Mohamed F. Hassan

A hybrid GA/heuristic approach to the simultaneous scheduling of machines and automated guided vehicles

In this paper, the problem of simultaneous scheduling of machines and identical automated guided vehicles (AGVs) in flexible manufacturing systems is addressed with the objective of minimizing the makespan. This problem is composed of two interrelated decision problems: the scheduling of machines, and the scheduling of AGVs. Both problems are known to be NP-complete, resulting in a more complicated NP-complete problem when they are considered simultaneously. A new hybrid Genetic-algorithm/heuristic coding scheme is developed for the studied problem. The developed coding scheme is combined with a set of genetic algorithm (GA) operators selected from the literature of the applications of GAs to the scheduling problems. The algorithm is applied to a set of 82 test problems, which was constructed by other researchers, and the comparison of the results indicates the superior performance of the developed coding.

Parallel asynchronous algorithms for optimal control of large‐scale dynamic systems

This paper presents two parallel asynchronous algorithms for the solution of the optimal control problem of linear large-scale dynamic systems. These algorithms are based on the prediction concept. The first one adopts the interaction prediction approach and the second is based upon the costate prediction approach. The convergence behaviour of the proposed algorithms is thoroughly investigated. The new algorithms are applied on three practical systems and simulation results are presented and compared with those obtained using the well-known synchronous algorithms. It is shown that substantial savings in computation time can be achieved by employing the proposed asynchronous algorithms.

A Dynamic Leontief Modeling Approach to Management for Optimal Utilization in Water Resources Systems

A quantitative framework to the problem of management for optimal utilization in water resources systems is developed using a dynamic Leontief modeling approach. The framework is comprised of a supply-demand disequilibrium model which allows for excess demand or supply to exist at any time. A linear control regulator formulation is undertaken in which the weighted difference between the time rate of change of actual water levels and the excess water demand is used as the control instrument. Application to the River Nile system is performed using the presently available data bank information. Several computer experiments illustrate the validity of the modeling approach and its suitability as a planning tool.

Brief paper: Near optimal decentralised control with a pre-specified degree of stability

In this paper we develop a method for computing near optimal decentralised control with a pre-specified degree of stability for large scale, linear, interconnected dynamical systems. All the calculations in the new method are performed off-line using a three level hierarchical structure. We provide a condition the satisfaction of which ensures that the system has a pre-specified degree of stability. We also show that the control developed using the new method is more stable than the optimal decentralised control obtained by neglecting all interactions between the subsystems.

Controllers for linear interconnected dynamical systems with prespecified degree of stability

The approach of Anderson and Moore for the design of controllers for linear systems having a prespecified degree of stability is extended to the case of interconnected dynamical systems. The stability and performance of such controllers is studied when the system is subjected to structural perturbations as well as to loss of coordination information. For both the cases a stability condition is derived and bounds are developed for the performance loss.

Stability and performance robustness for multivariable linear systems

Abstract This paper deals with both aspects of stability and performance robustness of linear systems in the time domain. A linear time-invariant feedback control law which yields the largest set of parameter variations is first determined. It is then used as an initial guess for designing a robust control law which produces satisfactory performance for each of several operating points of the system given a priori. The stability robustness properties of the resulting control law is also studied. The proposed approach is applied to an example yielding larger stability bounds including satisfactory performance in comparison with recently reported results.

An adaptive observer for robots with persistent excitation

An adaptive state observer for rigid link manipulators is proposed. The proposed adaptive state observer is based on parametrizing the robot dynamic model via the estimated state and parameters, thus yielding a nominal model plus two perturbation terms, one is linear and the other is quadratic in the state and parameter estimation errors. The update law for the adaptive observer is derived using the MKY lemma (Narendra and Annaswamy 1989). Stability of the origin of the overall error system is shown using the Gronwall-Belmann lemma (Khalil 1992) and a converse Lyapunov theorem (Khalil 1992). The proposed adaptive observer is tested through simulations

Brief paper: A two-level parameter estimation algorithm using the multiple projection approach

An efficient two-level algorithm is developed for parameter estimation using the multiple projection approach. The optimal minimum variance estimate is achieved using a fixed number of iterations. Both the recursive and non-recursive versions of the algorithm are presented. Simulation results of two examples have indicated that the new two-level algorithm provides accurate estimates whilst needing a reduced amount of computational effort.

A Multiple Projection Technique for Parameter Estimation in Large Scale Systems

Abstract In this paper a powerful algorithm has been developed for parameter estimation in large scale systems using the multiple projection approach. The approach uses a fixed number of iterations between the two levels of the hierarchy in order to achieve the optimal minimum variance estimate of the parameters. First, the basic algorithm is developed and subsequently, it is extended to the case of recursive estimation.

Management and control of a complex airport terminal

Hierarchical control theory is applied to the problem of management of passengers and luggage in an airport terminal. For this purpose, the airport system is analyzed and a mathematical model representing the dynamics of the controlled sections is proposed. A well-known interaction prediction method is utilized to achieve optimal performance of the system through the minimization of the queue lengths and hence the waiting time of passengers. Simulation results from a case study of Cairo Airport are presented and discussed.<<ETX>>