An efficient ant colony system based on receding horizon control for the aircraft arrival sequencing and scheduling problem

The aircraft arrival sequencing and scheduling (ASS) problem is a salient problem in air traffic control (ATC), which proves to be nondeterministic polynomial (NP) hard. This paper formulates the ASS problem in the form of a permutation problem and proposes a new solution framework that makes the first attempt at using an ant colony system (ACS) algorithm based on the receding horizon control (RHC) to solve it. The resultant RHC-improved ACS algorithm for the ASS problem (termed the RHC-ACS-ASS algorithm) is robust, effective, and efficient, not only due to that the ACS algorithm has a strong global search ability and has been proven to be suitable for these kinds of NP-hard problems but also due to that the RHC technique can divide the problem with receding time windows to reduce the computational burden and enhance the solution's quality. The RHC-ACS-ASS algorithm is extensively tested on the cases from the literatures and the cases randomly generated. Comprehensive investigations are also made for the evaluation of the influences of ACS and RHC parameters on the performance of the algorithm. Moreover, the proposed algorithm is further enhanced by using a two-opt exchange heuristic local search. Experimental results verify that the proposed RHC-ACS-ASS algorithm generally outperforms ordinary ACS without using the RHC technique and genetic algorithms (GAs) in solving the ASS problems and offers high robustness, effectiveness, and efficienc

Optimal greenhouse cultivation control: survey and perspectives

Abstract: A survey is presented of the literature on greenhouse climate control, positioning the various solutions and paradigms in the framework of optimal control. A separation of timescales allows the separation of the economic optimal control problem of greenhouse cultivation into an off-line problem at the tactical level, and an on-line problem at the operational level. This paradigm is used to classify the literature into three categories: focus on operational control, focus on the tactical level, and truly integrated control. Integrated optimal control warrants the best economical result, and provides a systematic way to design control systems for the innovative greenhouses of the future. Research issues and perspectives are listed as well

Receding horizon control for aircraft arrival sequencing and scheduling.

Airports, especially busy hub airports, proved to be the bottleneck resources in the air traffic control system. How to carry out arrival scheduling and sequencing effectively and efficiently is one of main concerns to improve the safety, capacity, and efficiency of the airports. This paper introduces the concept of receding horizon control (RHC) to the problem of arrival scheduling and sequencing in a dynamic environment. The potential benefits RHC could bring in terms of airborne delay and computational burden are investigated by means of Monte Carlo simulations. It is pointed out that while achieving similar performance as existing schemes, the new arrival scheduling and sequencing scheme significantly reduces the computational burden and provides potential for developing new optimization algorithms for further reducing airborne delay

Genetic algorithm based on receding horizon control for real-time implementations in dynamic environments

This paper introduces the concept of Receding Horizon Control (RHC) to Genetic Algorithm (GA) for real-time implementations in dynamic environments. The methodology of the new GA is presented with the emphases on how to effectively integrate the RHC strategy by following some RHC practices in control engineering, particularly, how to choose the length of receding horizon and how to design terminal penalty. Simulation results show that, when the RHC based GA is applied in dynamic environments, both computational efficiency and performance are improved in comparison with existing GAs

Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming

Autonomously training interpretable control strategies, called policies, using pre-existing plant trajectory data is of great interest in industrial applications. Fuzzy controllers have been used in industry for decades as interpretable and efficient system controllers. In this study, we introduce a fuzzy genetic programming (GP) approach called fuzzy GP reinforcement learning (FGPRL) that can select the relevant state features, determine the size of the required fuzzy rule set, and automatically adjust all the controller parameters simultaneously. Each GP individual's fitness is computed using model-based batch reinforcement learning (RL), which first trains a model using available system samples and subsequently performs Monte Carlo rollouts to predict each policy candidate's performance. We compare FGPRL to an extended version of a related method called fuzzy particle swarm reinforcement learning (FPSRL), which uses swarm intelligence to tune the fuzzy policy parameters. Experiments using an industrial benchmark show that FGPRL is able to autonomously learn interpretable fuzzy policies with high control performance.Comment: Accepted at Genetic and Evolutionary Computation Conference 2018 (GECCO '18