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Automation Science and Engineering, IEEE Transactions on

Issue 4 • Date Oct. 2014

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Displaying Results 1 - 25 of 38
  • Table of contents

    Page(s): C1 - B962
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    Freely Available from IEEE
  • IEEE Transactions on Automation Science and Engineering publication information

    Page(s): C2
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    Freely Available from IEEE
  • Guest Editorial Integrated Optimization of Industrial Automation

    Page(s): 963 - 964
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    Freely Available from IEEE
  • Integrated Optimization for the Automation Systems of Mineral Processing

    Page(s): 965 - 982
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3457 KB) |  | HTML iconHTML  

    The whole production line of hematite ore processing is composed of raw ore processing, shaft furnace roasting, grindings, and magnetic separation production phases. Their automation systems consist of the process control part and the operational optimization system. The target of the optimal operational control is to optimize the concerned operational indices, namely, the intermediate product quality, efficiency, and consumptions. The dynamics between the operational indices and the global production indices (i.e., the total concentration grade, metal recovery rate, production rate, beneficiation ratio, and costs) with month, day, and hour time scales changes in line with the variations of production conditions, composition of raw ore together with capability of equipment. These indices are difficult to measure online and as a result it is difficult to model accurately. Moreover, there are characteristics in terms of both interconnections and conflictions among these indices. This leads to isolated operation of individual automation systems for these processes and the optimization of global production indices for whole production line cannot be realized. This paper presents a novel problem description for the integrated optimization of the automation systems of mineral processing. For this purpose, the analysis is made on the difficulty of using the existing optimization methods-based decision making methods to obtain the integrated optimization of the automation systems. The integrated optimization strategy for the automation systems of mineral processes is proposed using our previously established target value optimization of global production indices , two time scales decomposition approach and target value optimization of operational indices. The proposed strategy aims at realizing the optimization of global production indices. Using real data from a mineral processing plant on hematite beneficiation process, relevant simulations, and real industrial experimen- s have been carried out. The obtained experimental results show the efficiency and effectiveness of the proposed strategy. View full abstract»

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  • A Quality-Relevant Sequential Phase Partition Approach for Regression Modeling and Quality Prediction Analysis in Manufacturing Processes

    Page(s): 983 - 991
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1408 KB) |  | HTML iconHTML  

    Competition and demand for consistent and high-quality product have spurred the development of quality prediction methods for industrial manufacturing processes. Multiplicity of phases is, in general, common nature of many batch manufacturing processes. Considering that different phases may have different effects on qualities, one of the key issues is how to partition the whole batch process into multiple phases. In the present work, an automatic quality-relevant step-wise sequential phase partition (QSSPP) algorithm is developed for phase-based regression modeling and quality prediction. It considers the time sequence of operation phases and can capture the time-varying quality prediction relationships. Using this algorithm, phases are separated in order from quality-relevant perspective, revealing different quality prediction relationships. The phase-based regression system is set up for online quality prediction and the online prediction results are quantitatively evaluated for each phase. The feasibility and performance of the proposed algorithm are illustrated by an important manufacturing process, injection molding. View full abstract»

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  • Data-Driven Soft-Sensor Modeling for Product Quality Estimation Using Case-Based Reasoning and Fuzzy-Similarity Rough Sets

    Page(s): 992 - 1003
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3057 KB) |  | HTML iconHTML  

    Efficient operation of the integrated optimization or automation system in an industrial plant depends mainly on good measurement of product quality. However, measuring or estimating the product quality online in many industrial plants is usually not feasible using the available techniques. In this paper, a data-driven soft-sensor using case-based reasoning (CBR) and fuzzy-similarity rough sets is proposed for product quality estimation. Owning to the sustained learning ability, the modeling of a CBR soft-sensor does not need any additional model correction which is otherwise required by the neural network based methods to overcome the slow time-varying nature of industrial processes. Because the conventional k-nearest neighbor ( k-NN) algorithm is strongly influenced by the value of k, an improved k-NN algorithm with dynamic adjustment of case similarity threshold is proposed to retrieve sufficient matching cases for making a correct estimation. Moreover, considering that the estimation accuracy of the CBR soft-sensor system is closely related to the weights of case feature, a feature weighting algorithm using fuzzy-similarity rough sets is proposed in this paper. This feature weighting method does not require any transcendental knowledge, and its computation complexity is only linear with respect to the number of cases and attributes. The developed soft-sensor system has been successfully applied in a large grinding plant in China. And the application results show that the system has achieved satisfactory estimation accuracy and adaptation ability. View full abstract»

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  • Modeling and Simulation of Whole Ball Mill Grinding Plant for Integrated Control

    Page(s): 1004 - 1019
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2478 KB) |  | HTML iconHTML  

    This paper introduces the development and implementation of a ball mill grinding circuit simulator, NEUSimMill. Compared to the existing simulators in this field which focus on process flowsheeting, NEUSimMill is designed to be used for the test and verification of grinding process control system including advanced control system such as integrated control. The simulator implements the dynamic ball mill grinding model which formulates the dynamic responses of the process variables and the product particle size distribution to disturbances and control behaviors as well. First principles models have been used in conjunction with heuristic inference tools such as fuzzy logic and artificial neural networks: giving rise to a hybrid intelligent model which is valid across a large operating range. The model building in the simulator adopts a novel modular-based approach which is made possible by the dynamic sequential solving approach. The simulator can be initiated with connection to a real controller to track the plant state and display in real-time the effect of various changes on the simulated plant. The simulation model and its implementation is verified and validated through a case of application to the design, development, and deployment of optimal setting control system. View full abstract»

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  • Adaptive Dynamic Programming for Optimal Tracking Control of Unknown Nonlinear Systems With Application to Coal Gasification

    Page(s): 1020 - 1036
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (5948 KB) |  | HTML iconHTML  

    In this paper, we establish a new data-based iterative optimal learning control scheme for discrete-time nonlinear systems using iterative adaptive dynamic programming (ADP) approach and apply the developed control scheme to solve a coal gasification optimal tracking control problem. According to the system data, neural networks (NNs) are used to construct the dynamics of coal gasification process, coal quality and reference control, respectively, where the mathematical model of the system is unnecessary. The approximation errors from neural network construction of the disturbance and the controls are both considered. Via system transformation, the optimal tracking control problem with approximation errors and disturbances is effectively transformed into a two-person zero-sum optimal control problem. A new iterative ADP algorithm is then developed to obtain the optimal control laws for the transformed system. Convergence property is developed to guarantee that the performance index function converges to a finite neighborhood of the optimal performance index function, and the convergence criterion is also obtained. Finally, numerical results are given to illustrate the performance of the present method. View full abstract»

    Open Access
  • Adaptive Observer Based Data-Driven Control for Nonlinear Discrete-Time Processes

    Page(s): 1037 - 1045
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    In this paper, two adaptive observer-based strategies are proposed for control of nonlinear processes using input/output (I/O) data. In the two strategies, pseudo-partial derivative (PPD) parameter of compact form dynamic linearization and PPD vector of partial form dynamic linearization are all estimated by the adaptive observer, which are used to dynamically linearize a nonlinear system. The two proposed control algorithms are only based on the PPD parameter estimation derived online from the I/O data of the controlled system, and Lyapunov-based stability analysis is used to prove all signals of close-loop control system are bounded. A numerical example, a steam-water heat exchanger example and an experimental test show that the proposed control algorithm has a very reliable tracking ability and a satisfactory robustness to disturbances and process dynamics variations. View full abstract»

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  • Nonlinear Model Predictive Control for DFIG-Based Wind Power Generation

    Page(s): 1046 - 1055
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2443 KB) |  | HTML iconHTML  

    Reliable control and optimal operation of the doubly fed induction generator (DFIG) is necessary to ensure high efficiency and high load-following capability in modern wind power plants. This is often difficult to achieve using conventional linear controllers, as wind power plants are nonlinear and contain many uncertainties. Furthermore, unbalanced conditions often exist on the power network, which can degrade DFIG system performance. Considering the nonlinear DFIG dynamics, this paper proposes a nonlinear modeling technique for DFIG, meanwhile taking into account unbalanced grid conditions. Then, a nonlinear model predictive controller is derived for power control of DFIG. The prediction is calculated based on the input-output feedback linearization (IOFL) scheme. The control is derived by optimization of an objective function that considers both economic and tracking factors under realistic constraints. The simulation results show that the proposed controller can effectively reduce wear and tear of generating units under normal grid conditions, and reduce the rotor over-current under unbalanced grid conditions, thereby improving the ability of grid-connected wind turbines to withstand grid voltage faults. View full abstract»

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  • Robust Model Predictive Control Under Saturations and Packet Dropouts With Application to Networked Flotation Processes

    Page(s): 1056 - 1064
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2400 KB) |  | HTML iconHTML  

    This paper investigates the problem of robust model predictive control (RMPC) with saturations and packet dropouts. In this model, polytopic uncertainties are adopted to describe the inconsistency arising from the discretization process of sampling, while the occurrence probabilities of packet dropouts are time-varying and saturations are taken into account to describe input and output signals. The problem of exponential RMPC with saturations and packet dropouts is solved and characterized by a convex optimization problem. The developed results of RMPC are then applied to networked flotation processes, which are made up of three layers: direct control layer, set-point control layer, and optimization layer. The RMPC is used for compensating the output information from the optimization layer to the direct control layer such that the desired economic objective can be achieved. Simulations are presented to show the effectiveness of the proposed method. View full abstract»

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  • Optimal Subtask Allocation for Human and Robot Collaboration Within Hybrid Assembly System

    Page(s): 1065 - 1075
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1931 KB) |  | HTML iconHTML  

    In human and robot collaborative hybrid assembly cell as we proposed, it is important to develop automatic subtask allocation strategy for human and robot in usage of their advantages. We introduce a folk-joint task model that describes the sequential and parallel features and logic restriction of human and robot collaboration appropriately. To preserve a cost-effectiveness level of task allocation, we develop a logic mathematic method to quantitatively describe this discrete-event system by considering the system tradeoff between the assembly time cost and payment cost. A genetic based revolutionary algorithm is developed for real-time and reliable subtask allocation to meet the required cost-effectiveness. This task allocation strategy is built for a human worker and collaborates with various robot co-workers to meet the small production situation in future. The performance of proposed algorithm is experimentally studied, and the cost-effectiveness is analyzed comparatively on an electronic assembly case. View full abstract»

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  • Optimization of Advertising Budget Allocation Over Time Based on LS-SVMR and DE

    Page(s): 1076 - 1082
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (925 KB) |  | HTML iconHTML  

    The advertising budget allocation problem for financial service is dealt with based on statistical learning and evolutionary computation in this paper. Taking the carry-over effects of the advertising into account, the least squares support vector machine regression (LS-SVMR) is used to construct the response model. A comparison between the proposed response model and traditional regression method based market response models is implemented. The results show the effectiveness and validity of the former model. Taking the budgets allocated to every month in the planning horizon as decision variables, the budget allocation optimization model is built and an improved differential evolution algorithm is used to find the optimal solutions. Finally, the proposed budget allocation method is illustrated by a practical problem. View full abstract»

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  • Optimization of Total Energy Consumption in Flexible Manufacturing Systems Using Weighted P-Timed Petri Nets and Dynamic Programming

    Page(s): 1083 - 1096
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2532 KB) |  | HTML iconHTML  

    Schedule optimization is crucial to reduce energy consumption of flexible manufacturing systems (FMSs) with shared resources and route flexibility. Based on the weighted p-timed Petri Net (WTPN) models of FMS, this paper considers a scheduling problem which minimizes both productive and idle energy consumption subjected to general production constraints. The considered problem is proven to be a nonconvex mixed integer nonlinear program (MINLP). A new reachability graph (RG)-based discrete dynamic programming (DP) approach is proposed for generating near energy-optimal schedules within adequate computational time. The nonconvex MINLP is sampled, and the reduced RG is constructed such that only reachable paths are retained for computation of the energy-optimal path. Each scheduling subproblem is linearized, and each optimal substructure is computed to store in a routing table. It is proven that the sampling-induced error is bounded, and this upper bound can be reduced by increasing the sampling frequency. Experiment results on an industrial stamping system show the effectiveness of our proposed scheduling method in terms of computational complexity and deviation from optimality. View full abstract»

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  • Beam Search Combined With MAX-MIN Ant Systems and Benchmarking Data Tests for Weighted Vehicle Routing Problem

    Page(s): 1097 - 1109
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2091 KB) |  | HTML iconHTML  

    In real-world cargo transportation, there are charges associated with both the traveling distance and the loading quantity. Cargo trucks must comply with a mandatory lower carbon emissions policy: the emissions of large-volume cargo truck/containers depend greatly on the cargo loading and the traveling distance. To address this issue, instead of assuming a constant vehicle loading from one customer to another, a variable vehicle loading should be used in optimizing the vehicle routine, which is known as a weighted vehicle routing problem (WVRP) model. The WVRP is an NP-hard problem; thus, the purpose of this paper is to develop a BEAM-MMAS algorithm that combines a MAX-MIN ant system with beam search to show that the WVRP is more effective than the VRP and to determine the types of VRP instances for which the WVRP has more cost-savings than the VRP. To this end, computational experiments are carried out on benchmark problems of the capacitated VRP for seven types of distributions, and the effectiveness of the BEAM-MMAS algorithm is compared with that of general ACO and MMAS algorithms for large-size benchmarking instances. The benchmarking tests show that lower operation costs are produced using the WVRP than using the optimal or best known paths of the CVRP and that the WVRP can increase cost savings for the instances with a dispersed customer distribution and a large weight. View full abstract»

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  • Using Lagrangian Relaxation Decomposition With Heuristic to Integrate the Decisions of Cell Formation and Parts Scheduling Considering Intercell Moves

    Page(s): 1110 - 1121
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1674 KB) |  | HTML iconHTML  

    Cell formation and parts scheduling are two important correlated processes in a cellular manufacturing system; however, the decisions involved in these processes are typically made individually. Determining how to integrate these decisions effectively to pursue a productive and lower cost system has become an important issue. This paper focuses on providing an effective solution to integrate the decisions of cell formation and parts scheduling, while considering intercell moves by using a Lagrangian relaxation decomposition method. A mixed integer nonlinear programming mathematical model (CFPSP) is proposed to determine which part families and machine groups are assigned to cells and in which sequence the parts are processed in the machines to minimize the total tardiness penalty cost. To effectively solve the model, a Lagrangian relaxation decomposition method with a heuristic (LRDH) is developed. Using the LRDH, the CFPSP model is solved by decomposing the model into two subproblems, i.e., the cell formation subproblem (CFPSP-FD) and the parts scheduling subproblem (CFPSP-SD). After linearizing the CFPSP-FD model, the subproblem CFPSP-FD is solved by the MIP optimizer CPLEX. A scatter search approach is developed to solve the subproblem CFPSP-SD. Combined with the Lagrange multipliers, the CFPSP-SD model takes into consideration the assignment of part families and the associated machine groups to each cell, when it sequences the processing of the parts on each machine in cells. An illustration of the application of the CFPSP model in an electronic appliance cellular manufacturing enterprise in China is presented. View full abstract»

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  • Solving an Extended Double Row Layout Problem Using Multiobjective Tabu Search and Linear Programming

    Page(s): 1122 - 1132
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1801 KB) |  | HTML iconHTML  

    Facility layout problems have drawn much attention over the years, as evidenced by many different versions and formulations in the manufacturing context. This paper is motivated by semiconductor manufacturing, where the floor space is highly expensive (such as in a cleanroom environment) but there is also considerable material handling amongst machines. This is an integrated optimization task that considers both material movement and manufacturing area. Specifically, a new approach combining multiobjective tabu search with linear programming is proposed for an extended double row layout problem, in which the objective is to determine exact locations of machines in both rows to minimize material handling cost and layout area where material flows are asymmetric. First, a formulation of this layout problem is established. Second, an optimization framework is proposed that utilizes multiobjective tabu search and linear programming to determine a set of non-dominated solutions, which includes both sequences and positions of machines. This framework is applied to various manufacturing situations, and compared with an exact approach and a popular multiobjective genetic algorithm optimization algorithm. Experimental results show that the proposed approach is able to obtain sets of Pareto solutions that are far better than those obtained by the alternative approaches. View full abstract»

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  • Monitoring for Nonlinear Multiple Modes Process Based on LL-SVDD-MRDA

    Page(s): 1133 - 1148
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3630 KB) |  | HTML iconHTML  

    This study proposes an online monitoring technique for nonlinear multiple-mode problems in industrial processes. The contributions of the proposed technique are summarized as follows: 1) Lazy learning (LL), a new adaptive local modeling method, is introduced for multiple-mode process monitoring. In this method, multiple modes are separated and accurately modeled online, and the between-mode dynamic process is considered. 2) The modified receptor density algorithm (MRDA) exhibiting superior nonlinear ability is introduced to analyze the residuals between the actual system output and the model-predicted output. The simulation of the Tennessee Eastman process with multiple operation modes shows that compared with other techniques mentioned in this study, the proposed technique performs more accurately and is more suitable for nonlinear processes with multiple operation modes. View full abstract»

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  • A Bayesian Networks Structure Learning and Reasoning-Based Byproduct Gas Scheduling in Steel Industry

    Page(s): 1149 - 1154
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (639 KB) |  | HTML iconHTML  

    It is very crucial for the byproduct gas system in steel industry to perform an accurate and timely scheduling, which enables to reasonably utilize the energy resources and effectively reduce the production cost of enterprises. In this study, a novel data-driven-based dynamic scheduling thought is proposed for the real-time gas scheduling, in which a probability relationship described by a Bayesian network is modeled to determine the adjustable gas users that impact on the gas tanks level, and to give their scheduling amounts online by maximizing the posterior probability of the users' operational statuses. For the practical applicability, the obtained scheduling solution can be further verified by a recurrent neural network reported in literature. To indicate the effectiveness of the proposed data-driven scheduling method, the real gas flow data coming from a steel plant in China are employed, and the experimental results indicate that the proposed method can provide real-time and scientific gas scheduling solution for the energy system of steel industry. View full abstract»

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  • A Bayesian Approach to Automated Optical Inspection for Solder Jet Ball Joint Defects in the Head Gimbal Assembly Process

    Page(s): 1155 - 1162
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3277 KB) |  | HTML iconHTML  

    Automation or selective automation is adopted as a solution to most productivity problems in the hard disk drive (HDD) industry as the industry continues to grow at a 40% compounded annual growth rate. An automated production line for manufacturing the head gimbal assembly (HGA) has been developed as part of the automation solution. In the automated HGA production line, a solder jet ball (SJB) soldering station connects the suspension circuit to the slider body. We propose a Bayesian approach to automated optical inspection (AOI) of the SJB joint in the HGA process, implementing Tree Augmented Naïve Bayes Network (TAN-BN) plus check classifier in-situ using GeNIe/SMILE within the inspection software. The system is further enhanced with a result checker, achieving an overall accuracy of 91.52% with 660 production parts in a blind test. View full abstract»

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  • Energy-Aware Scheduling of Distributed Systems

    Page(s): 1163 - 1175
    Multimedia
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1353 KB) |  | HTML iconHTML  

    Scheduling of tasks on a multi-machine system to reduce the makespan, while satisfying the precedence constraints between the tasks, is known to be an NP-hard problem. We propose a new formulation and show that energy-aware scheduling is a generalization of the minimum makespan scheduling problem. Taking the system graph and program graph as inputs, we propose three different algorithms for energy-aware scheduling, each of them having its own strengths and limitations. The first is a genetic algorithm (Plain GA) that searches for an energy reducing schedule. The second (CA+GA) uses cellular automata (CA) to generate low energy schedules, while using a genetic algorithm (GA) to find good rules for the CA. The third (EAH) is a heuristic which gives preference to high-efficiency machines in allocation. We have tested our algorithms on well-known program graphs and compared our results with other state-of-the-art scheduling algorithms, which confirms the efficacy of our approach. Our work also gives insight into the time-energy trade-offs in scheduling. View full abstract»

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  • A Novel Iterative \theta -Adaptive Dynamic Programming for Discrete-Time Nonlinear Systems

    Page(s): 1176 - 1190
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (4364 KB) |  | HTML iconHTML  

    This paper is concerned with a new iterative θ-adaptive dynamic programming (ADP) technique to solve optimal control problems of infinite horizon discrete-time nonlinear systems. The idea is to use an iterative ADP algorithm to obtain the iterative control law which optimizes the iterative performance index function. In the present iterative θ-ADP algorithm, the condition of initial admissible control in policy iteration algorithm is avoided. It is proved that all the iterative controls obtained in the iterative θ-ADP algorithm can stabilize the nonlinear system which means that the iterative θ-ADP algorithm is feasible for implementations both online and offline. Convergence analysis of the performance index function is presented to guarantee that the iterative performance index function will converge to the optimum monotonically. Neural networks are used to approximate the performance index function and compute the optimal control policy, respectively, for facilitating the implementation of the iterative θ-ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the established method. View full abstract»

    Open Access
  • Constrained Optimal Test Signal Design for Improved Prediction Error

    Page(s): 1191 - 1202
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2059 KB) |  | HTML iconHTML  

    This paper presents a new efficient methodology for the optimal design of discrete test signals in black-box dynamic nonlinear system identification. The approach is based on a new criterion which weights the parameter covariances with the magnitudes of output sensitivities both to reduce the parameter estimation error and also allow the optimization of the output fitness. Optimization using this criterion has a low computational cost and in the case that the regressors are well chosen the performance index approximates that of the I-optimality criterion and results in high output fitness. The new method allows for the efficient use of numerical constrained global optimization algorithms to be applied to magnitude and rate constraints on system inputs and outputs, which are essential considerations in experimental applications. The approach should thus be employable as a component of an iterative bootstrapping procedure for experimental system identification subject to safe operating limits. The approach is applied to the black-box nonlinear multiple-input multiple-output identification of an automotive engine-fueling model as a benchmark. The results are compared with those obtained by other computationally efficient methods of both nonoptimal and optimal type. Statistical validation of the results shows that the design method using the new criterion gives test signals satisfying the required operational constraints which have superior outcomes in output prediction fit. View full abstract»

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  • Simulation and Experimental Verification of Weighted Velocity and Acceleration Minimization for Robotic Redundancy Resolution

    Page(s): 1203 - 1217
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2928 KB) |  | HTML iconHTML  

    This paper proposes and investigates a weighted velocity and acceleration minimization scheme to prevent the occurrence of high joint velocity and joint acceleration caused by the minimum acceleration norm (MAN) scheme in redundant robot manipulators. The proposed scheme considers minimum kinetic energy (MKE) and MAN criterions via two weighting factors, thus guaranteeing the final joint velocity of motion to be near zero, which is acceptable for engineering applications. Joint physical constraints (i.e., joint angle limits, joint velocity limits, and joint acceleration limits) are incorporated in the formulation of the proposed scheme. The proposed scheme is reformulated as a quadratic program and then calculated by using a numerical algorithm based on linear variational inequality. Computer simulation results of a PUMA560 robot manipulator verify the efficacy and flexibility of the proposed scheme for redundancy resolution in robot manipulators. Experimental verifications conducted on a six-link planar robot manipulator demonstrate the effectiveness and physical realizability of the proposed scheme. View full abstract»

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  • Command and Control of Discrete-Event Systems: Towards Online Hierarchical Control Based on Feasible System Decomposition

    Page(s): 1218 - 1228
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2940 KB) |  | HTML iconHTML  

    A new operational design for hierarchical control of discrete-event systems is proposed. The design brings the structure of command and control from concept to realization for online control operation. For a command reference input, a new concept for output control feasibility of a discrete-event system modeled by a Moore automaton is characterized; and a system decomposition of a suitably structured Moore automaton into a controllable subsystem and an uncontrollable subsystem is formulated. Based on these results, the new command and control design for controller operation is realized, examined, and discussed. View full abstract»

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Aims & Scope

T-ASE will publish foundational research on Automation: scientific methods and technologies that improve efficiency, productivity, quality, and reliability, specifically for methods, machines, and systems operating in structured environments over long periods, and the explicit structuring of environments.

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Meet Our Editors

Editor-in-Chief
Ken Goldberg
University of California, Berkeley