This study proposes a synchronization control of chaotic systems by using an adaptive recurrent wavelet cerebellar model articulation controller (RWCMAC). The proposed adaptive RWCMAC system contains a RWCMAC and a fuzzy compensation controller. Due to favorable capability of wavelet, the wavelet function is used as a base function of CMAC. Based on Lyapunov stability theory, the parameters of RWCMAC are on-line tuned; and the fuzzy compensation controller is designed to eliminate approximation error between ideal controller and RWCMAC. The developed control system is applied to control an unified chaotic system. The simulation results demonstrate the effectiveness of the proposed control scheme.

This study proposes an adaptive wavelet cerebellar -model-articulation-controller (AWCMAC) for multi-input multi-output nonlinear systems. In this proposed scheme, wavelet cerebellar-model articulation-controller (WCMAC) is the main controller utilized to mimic an ideal controller and the parameters of WCMAC are on-line adjusted by the derived tuning algorithm. Moreover the robust controller is designed to dispel the residual of the approximation error for achieving H∞robust performance. PSO algorithm is used to tune the learning-rates to achieve fast learning speed of AWCMAC. The simulation results of a three-link robotic arm demonstrate the effectiveness of the proposed control scheme.

This study addresses synchronization of two chaotic gyros by using an adaptive recurrent wavelet cerebellar model articulation controller (RWCMAC). The proposed adaptive RWCMAC system contains an RWCMAC and a robust controller. Based on Lyapunov stability theory, the parameters of RWCMAC are on-line tuned and the robust controller is designed for achieving H ∞ robust performance. Finally, the proposed adaptive RWCMAC system is applied to synchronize two chaotic gyros. Numerical simulation results demonstrate the effectiveness of the proposed control scheme.

An output recurrent cerebellar model articulation controller (ORCMAC) via the backstepping control technique is designed to control a linear ultrasonic motor (LUSM) for the tracking of periodic reference trajectories in this paper. The proposed ORCMAC has superior capability to the conventional cerebellar model articulation controller (CMAC) in efficient learning mechanism and dynamic response. In the ORCMAC backstepping control system, an adaptive ORCMAC is used to mimic an ideal backstepping control law and a compensated controller is designed to compensate for the difference between the ideal backstepping control law and the adaptive ORCMAC. Moreover, the Taylor linearization technique is employed to derive the linearized model of the ORCMAC. The adaptation laws of the control system are derived in the sense of Lyapunov stability analysis, so that the stability of the system can be guaranteed. Finally, the effectiveness of the proposed control system is verified by the experiments of LUSM motion control. Experimental results show that high-precision tracking response can be achieved by using the proposed ORCMAC backstepping control system.

We propose a modeling tool named QCPN (queueing colored Petri net). The main idea of this tool is to combine the original CTPN (colored timed Petri net) with the queueing systems. With the QCPN model, we can predict the delivery date of any specific product under some scheduling policies efficiently and rather precisely. In the scheduling phase, we use the GA based approach to search for the optimal combination of a number of heuristic rules. This QCPN based GA scheduler can greatly reduce the computation time so as to meet the need for a rapidly changing environment.

We can decide the operation order with unknown potential order requirements using the scheduling architecture proposed in the paper. First, the branch-and-bound search based on a Markov chain method is proposed. The Markov chain gets the service rate records and arrival rate records from the manufacturing execution system. We can get the possible beginning times of operations for each job via the Markov chain. The information of the possible beginning time can help us to approximate the solution space. Thus, by the information of the possible beginning times of operations, a branch-and-bound search scheduler can be used to find a sub-optimal scheduling.

A genetic algorithm (GA) embedded search strategy over a colored timed Petri net (CTPN) for wafer fabrication is proposed. Through the CTPN model, all possible behaviors of the wafer manufacturing systems, such as WIP status and machine status, can be completely tracked down by the reachability graph of the net. The chromosome representation of the search nodes in GA is constructed directly from the CTPN model, recording information about the appropriate scheduling policy for each workstation in the fabrication. A better chromosome found by GA is received by the CTPN based schedule builder, and a near-optimal schedule is then generated.

A significant amount of risk is involved in the wafer fabrication due to huge investment costs, long production cycle time, and short production life cycle. In this paper, a genetic algorithm (GA) embedded search strategy over a hybrid color-timed Petri-net (HCTPN) for wafer fabrication is proposed. Through the HCTPN model, all possible behaviors of the wafer manufacturing systems such as WIP status and machine status can be completely tracked down by the reachability graph of the net. The chromosome representation of the search nodes in GA is constructed directly from the HCTPN model, recording the information about the appropriate scheduling policy for each workstation in the fabrication. A better chromosome found by GA is received by the HCTPN based schedule builder, and then a near-optimal schedule is generated.

This paper proposes a new generation of evaluation tool for online design and scheduling, wherein the planned control policy designed by engineers can be online evaluated. Performance can be evaluated by a new prediction rule that differs from existent state-independent or steady state model such as a queuing network model. In addition to that, a new optimal control rule or schedule can then be derived from the originally planned ones and the feedback from the manufacturing engineers. Simulation and prediction methods are applied together to give an optimum in the time slot of snapshot. Such evaluation tool can also be used to look for the possible conflicts in the potential solution and suggest some necessary steps to avoid those conflicts.

Nowadays, the market place is continuously changing and unpredictable, and hence an efficient prototyping environment is crucial. We propose a virtual factory wherein an efficient prototyping testbed is provided. This paper is one among very few that tries to represent the virtual factory in an analytic form so that many existing mathematical analysis can be applied. New pseudo resources can be added to form a new virtual environment, and control policy designed by engineers will be evaluated before being issued. An example of utilizing the prototyping testbed is given. The proposed testbed is compared with a traditional testbed and the results validate the intelligence and efficiency of the present prototyping testbed.

Proposes a systematic method of creation and application of a virtual factory. Basic definition of a virtual factory is first given, followed by its foundation principles and architectural overview, and then a systematic method based on object technologies is proposed to create the virtual factory. Specially, for a real manufacturing environment, a corresponding virtual factory can be created via the polymorphism parameterized universal virtual factory with several interactions. The virtual factory can be linked to the real factory and enable simulation-based control by means of the switching architecture we proposed. Its reliable predictions can not only prove the production scenarios but also improve the decision making process of acquisition managers in the applications for flexible automated production. The present work is being applied to the experimental robotized flexible assembly system developed by Intelligent Robotics and Automation Laboratory in National Taiwan University.