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This article presents the field-programmable gate array (FPGA) implementation of a particle swarm optimization (PSO)-based nonlinear model-predictive control (NMPC) for applications with millisecond timescales and resource-constrained embedded systems. A custom hardware circuit architecture of an NMPC controller, which mainly includes phase-locked loop clock, universal asynchronous receiver transmitter interface, trigonometric function, random number generator, objective function, and PSO solver modules, is implemented on the FPGA based on a hardware description language specification. It employs parallelism, pipelining, and specialized numerical formats to enhance the online computation performance of NMPC. In addition, the partial parallel expansion calculations of the fitness (objective) function and the particle position update are adopted for trading off the computational performance against resource usage. The hardware-in-the-loop experimental results through controlling a wheeled mobile robot model demonstrate that the design is capable of computing the required control action in real time by using a midrange FPGA, while still offering good control performance. [ABSTRACT FROM AUTHOR]

A maximally permissive (or optimal) supervisory control of an automated manufacturing system (AMS) modeled by Petri nets (PNs) can be usually implemented by imposing constraints in the form of a set of linear inequalities. To find such a set of linear constraints, in the existing work, an integer linear programming (ILP) problem is generally formulated and solved for some dominant markings obtained by reachability analysis and vector covering theory, which is computationally inefficient due to the combinatorial nature of solving ILPs to decide the coefficients of optimal linear constraints. This paper addresses the deadlock prevention problem for AMSs by developing efficient methods to reduce the computational overhead through the establishment of conditions on deciding the maximal permissiveness of linear constraints imposed on a system. By taking the advantage of structural properties of a PN model under consideration, we identify the most part of minimal covered illegal markings that can be optimally controlled via policies obtained by specific linear inequality constraints. Also, algorithms are developed to implement the proposed policies. The proposed approach can verify the optimality of a linear constraint efficiently without solving ILP problems. A linear programming method is developed to deal with the markings that cannot be processed by the proposed structural analysis. It is shown that for the considered class of PNs, called system of simple sequential processes with resources (S3PR), no mixed ILP problem needs to solve and the computational burden is dramatically reduced. Two examples are employed to demonstrate the efficiency of the developed method. [ABSTRACT FROM AUTHOR]

Demanufacturing aims to recover value and conserve energy from end-of-life (EOL) products, contributing to sustainable manufacturing. To make the full use of EOL products, they are usually disassembled into components that have different values and embodied energy at different EOL options. This article studies a disassembly planning (DP) that integrates the decisions on disassembly sequence and EOL strategy to maximize the recovered value and energy conservation from EOL products. We propose a multiobjective DP based on the value recovery and energy conservation (MDPVE) model, which is different from the existing DP models by focusing on the embodied energy rather than the energy consumption during disassembly. An adapted multiobjective artificial bee colony (ABC) algorithm [multiobjective ABC (MOABC)] is developed to identify the Pareto solutions for the MDPVE and is compared with a well-known metaheuristic algorithm, Non-dominated Sorting Genetic Algorithm-II (NSGA-II). A real-world case study demonstrated the superior solution quality and computational efficiency of MOABC. Note to Practitioners—There is often more than one treatment option for EOL products or components, including reuse, remanufacturing, and recycling. However, the decision on which EOL option to select is not considered in most of the DP studies by assuming an EOL option given for each component. Hence, the disassembly plan with the EOL decision is focused in this article. As energy sustainability gains an increasing attention, it is essential to assess the profitability and energy conservation simultaneously for EOL products. Since there could be a tradeoff between recovered profit and conserved energy, a multiobjective evolutionary algorithm is developed for generating Pareto solutions which help decision-makers to find good solutions for both evaluation indicators. [ABSTRACT FROM AUTHOR]

In semiconductor manufacturing, a process module (PM) failure in cluster tools (CTs) happens from time to time. To effectively operate a CT, such a failure should be handled in a proper and timely manner. This issue becomes much more complicated because wafer residency time constraints (WRTCs) must be met to ensure the quality for some wafer fabrication processes. With such constraints, if a tool is operated under a periodic schedule and a PM fails, it is desired that the tool can still operate under a periodic schedule if it is possible. Nevertheless, the periodic schedule after a PM failure must be different from that before its failure since in this case the tool is degraded. Thus, there must be a transient process between them. It is a great challenge to operate a tool such that it can go through such a transient process with WRTCs being always satisfied. This paper aims to solve this problem by proposing PM failure response policies which can successfully transfer a CT to the feasible schedule after failure from the one before a failure. Then, efficient algorithms are developed to improve these response policies. The proposed policies are composed of simple control laws such that they can be realized in real time and online. Illustrative examples are presented to show their applications. [ABSTRACT FROM AUTHOR]

This paper deals with the medium between two reconfigurable sensor nodes characterized by radio interfaces that support multiple channels for exchanging real-time messages under energy constraints. These constraints are violated if the consumed energy in transmission is higher than the remaining quantity of energy. A reconfiguration, i.e., any addition or removal of tasks in devices and consequently of messages on the medium, can cause the violation of real-time or energy constraints at run time. To achieve a feasible scheduling in time (i.e., message deadlines will be respected) and energy (i.e., there is available energy) on the medium, we propose new dynamic solutions: Balance, Dilute, and a Combination of them to manage any addition or removal of messages. The proposed approach utilizes the energy harvesting techniques and the PowerControl algorithm to reduce the nonharvested consumed energy. The proposed strategies achieve significant improvement over existing methods and provide the highest percentage of adding messages, with a lower average in response time and energy consumption. They reach a percentage of success in adding the highest priority messages while meeting deadlines up to 85%. [ABSTRACT FROM AUTHOR]

This paper proposes a personalized adaptive cruise control (ACC) system based on driving style recognition and model predictive control (MPC) to meet different driving styles while ensuring car-following, comfort and fuel-economy performances. To obtain the controller parameters corresponding to different driving styles, a set of real vehicle experiments are conducted to collect driving data of 66 randomly recruited drivers, then the experimental data is clustered through unsupervised machine learning method. On the basis of it, a driving style classifier is designed by supervised machine learning method, which can be used to recognize the driving style of drivers online. Then, the control problem of the personalized ACC system is described as a multi-objective optimization problem which is solved by MPC method. The simulation results show that the proposed personalized ACC system can meet the requirements of different driving styles and guarantee various performances. [ABSTRACT FROM AUTHOR]

This paper deals with the formal verification of reconfigurable discrete event control systems (RDECSs) using reconfigurable timed net condition/event systems (R-TNCESs) formalism. A reconfigurable system switches from a mode to another during its working process to adapt its behavior to the related environment. By including such a feature, RDECSs become complex and their verification is often expensive in terms of computation time and memory. In this paper, a new methodology for formal verification of RDECSs is proposed in order to ensure the correctness of these systems with a reduced cost (decreasing the verification time and memory occupation). The proposed contribution includes an improved modeling and verification of RDECSs. The modeling with R-TNCESs is enriched with all reconfiguration forms, and the verification involves an improvement method that avoids any redundancy and cancels unnecessary calculations. In addition, a visual tool called Rec-AG based on the proposed methodology is developed. The performance evaluation of this paper is achieved by measuring computation time and memory for several systems and different sizes of the problem. This paper’s contribution is applied to the benchmark production system FESTO. [ABSTRACT FROM AUTHOR]

Time-varying disturbances may severely deteriorate control performance. For instance, the time-varying vehicle mass and the clutch friction coefficient may make automated manual transmission vehicle fail to startup on a slope. A quadratic output regulator is proposed in this brief to deal with time-varying disturbances, and the unmeasurable disturbances are estimated by a disturbance observer. To derive the control law, a novel form of multiplier function $\lambda $ is introduced, and the control law is represented as a feedback form of the system states, the disturbances and their derivatives. The system is guaranteed to be input-to-state stable, which will significantly relax the dependence of the controller on modeling accuracy. Eventually, the proposed control law is validated on clutch slip control. Although the performance of clutch slip control is susceptible to the uncertainties in dry clutch system and the disturbances from external environment, the simulation and experimental results demonstrate that the control law is robust enough and has strong ability in rejecting time-varying disturbances, and the vehicle can be launched successfully with high performance through clutch slip control under various working scenarios. [ABSTRACT FROM AUTHOR]

Scheduling a cluster tool with wafer residency time constraints is challenging and important in wafer manufacturing. With a backward strategy, the scheduling problem of such single-robot-arm cluster tools is well-studied in the literature. It is much more challenging to schedule a more general case whose optimal scheduling strategy is not limited to the backward one. This work uses a timed Petri net (PN) to model the dynamic behavior of the system and presents a method to determine the optimal scheduling strategy for the system. Based on its PN model and the obtained strategy, it reveals that the key issue to schedule such a tool is to determine when and how long the robot should wait for. Based on this finding, this work establishes for the first time the necessary and sufficient conditions regarding the existence of an optimal and feasible one-wafer cyclic schedule for single-robot-arm cluster tools. It then formulates a computationally efficient linear program to find it if existing, and finally gives industrial examples to show the application and power of the proposed method. [ABSTRACT FROM AUTHOR]

Reinforcement learning is regarded as a potential method to be applied in automated vehicles, but the stability and efficiency of algorithms are concerns. To improve them, the deterministic promotion reinforcement learning method is put forward, which can promote the policy determinately. Correspondingly, the policy evaluation in critic and the exploration in actor are improved, which combines a normalization-based evaluation and a model-free search guide. The aim is finding the right action exploration direction by critic, then the direction is used to update and guide action exploration in actor. The modified method decreases the dependencies of exploring a good action for promotional updating and only makes deterministic promotion in policy. Consequently, the efficiency of the algorithm is improved without loss in stability. More notably, it can relieve the cold-start and circumvent the limitations in learning with constrained physical systems. To verify the proposed method, the longitudinal velocity control problem for automated vehicles is considered, which contains car-following and non-car-following conditions in a unitized form. The learning system is established in Carsim. Furthermore, some different reinforcement learning technologies are used to accelerate learning. Real-vehicle experiments for validation are also given. The results indicate that the proposed method can achieve permissible learning performance in the longitudinal velocity continuous control problem. [ABSTRACT FROM AUTHOR]

The cloud datacenter network is consistently undergoing changing, due to a variety of topology and traffic updates, such as the VM migrations. Given an update event, prior methods focus on finding a sequence of lossless transitions from an initial network state to an end network state. They, however, suffer frequent and global search of the feasible end network states. This incurs non-trivial computation overhead and decision-making delay, especially in large-scale networks. Moreover, in each round of transition, prior methods usually cause the cascaded migrations of existing flows; hence, significantly disrupt production services in IaaS data centers. To tackle such severe issues, we present a simple update mechanism to minimize the amount of flow migrations during the congestion-free network update. The basic idea is to replace performing the sequence of global transitions of network states with local reschedule of involved flows, caused by an update event. We first model all involved flows due to an update event as a set of new flows, and then propose a heuristic method Lupdate. It motivates to locally schedule each new flow into the shortest path, at the cost of causing the extra migration of at most one existing flow if needed. To minimize the amount of migrated traffic, the migrated flow should be as small as possible. To further improve the success rate, we propose an enhanced method Lupdate-S. It shares the similar design of Lupdate, but permits to migrate multiple necessary flows on the shortest path allocated to each new flow. We conduct large-scale trace-driven evaluations under widely used Fat-Tree and ER data centers. The experimental results indicate that our methods can realize congestion-free network with as less amount of traffic migration as possible even when the link utilization of a majority of links is very high. The amount of traffic migration caused by our Ludpate method is 1.2 times and 1.12 times of the optimal result in the Fat-Tree and ER random networks, respectively. [ABSTRACT FROM AUTHOR]

Wafer revisiting and residency time constraints complicate the scheduling problem of cluster tools in semiconductor manufacturing. Random disturbance to the activity time in operating a tool further complicates such a scheduling problem. To solve this challenging problem, this paper proposes a robust real-time schedule which consists of a real-time controller (RTC) and an off-line schedule. The former is developed to offset the activity time disturbance such that the wafer sojourn time fluctuation in a process module is minimized. With the RTC, to find the off-line schedule, necessary and sufficient schedulability conditions under which a feasible schedule exists are derived and these conditions can be easily checked. Then, the off-line schedule can be efficiently found by the proposed algorithms based on nondisturbed activity time if a feasible schedule exists. With the obtained real-time schedule, it is shown that the productivity of the system is maximized. Finally, examples are used to illustrate the proposed approach. [ABSTRACT FROM AUTHOR]

Recent results have made a promising case for offering oversubscribed wired data center networks (DCN) with extreme costs. Inter-rack wireless networks are drawing intensive attention to augment such wired DCNs with a few wireless links. Inspired by the promise of easy deployment and plug-and-play, we present VLCcube, a novel inter-rack wireless solution that extends the design of wireless DCN into three further dimensions: (1) all inter-rack links are wireless; (2) there is no imposition of any infrastructure-level alteration on wired production data centers; and (3) it should be plug-and-play, without any need of additional mechanical or electronic control operations. This vision, if realized, will lead to increased flexibility, reduced reconstructing cost, simplified configuration and usage, and outstanding compatibility with existing wired DCNs. Previous proposals, however, are opposed to the last two design rationales. To achieve this vision, the proposed VLCcube augments Fat-Tree, a representative DCN in production data centers, by organizing all racks into a wireless Torus structure via the emerging visible light links. We further present the topology design, hybrid routing, and flow scheduling schemes for VLCcube. Extensive evaluations indicate that VLCcube outperforms Fat-Tree significantly under the existing ECMP flow scheduling scheme, irrespective of the undergoing traffic pattern. Moreover, the performance of VLCcube can be significantly promoted by our congestion-aware flow scheduling scheme. More precisely, compared to ECMP, our flow scheduling scheme makes VLCcube achieve \times 1.50 throughput under batched flows, \mathrm{\times}2.21 and \times 2.59$ throughput under two different kinds of online flows. [ABSTRACT FROM AUTHOR]

Great advances in simulation-based vehicle system design and development of various driver assistance systems have enhanced the research on improved modeling of driver steering skills. However, little effort has been made on developing driver steering skill models while capturing the uncertainties or statistical properties of the vehicle-road system. In this paper, a stochastic model predictive control (SMPC) approach is proposed to model the driver steering skill, which effectively incorporates the random variations in the road friction and roughness, a multipoint preview approach, and a piecewise affine (PWA) model structure that are developed to mimic the driver's perception of the desired path and the nonlinear internal vehicle dynamics. The SMPC method is then used to generate a steering command by minimization of a cost function, including the lateral path error and ease of driver control. In the analyses, first, the experimental data of Hongqi HQ430 are used to validate the driver steering skill controller. Then, the parametric studies of control performance during a nonlinear steering maneuver are provided. Finally, further discussions about the driver's adaption and the indication on vehicle dynamics tuning are given. The proposed switching-based SMPC driver steering control framework offers a new approach for driver behavior modeling. [ABSTRACT FROM PUBLISHER]

In a previous paper in this journal named The TOC-Based Algorithm for Solving Multiple Constraint Resources, Ray considered an integrated heuristic approach named AHP/TOC comprising the analytic hierarchy process (AHP) and theory of constraints (TOC) in a multiple constraints resource product mix problem. This paper gives three typical examples and points out that the proposed approach would not generate the optimal solution. Furthermore, the reasons are analyzed in terms of the ranking approach of product priority using AHP and the adjustment approach of product mix after a new bottleneck has been identified. We clarify the cases under which the AHP/TOC method can and cannot output the optimal solution. Finally, some possible improvements are given. [ABSTRACT FROM AUTHOR]