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

Genetic algorithm based on receding horizon control for arrival sequencing and scheduling

The concept of Receding Horizon Control (RHC) is introduced into Genetic Algorithm (GA) in this paper to solve the problem of Arrival Scheduling and Sequencing (ASS) at a busy hub airport. A GA based method is proposed for solving the dynamic ASS problem, and the focus is put on the methodology of integrating the RHC strategy into the GA for real-time implementations in a dynamic environment of air traffic control (ATC). Receding horizon and terminal penalty are investigated in depth as two key techniques of this novel RHC based GA. Simulation results show that the new method proposed in this paper is effective and efficient to solve the ASS problem in a dynamic environment. Key words: Receding Horizon Control, Genetic Algorithm, Air Traffic Contr

Receding horizon control for free-flight path optimisation

This paper presents a Receding Horizon Control (RHC) algorithm to the problem of on-line flight path optimization for aircraft in a Free Flight (FF) environment. The motivation to introduce the concept of RHC is to improve the robust performance of solutions in a dynamic and uncertain environment, and also to satisfy the restrictive time limit to the real-time optimization of this complicated air traffic control problem. Firstly, the mathematical model for the on-line FF path optimization problem is set up and discussed. Then, the proposed RHC algorithm is described in details. Simulation results illustrate that the new algorithm is very efficient and promising for practical applications. While achieving almost the same optimal solution as an existing algorithm in the absence of environmental uncertainties, it works better in a dynamic and uncertain environment. In either case, the online computational time of the proposed RHC algorithm is only a fraction of that of the existing algorithm

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

A stable model predictive control algorithm without terminal weighting

The introduction of terminal penalty in the performance index and the usage of the concept of terminal regions now become common practice in Model Predictive Control (MPC) for guaranteeing its stability. However, it is quite difficult and conservative to propagate the influence of disturbances and uncertainties from an initial state to the terminal state, in particular, when the predictive horizon is long. This paper presents a new stable MPC algorithm where the additional weighting on the first state rather than on the terminal state in the horizon is imposed. Furthermore, a new tuning knob is introduced in the performance index, which can be used to trade off between disturbance attenuation/robustness and stability. It is shown that in the absence of disturbances and uncertainties, the new MPC algorithm achieves the similar performance as current terminal weighting based MPC algorithms. However, it exhibits much better disturbance attenuation ability and robustness against uncertainties. The proposed method is favorably compared with terminal weighting based MPC algorithms by a numerical example

Multiairport capacity management: genetic algorithm with receding horizon

The inability of airport capacity to meet the growing air traffic demand is a major cause of congestion and costly delays. Airport capacity management (ACM) in a dynamic environment is crucial for the optimal operation of an airport. This paper reports on a novel method to attack this dynamic problem by integrating the concept of receding horizon control (RHC) into a genetic algorithm (GA). A mathematical model is set up for the dynamic ACM problem in a multiairport system where flights can be redirected between airports. A GA is then designed from an RHC point of view. Special attention is paid on how to choose those parameters related to the receding horizon and terminal penalty. A simulation study shows that the new RHC-based GA proposed in this paper is effective and efficient to solve the ACM problem in a dynamic multiairport environment

Model predictive control for constrained systems with uncertain state-delays

This paper presents a Model Predictive Control (MPC) algorithm for a class of constrained linear systems with uncertain state-delays. Based on a novel artificial Lyapunov function, a new stabilizing condition dependent of the upper bound of uncertain state-delays is presented in an LMI (Linear Matrix Inequality) form. The proposed MPC algorithm is developed by following the fashion of stability-enforced scheme. The new algorithm is then extended to linear time-varying (LTV) systems with multiple uncertain state-delays. Numerical examples illustrate the effectiveness of the new algorithm

Model predictive control of linear systems with nonlinear terminal control

In terminal weighting based model predictive control (MPC) algorithms, the terminal weighting term is used to cover the performance cost under the terminal control for guaranteeing stability. Therefore, the terminal control is crucial in choosing the terminal weighting term and also determining/estimating the stability region. However, most of the existing MPC algorithms are developed based on linear terminal control which restricts the achievable performance and stability of MPC. For linear systems, an MPC algorithm with nonlinear terminal control is presented in this paper, where the gain of the terminal control varies with the terminal state. An offline algorithm is proposed to determine the nonlinear gains and to choose the associated terminal weighting term. It is shown that, compared with the MPC algorithms with fixed linear terminal gain, a nonlinear terminal control results in a much larger stability region, which is confirmed by a numerical example