Agent-based evacuation modeling approach is gained more and more attention for investigating human cognitive capabilities and social behaviors in building fires. This paper mainly overviews the research about various ...
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It is difficult to select a composite service with the lowest actual executing cost using the existing methods for QoS-aware service composition in cloud computing. By analyzing the dynamic execution process of compos...
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ISBN:
(纸本)9781467313988
It is difficult to select a composite service with the lowest actual executing cost using the existing methods for QoS-aware service composition in cloud computing. By analyzing the dynamic execution process of composite service with state transition matrix, this paper proposes a new QoS aware optimal service composition method. In view of the effect of composite services reliability on the composite service performance, the method regards the cost averaged for one time of successful execution of a composite as its actual executing cost, and then selects the composite services with the aim of minimizing the composite service execution cost. The simulation result shows that the proposed method is superior to other methods in execution cost.
A trajectory planning method in joint space which provides a continuity of position, velocity and acceleration just with simple numerical simulations was already presented by us before. This paper, therefore, presents...
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ISBN:
(纸本)9781467313988
A trajectory planning method in joint space which provides a continuity of position, velocity and acceleration just with simple numerical simulations was already presented by us before. This paper, therefore, presents techniques in detail for implementation of it. To guarantee high performance, a novel DSP-based multiaxis motion controller is developed for executing the trajectory planning method on-line. Hardware design and software design of the motion controller is described. Finally, the proposed trajectory planning method is tested based on the proposed motion controller for an arc welding robot. Experimental results and performances evaluation are also presented in this paper.
Adaptive dynamic programming (ADP) is an effective method for learning while fuzzy controller has been put into use in many applications because of its simplicity and no need of accurate mathematic modeling. The combi...
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ISBN:
(纸本)9781467313988
Adaptive dynamic programming (ADP) is an effective method for learning while fuzzy controller has been put into use in many applications because of its simplicity and no need of accurate mathematic modeling. The combination of ADP and fuzzy control has been studied a lot. Before this paper, we have studied using ADP to learn the fuzzy rules of a Monotonic controller, which shows good performance. In this paper, a hyperbolic fuzzy model is adopted to make an improvement. In this way, both membership function and fuzzy rules are learned. With ADP algorithm, fuzzy controller has the capacity of learning and adapting. Simulations on a single cart-pole plant and a rotational inverted pendulum are implemented to observe the performance, even with uncertainties and disturbances.
Nowadays, most industrial robots have been designed to be mechanically stiff with rigid link. When a robot with heavy payload is running in fast motions, the residual vibrations of the end-effector are primary caused ...
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Nowadays, most industrial robots have been designed to be mechanically stiff with rigid link. When a robot with heavy payload is running in fast motions, the residual vibrations of the end-effector are primary caused by joint flexibility. Therefore, the flexibility of joint cannot be neglected. This paper presents a systematic approach to dynamic modeling and residual vibration mode analysis for an industrial robot with rigid links and flexible joints (RLFJ). First, the flexibility of the joint is modeled as a torsional spring and the dynamic equations for this robot are derived by using Lagrange’s method. Then, to examine residual vibration properties of the system, numerical simulation is carried out. The following important conclusions are drawn from simulation results: (1) the proposed dynamic model can represent joint flexibility of industrial robot and the joint flexibility causes the residual vibration of the end-effector;(2) for a given RLFJ model, the residual vibration is mainly affected by the payload mass, path of the task and joint stiffness.
In this paper, a new stable value iteration adaptive dynamic programming (ADP) algorithm, named "θ-ADP" algorithm, is proposed for solving the optimal control problems of infinite horizon discrete-time nonl...
ISBN:
(纸本)9781467314886
In this paper, a new stable value iteration adaptive dynamic programming (ADP) algorithm, named "θ-ADP" algorithm, is proposed for solving the optimal control problems of infinite horizon discrete-time nonlinear systems. By introducing a parameter θ in the iterative ADP algorithm, it is proved that any of iterative control obtained in the proposed algorithm can stabilize the nonlinear system which overcomes the disadvantage of traditional value iteration algorithms. 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, a simulation example is given to illustrate the performance of the proposed method.
In this paper, we solve the H_∞ robust optimal control problem for discrete-time nonlinear systems with control saturation constraints using the iterative adaptive dynamic programming algorithm. First, a heuristic dy...
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ISBN:
(纸本)9781467314886
In this paper, we solve the H_∞ robust optimal control problem for discrete-time nonlinear systems with control saturation constraints using the iterative adaptive dynamic programming algorithm. First, a heuristic dynamic programming algorithm is derived to solve the Hamilton-Jacobi-Isaacs equation associated with the H_∞ control problem, and a convergence analysis is provided. Then, a dual heuristic dynamic programming algorithm with nonquadratic performance functional is developed to overcome the control saturation constraints. Finally, to facilitate the implementation of the algorithm, four neural networks are used to approximate the unknown nonlinear system, the control policy, the disturbance policy, and the value function.
Modern power system is a typical multi-level complex giant system consisting of physical infrastructures, human operators, and social resources, etc. The conventional analytical methods and simulation systems can'...
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In this paper, we extend our previous work of a three-network adaptive dynamic programming design [1] to be a multiple critic networks design for online learning and control. The key idea of this approach is to develo...
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ISBN:
(纸本)9781467313988
In this paper, we extend our previous work of a three-network adaptive dynamic programming design [1] to be a multiple critic networks design for online learning and control. The key idea of this approach is to develop a hierarchical internal goal representation to facilitate the online learning with detailed and informative internal value signal representations. We present our learning architecture in detail, and also demonstrate its performance on the popular cart-pole balancing benchmark. Simulation results demonstrate the effectiveness of our approach. We also present discussions of further research directions along this topic.
This paper aims to integrate the fuzzy control with adaptive dynamic programming (ADP) scheme, to provide an optimized fuzzy control performance, together with faster convergence of ADP for the help of the fuzzy prior...
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ISBN:
(纸本)9781467314909
This paper aims to integrate the fuzzy control with adaptive dynamic programming (ADP) scheme, to provide an optimized fuzzy control performance, together with faster convergence of ADP for the help of the fuzzy prior knowledge. ADP usually consists of two neural networks, one is the Actor as the controller, the other is the Critic as the performance evaluator. A fuzzy controller applied in many fields can be used instead as the Actor to speed up the learning convergence, because of its simplicity and prior information on fuzzy membership and rules. The parameters of the fuzzy rules are learned by ADP scheme to approach optimal control performance. The feature of fuzzy controller makes the system steady and robust to system states and uncertainties. Simulations on under-actuated systems, a cart-pole plant and a pendubot plant, are implemented. It is verified that the proposed scheme is capable of balancing under-actuated systems and has a wider control zone.
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