learning control for local ramp metering. In: Workshop on Power Electronics and Intelligent Transportation System, pp. 122–126, 2008Google Scholar
- 13.Chi, R.H., Hou, Z.S., Shu, S.L.: Discrete-time adaptive iterative learning from different tracking tasks with variable initial conditions. In: Proceedings of the 26th Chinese Control Conference, pp. 791–795, 2007Google Scholar
- 17.Chi, R.H., Wang, D.W., Hou, Z.S., Jin, S.T., Zhang, D.X.: A discrete-time adaptive iterative learning from different reference trajectory for linear time-varying systems. In: Chinese Control Conference, pp. 3013–3016, 2012Google Scholar
- 18.Chi, R.H., Hou, Z.S., Jin, S.T., Wang, D.W.: A new dynamical linearization based adaptive ILC for nonlinear discrete-time MIMO systemss. In: International Conference on Control Automation Robotics and Vision (ICARCV)Google Scholar
- 21.Das, S., Biswas, A., Dasgupta, S., Abraham, A.: Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications. In: Abraham, A., Hassanien, A.-E., Siarry, P., Engelbrecht, A. (eds.) Foundations of Computational Intelligence, vol. 3, pp. 23–55. SpringerVerlag, Heidelberg (2007)Google Scholar
- 32.Zhao, X.J., Xu, J.X., Srinivasan, D.: Freeway ramp metering by macroscopic traffic scheduling with particle swarm optimization. In: 2013 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems, 2013Google Scholar
- 34.Das, S., Biswas, A., Dasgupta, S., Abraham, A.: Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications. Found. Comput. Intell. 3, 23–55 (2009)Google Scholar