S. Lloyd

Combined Petri net modelling and AI-based heuristic hybrid search for flexible manufacturing systems-part I: Petri net modelling and heuristic search

This two-part paper presents modelling and scheduling approaches for flexible manufacturing systems (FMS) using Petri nets (PNs) and artificial intelligence (AI) based heuristic search methods. In part I, the description of FMS formulation that will be considered throughout the paper is presented. A new class of PNs, Buffer-nets, for defining FMS is proposed, which enhances the modelling techniques for manufacturing systems with features that are considered difficult to model. An input language for automatic synthesis of these nets is developed. A scheduling architecture, which integrates PN models and AI techniques, is proposed. Finally, the complexity issues of manufacturing systems are addressed.

Integrating Petri nets and hybrid heuristic search for the scheduling of FMS

This paper studies modelling and scheduling of Flexible Manufacturing Systems (FMS) using Petri Nets (PNs) and Artificial Intelligence (AI) based on heuristic search methods. A subclass of PNs, Buffer nets or B-nets is obtained by the systematic synthesis of PN models from FMS formulations. Scheduling is performed as heuristic search in the reachability tree, which is guided by a new heuristic function that exploits PN information. This heuristic is derived from a new concept, the Resource Cost Reachability (RCR) matrix which builds on the properties of B-nets. To mitigate the complexity problem, a hybrid search algorithm is proposed. The algorithm combines dispatching rules based on analysis information provided by the PN simulation with a modified stage-search algorithm. Experimental results are provided and indicate the effectiveness of the approach and the potential of PN-based heuristic search for FMS scheduling.

Combined Petri net modelling and AI-based heuristic hybrid search for flexible manufacturing systems-part II: heuristic hybrid search

This two-part paper presents modelling and scheduling approaches of flexible manufacturing systems using Petri nets (PNs) and artificial intelligence (AI)-based heuristic search methods. In Part I, PN-based modelling approaches and basic AI-based heuristic search algorithms were presented. In Part II, a new heuristic function that exploits PN information is proposed. Heuristic information obtained from the PN model is used to dramatically reduce the search space. This heuristic is derived from a new concept, the resource cost reachability matrix, which builds on the properties of B-nets proposed in Part I. Two hybrid search algorithms, (1) an approach to model dispatching rules using analysis information provided by the PN simulation and (2) an approach of the modified stage-search algorithm, are proposed to reduce the complexity of large systems. A random problem generator is developed to test the proposed methods. The experimental results show promising results.

Combined direct and indirect adaptive control of constrained robots

The force tracking error is dependent on both the position tracking error and the estimated parameter error so that, in constrained adaptive robot control, the convergence of the estimated parameter error becomes more important than in unconstrained adaptive robot control. However, in the direct adaptive controller, the parameter adaptation is driven only by the tracking error in the joint motion, while in the indirect adaptive controller, the parameter estimation is driven only by the prediction error in the joint torque. Based on this observation a new combined direct and indirect adaptive controller driven by both the tracking error and the prediction error is developed for constrained robots with inertia parameter uncertainty. Stability of the combined adaptive controllers is established in the Lyapunov sense. The combined control improves both position and force tracking performance compared with direct adaptive control. The computer simulations are presented to demonstrate the proposed schemes.

An evolutionary hybrid scheduler based in Petri net structures for FMS scheduling

Addresses a hybrid scheduling methodology for flexible manufacturing systems (FMS) that uses Petri nets (PNs) as a modeling tool and several successfully employed scheduling methods: conflict-solving based on heuristic dispatching algorithms, artificial intelligence (AI) heuristic search, problem decomposition and evolutionary approximation algorithms as search tools. PNs have been traditionally employed in scheduling approaches based on discrete event simulation and more recently, the combination of PNs and AI heuristic search has produced interesting results. PNs also allow easy structural analysis towards a decomposition of the problem. In this paper PNs are employed as a representation paradigm and a decomposition-construction scheduling method is based on them. A PN-based AI systematic heuristic search is used to solve sub-problems which are progressively joined by an evolutionary building procedure. Experimental results based on a preliminary implementation of the method are presented.

FMS scheduling using Petri net modeling and a branch & bound search

An optimum scheduling algorithm for flexible manufacturing systems using Petri net modeling and modified branch and bound search is proposed in this paper. A Petri net modeling of the system is first discussed. Then the scheduling algorithm is developed using a modified branch and bound search. The method uses a Petri net model to generate and search a partial reachability graph, and presents an optimal makespan in terms of a firing sequence of transitions of the Petri net model of the system. A software package based an the Windows environment is described which produces a Petri net model satisfying the production requirement and this is used to obtain an optimum makespan through a modified branch and bound search. The results of several simulations are presented to demonstrate the validity of the proposed algorithm and show some improvement over previous work.

Adaptive control of robot manipulators including motor dynamics

An adaptive control scheme for robot manipulators including motor dynamics is proposed in this paper. The proposed scheme avoids the assumption that the values of motor parameters are known. An exponential control law is first developed under the assumption of no parameter uncertainty. This forms a controller structure for the adaptive control. Using this control structure, a full order adaptive control law is proposed to overcome parameter uncertainty for both manipulator links and motors. The asymptotic stability of the tracking error is proved using the Lyapunov stability theory. The method is further extended to the task space.

An Efficient Robust Adaptive Control Law For Robot Manipulators

A computationally efficient robust adaptive control algorithm is proposed in this paper. The regressors are implemented using the desired trajectories to replace the actual trajectories in order to reduce the computational burden. To reduce the disturbance introduced by this replacement, an adaptive variable structure control law is proposed. The proposed adaptive control law results in a system that is robust to bounded input disturbances. A small modification of the control law makes the system robust to more general input disturbances. The stability analysis is in the Lyapunov sense. Simulation results demonstrate the validity of the proposed scheme.

Adaptive Control of Robot Manipulators Including Motor Dynamics

An adaptive control scheme for robot manipulators including motor dynamics is proposed in this paper. The proposed scheme avoids the assumption that the values of motor parameters are known which is required in reference (13). An exponential control law is first developed under the assumption of no uncertainty. This forms a controller structure for the adaptive control. Using this control structure, a full-order adaptive control law is proposed to overcome parameter uncertainty for both robot link and motor. The stability analysis is in the Lyapunov stability sense. The method is further extended to the task space. Extensive simulations are performed to compare the different control schemes.