Safe moving is a basic ability for a mobile robot, and it is beneficial for the robot to avoid the collisions with the environment if it knows the boundaries between the obstacles and free space. In this paper, a cont...
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With the increasing resolution and availability of digital cameras, text detection in natural scene images receives a growing attention. When taking pictures using a mobile device, people generally only concerned with...
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A new type of Biomimetic Underwater Vehicle (RobCutt-I) inspired by cuttlefish was designed and fabricated in this paper. The RobCutt-I has a good maneuverability and can perform multiple motion modes especially can d...
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This paper proposes to obtain high-level, domain-robust representations for cross-view face recognition. Specially, we introduce Convolutional Deep Belief Networks (CDBN) as the feature learning model, and an CDBN bas...
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A novel adaptive cruise control (ACC) system is proposed in this paper. A hierarchical control framework is adopted for the adaptive cruise control problem. For the upper level, a supervised actor-critic (SAC) reinfor...
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A novel adaptive cruise control (ACC) system is proposed in this paper. A hierarchical control framework is adopted for the adaptive cruise control problem. For the upper level, a supervised actor-critic (SAC) reinforcement learning approach is presented to obtain the desired acceleration controller. In the lower level, throttle and brake controllers calculate the required throttle and/or brake signals based on the desired longitudinal acceleration. Feed-forward neural networks are used to implement the actor and critic components of the SAC learning algorithm. An online learning mechanism is introduced to implement the SAC training process. dSPACE simulator is used to verify the effectiveness of the ACC system. Typical emergency braking scenario is simulated to test the adaptability of the ACC system. Road condition change (e.g. wintry or wet conditions) simulation is first investigated to evaluate the robustness of the ACC system. Performance of the proposed ACC system is proved to be very practical.
Kaiman Alter is an efficient way to estimate the parameters of the value function in reinforcement learning. In order to solve Markov Decision Process (MDP) problems in both continuous state and action space, a new on...
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In this paper, we develop data-based methods to analyze the characteristics of linear discrete-time systems, which have unknown parameter matrices. These characteristics include output controllability, asymptotic stab...
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In this paper, a new generalized value iteration algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. The idea is to use iterative adaptive dynamic programming...
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This paper is devoted to the exploration of three-dimensional (3-D) maneuvers using a free-swimming fishlike robot. For the sake of a better maneuverability, an Esox lucius robotic fish consisting of a yawing head, tw...
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In this paper, a stochastic model predictive control (SMPC) approach is proposed to schedule a hybrid energy system (HES) which composes a battery energy storage system (BESS) and a wind farm. The SMPC is used to cont...
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ISBN:
(纸本)9781479975617
In this paper, a stochastic model predictive control (SMPC) approach is proposed to schedule a hybrid energy system (HES) which composes a battery energy storage system (BESS) and a wind farm. The SMPC is used to control the charge and discharge of the BESS to minimize the operation costs and maximum the selling power revenue for the HES owner with considering wind production and electricity price forecast uncertainties. Case study is employed to assess the performance of the SMPC approach and simulation results show that this approach proposed in this paper is effective and feasible.
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