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, 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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We consider the event-triggered state estimation of a finite-state hidden Markov model with a general stochastic event-triggering condition. Utilizing the change of probability measure approach and the event-triggered...
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ISBN:
(纸本)9781479978878
We consider the event-triggered state estimation of a finite-state hidden Markov model with a general stochastic event-triggering condition. Utilizing the change of probability measure approach and the event-triggered measurement information available to the estimator, analytical expressions for the conditional probability distributions of the states are obtained, based on which the minimum mean square error event-based state estimates are further calculated. We show that the results also cover the case of packet dropout, under a special parameterization of the event-triggering conditions. With the results on state estimation, a closed-form expression of the average sensor-to-estimator communication rate is also presented. The effectiveness of the proposed results is illustrated by a numerical example and comparative simulations.
To design a control strategy for iLeg, an exoskeleton robot developed for lower limb rehabilitation aiming at investigating the feasibility of integrating functional electrical stimulation (FES) with robot-based rehab...
To design a control strategy for iLeg, an exoskeleton robot developed for lower limb rehabilitation aiming at investigating the feasibility of integrating functional electrical stimulation (FES) with robot-based rehabilitation training, an FES-assisted training strategy combined with impedance control, has been proposed in this paper. Through impedance control, an active compliance of the robot is established, and the patient’s voluntary effort to accomplish the training task is inspired. During the training process, the patient’s related muscles are applied with FES which provides an extra assistance to the patient. The intensity of the FES is properly chosen in order to induce a desired active torque which is proportional to the voluntary effort extracted from the electromyography signals of the related muscles using back propagation neural networks. This kind of enhancement serves as a positive feedback which reminds the patient of the correct attempt to fulfill the desired motion. FES control is conducted by a combination of neural network-based feedforward controller and a PD feedback controller. Simulation conducted using Matlab and the experiment with a spinal cord injury subject and a healthy subject have shown satisfactory results which verify the feasibility of this control strategy.
An introduction is presented in which the editor discusses the theme of the periodical based on learning in nonstationary and evolving environments along with changes in the editorial board and also offers brief profi...
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An introduction is presented in which the editor discusses the theme of the periodical based on learning in nonstationary and evolving environments along with changes in the editorial board and also offers brief profiles of associate editors including Sander Bohte, Preben Kidmose, and Peter Tino.
In this paper, a model-free and effective approach is proposed to solve infinite horizon optimal control problem for affine nonlinear systems based on adaptive dynamic programming technique. The developed approach, re...
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In this paper,the nonlinear H guidance law design is proposed for the near space interceptor(NSI)based on Galerkin simultaneous policy update algorithm(GSPUA).Initially,the nonlinear H guidance law design problem for ...
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ISBN:
(纸本)9789881563842
In this paper,the nonlinear H guidance law design is proposed for the near space interceptor(NSI)based on Galerkin simultaneous policy update algorithm(GSPUA).Initially,the nonlinear H guidance law design problem for the NSI is converted to solve the Hamilton-Jacobi-Isaacs(HJI)equation,which is notoriously difficult to solve both numerically and *** the two-player zero-sum differential game theory,where the control player desires to minimize the cost function and the disturbance player desires to maximize ***,a simultaneous policy update algorithm(SPUA)is developed to solve the HJI equation,in which the control and disturbance policies are updated ***,the SPUA is implemented based on the Galerkin’s method,where the Lyapunov function equations(LFEs)in the iterative process of SPUA are ***,simulation results on the NSI demonstrate that the proposed nonlinear H guidance law is effective.
In this paper, surface electromyography (sEMG) from muscles of the lower limb is acquired and processed to estimate the singlejoint voluntary motion intention, based on which, two single-joint active training strategi...
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