In practical processes,lots of phenomena are unable to be accurately described by conventional integer order models,while fractional order models can describe the characteristics more *** this paper,a new fractional o...
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ISBN:
(纸本)9781509009107
In practical processes,lots of phenomena are unable to be accurately described by conventional integer order models,while fractional order models can describe the characteristics more *** this paper,a new fractional order predictive functional control(FPFC) method is presented for the fractional order *** Oustaloup approximation is employed to derive the approximate model of the original ***,the Griinwald-Letnikov definition and fractional operator are used to extend the integer order predictive functional control to the *** with traditional predictive functional control,simulation results reveal that the fractional order controller can achieve improved control performance.
A discrete artificial bee colony algorithm is proposed for solving the blocking flow shop scheduling problem with total flow time criterion. Firstly, the solution in the algorithm is represented as job permutation. Se...
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A discrete artificial bee colony algorithm is proposed for solving the blocking flow shop scheduling problem with total flow time criterion. Firstly, the solution in the algorithm is represented as job permutation. Secondly, an initialization scheme based on a variant of the NEH (Nawaz-Enscore-Ham) heuristic and a local search is designed to construct the initial population with both quality and diversity. Thirdly, based on the idea of iterated greedy algorithm, some newly designed schemes for employed bee, onlooker bee and scout bee are presented. The performance of the proposed algorithm is tested on the well-known Taillard benchmark set, and the computational results demonstrate the effectiveness of the discrete artificial bee colony algorithm. In addition, the best known solutions of the benchmark set are provided for the blocking flow shop scheduling problem with total flow time criterion.
A trajectory-tracking problem for a vision-based quadrotor control system is investigated in this paper. A super twisting sliding mode (STSM) controller is proposed for finite-time trajectory tracking control. With th...
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Synthesis and optimization of utility system usually involve grassroots design, retrofitting and operation optimization, which should be considered in modeling process. This paper presents a general method for synthes...
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Synthesis and optimization of utility system usually involve grassroots design, retrofitting and operation optimization, which should be considered in modeling process. This paper presents a general method for synthesis and optimization of a utility system. In this method, superstructure based mathematical model is established, in which different modeling methods are chosen based on the application. A binary code based parameter adaptive differential evolution algorithm is used to obtain the optimal con figuration and operation conditions of the system. The evolution algorithm and models are interactively used in the calculation, which ensures the feasibility of con figuration and improves computational ef ficiency. The capability and effectiveness of the proposed approach are demonstrated by three typical case studies.
Deep learning has revolutionized the field of artificial *** on the statistical correlations uncovered by deep learning-based methods,computer vision tasks,such as autonomous driving and robotics,are growing *** being...
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Deep learning has revolutionized the field of artificial *** on the statistical correlations uncovered by deep learning-based methods,computer vision tasks,such as autonomous driving and robotics,are growing *** being the basis of deep learning,such correlation strongly depends on the distribution of the original data and is susceptible to uncontrolled *** the guidance of prior knowledge,statistical correlations alone cannot correctly reflect the essential causal relations and may even introduce spurious *** a result,researchers are now trying to enhance deep learningbased methods with causal *** theory can model the intrinsic causal structure unaffected by data bias and effectively avoids spurious *** paper aims to comprehensively review the existing causal methods in typical vision and visionlanguage tasks such as semantic segmentation,object detection,and image *** advantages of causality and the approaches for building causal paradigms will be *** roadmaps are also proposed,including facilitating the development of causal theory and its application in other complex scenarios and systems.
This study focuses on the learning-based asynchronous sliding mode control for switching systems, operating under a general switching rule and partially unknown probability information. A novel switching rule is const...
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This research article considers the design of static output-feedback sliding mode control for Markovian jump systems,in which the attacker may inject false information into the communication channel between the contro...
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ISBN:
(纸本)9781665431293
This research article considers the design of static output-feedback sliding mode control for Markovian jump systems,in which the attacker may inject false information into the communication channel between the controller and the *** key issue is how to design the feasible sliding mode control law to overcome the effects of unknown and time-varying *** this end,an on-line estimation scheme is introduced to deal with the unknown network attack *** then,a linear sliding surface is constructed based on the measured output information and an outputfeedback sliding mode controller is correspondingly *** is shown that the reachability of the specified sliding surface can be achieved and the asymptotic stability of the closed-loop system can be ensured under the derived sufficient ***,simulation examples are provided to verify the developed static output-feedback sliding mode control strategy.
For constrained piecewise linear （PWL） systems, the possible existing model uncertainty will bring the difficulties to the design approaches of model predictive control （MPC） based on mixed integer programming （...
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For constrained piecewise linear （PWL） systems, the possible existing model uncertainty will bring the difficulties to the design approaches of model predictive control （MPC） based on mixed integer programming （MIP）. This paper combines the robust method and hybrid method to design the MPC for PWL systems with structured uncertainty. For the proposed approach, as the system model is known at current time, a free control move is optimized to be the current control input. Meanwhile, the MPC controller uses a sequence of feedback control laws as the future control actions, where each feedback control law in the sequence corresponds to each partitions and the arbitrary switching technique is adopted to tackle all the possible switching. Furthermore, to reduce the online computational burden of MPC, the segmented design procedure is suggested by utilizing the characteristics of the proposed approach. Then, an offline design algorithm is proposed, and the reserved degree of freedom can be online used to optimize the control input with lower computational burden.
In process monitoring, some specific performance indexes need to be paid attention to. Therefore, the performance-triggered process monitoring scheme is proposed. Different from the traditional process monitoring meth...
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Multiunit non-Gaussian dynamic processes are more popular, and monitoring of such processes becomes important. A dynamic canonical correlation analysis (DCCA)-based distributed monitoring approach for multiunit non-Ga...
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