This paper studies the loading coordinations for large-population autonomous individual (plug-in) electric vehicles (EVs) and a few controllable bulk loads, e.g. EV fleets, pumped storage hydro units, and so on. Due t...
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ISBN:
(纸本)9781612848006
This paper studies the loading coordinations for large-population autonomous individual (plug-in) electric vehicles (EVs) and a few controllable bulk loads, e.g. EV fleets, pumped storage hydro units, and so on. Due to the computational infeasibility of the centralized coordination methods to the underlying large-population systems, in this paper we develop a novel game-based decentralized coordination strategy. Following the proposed decentralized strategy update mechanism and under some mild conditions, the system may quickly converge to a nearly valley-fill Nash equilibrium. The results are illustrated with numerical examples.
Since the Pneumatic Muscle Actuator (PMA) has the characteristic of strong nonlinear and time lags, it is difficult to establish a precise mathematical mode. Model-Free Adaptive control (MFAC) is an advanced control a...
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ISBN:
(纸本)9781424490103
Since the Pneumatic Muscle Actuator (PMA) has the characteristic of strong nonlinear and time lags, it is difficult to establish a precise mathematical mode. Model-Free Adaptive control (MFAC) is an advanced control algorithm that does not require building an off-line mathematical model. This paper is basing on the feature of the PMA and presents a model-free adaptive control algorithm with the nonlinear feedback. Finally, experimental results show the strong robustness, fast response, and high precision of this control algorithm on the displacement control of the PMA.
The present work considers a scenario that a multi-actuator-sensor network neutralizes poisonous gas and tracks the pollution sources in a bounded area. A novel algorithm is proposed to minimize the system information...
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The present work considers a scenario that a multi-actuator-sensor network neutralizes poisonous gas and tracks the pollution sources in a bounded area. A novel algorithm is proposed to minimize the system information uncertainty while reaching balance on the workload of actuators. The method combines the centroidal Voronoi tessellations (CVT) with a consensus strategy. The CVT of the region insures a local optimal position configuration of the actuators, thus the sensing uncertainty can be minimized. The consensus algorithm utilizes the connection information among actuators, and helps them to reach a common workload. The consensus component will be terminated or suppressed when the workload is averaged. The consensus component may postpone the realization of CVT configuration. But it could be viewed as a perturbation that helps the actuators jump out of the local optimal CVT configuration. As a result, the information uncertainty may be further reduced. Comparison is drawn between the pure CVT algorithm and the method with consensus strategy. Simulations validated the proposed approach.
Based on the comparison of several common methods of electronic compass error compensation, this paper presents a new error compensation method based on Adaptive Differential Evolution-Fourier Neural Networks (ADE-FNN...
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An adaptive sliding mode control scheme for electromechanical actuator has been presented. The adaptive control strategy can estimate the uncertain parameters and adaptively compensate the modeled dynamical uncertaint...
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An adaptive sliding mode control scheme for electromechanical actuator has been presented. The adaptive control strategy can estimate the uncertain parameters and adaptively compensate the modeled dynamical uncertainties, while the sliding mode control method overcomes the unmodelled dynamics. In the adaptive law an equivalent output injection of the sliding mode observer which contains the parameter estimation error is used, and estimates of parameters can approximate the true values without prediction-error that is typically used in compositive adaptive law. Due to the improved estimation of uncertain parameters, the sliding mode law can robustifies the design against model uncertainties with a small swithcing gain. Stability of the system with the proposed approach has been proved and it has also been shown that the systemstates can reach the sliding mode in finite time. Finally, the effectiveness of the proposed control scheme has been exhibited via simulation examples.
Generalized second-price (GSP) is currently the dominant auction mechanism used in the sponsored search advertising market. However, despite its tremendous commercial success and theoretical optimality, its effectiven...
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Generalized second-price (GSP) is currently the dominant auction mechanism used in the sponsored search advertising market. However, despite its tremendous commercial success and theoretical optimality, its effectiveness is jeopardized by the severe click frauds conducted by advertisers and third-party publishers and the vicious bidding strategy used by advertisers to exhaust the budget of rivals. In this paper, we analyze the drawbacks of GSP that tolerate or even encourage such negative behaviors (i.e., click fraud and vicious bidding) and propose a dynamic modification of the original GSP mechanism to address these drawbacks. Our modified auction mechanism incorporates budget into slot allocation and payment determination and relates the quality score of an advertisement to the current bid. Our analysis shows that our mechanism can effectively reduce the effects of click fraud and vicious bidding.
Differential Evolution (DE) is a simple and efficient numerical optimization method. Most DE variants in the literature adopt fixed population size. This paper incorporates into DE the mechanisms of lifetime and extin...
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ISBN:
(纸本)9781612844879
Differential Evolution (DE) is a simple and efficient numerical optimization method. Most DE variants in the literature adopt fixed population size. This paper incorporates into DE the mechanisms of lifetime and extinction which regulate DE's population size in an adaptive manner. The population size is adjusted according to the online progress of fitness improvement. Two schemes of inserting new individuals are proposed to match different mechanisms respectively. The performance of these innovations is examined through the optimization of benchmark problems. The results show that the proposed adaptive population sizing strategy is efficient for improving the convergence and efficiency of the DE.
In order to solve the multi-UAV cooperative path planning problem of low-altitude penetration, the paper proposes an improved Multi-agent Coevolutionary Algorithm (IMACEA), which introduces co-evolution mechanism base...
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作者:
Gong KunDeng FangMa TaoGong Kun is with School of Automation
Beijing Institute of Technology and Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) Beijing China Deng Fang is with School of Automation
Beijing Institute of Technology and Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) Beijing China Ma Tao is with School of Automation
Beijing Institute of Technology and Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing China
In order to improve the precision of the azimuth measured by mobile robot's electronic compass, this paper proposes a new calibration method based on Fourier Neural Network trained by Modified Particle Swarm Optim...
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In order to improve the precision of the azimuth measured by mobile robot's electronic compass, this paper proposes a new calibration method based on Fourier Neural Network trained by Modified Particle Swarm Optimization (MPSO-FNN). This method makes use of Fourier Neural Network (FNN) to establish the error compensation model of electronic compass's azimuth, and introduces Modified Particle Swarm Optimization (MPSO) algorithm to optimize the weights of neural network. Thus the comparatively accurate error model of azimuth is obtained to compensate the output of electronic compass. This method not only has strong nonlinear approximation capability, but also overcomes the neural networks' shortcomings which are too slow convergence speed, oscillation, and easy to fall into local optimum and sensitive to the initial values. Experimental results demonstrate that after calibrated by this method, the range of azimuth error reduces to -0.35°~0.70° from -3.4°~25.2°, and the average value of absolute error is only 0.30°.
A plant-friendly proportional-integral-derivative (PID) controller optimization framework is proposed to make tradeoffs among set-point response,controller output variations and *** objective function is chosen as t...
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A plant-friendly proportional-integral-derivative (PID) controller optimization framework is proposed to make tradeoffs among set-point response,controller output variations and *** objective function is chosen as the weighted sum of the integral of squared time-weighted error and the integral of squared timeweighted derivative of the control variable with respect to set-point response,while the robustness of the system is guaranteed by constraints on gain and phase *** to the complex structure of the constraints,the problem is solved by genetic *** analysis show the proposed method could efficiently reduce the controller output variations while maintaining a short settling *** on the simulation results,iterative tuning rules for the weighting factor in the objective function are obtained,which allows efficient simple proportional-integral(PI) tuning formulae to be derived.
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