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Fuel-cell/ultracapacitor hybrid vehicle (FHV) needs distributing load power appropriately to its fuel cell system and ultracapacitor bank in order to minimize fuel consumption and power fluctuations in the fuel cell system while supplying adequate power to the load, and the state of charge of the ultracapacitor bank maintained at the permissible levels. This paper proposes a self-optimizing energy...
This paper presents the results of numerical study on the optimal control strategy of nonlinear stochastic systems. The systems under investigation are mechanical oscillators and a damping device. The numerical approach to obtain the optimal control strategy involves solving a nonlinear partial differential equation — the Hamilton-Jacobi-Bellman equation. Since civil engineering structural systems...
Battery-powered electric vehicles may capture regenerative energy to extend driving range. However, while larger regenerative braking torque attains more regenerative power, excess braking torque may lock up the wheel and result in safety issues. In effect, maximization of the regenerative torque and in turn the energy recapture is subject to the safety requirements. This paper translates the energy...
Motion controllers capable of incremental learning and optimization can automatically tune their parameters to pursue optimal control. By implementing reinforcement learning and approximate dynamic programming, an adaptive critic motion controller is shown able to achieve this objective. The control policy and the adaptive critic are implemented by sparse radial basis function networks. The policy...
This paper proposes a new approach to achieve an on-line control of trajectory tracking and obstacle avoidance for redundant manipulators without prechecking path-planning in whole trajectory tracking. In the trajectory tracking process, manipulator is required to keep a configuration with maximal avoidance manipulability in real-time. In this paper, we present a new idea: Multi-Preview Control, which...
This paper presents an adaptive critic neuro-fuzzy control design, which enables learning from scratch to achieve the control objective. The learning algorithm is derived from the Dual Heuristic Programming (DHP) method to approximate optimal control. The learning structure contains the action, critic and verification neuro-fuzzy networks each corresponding to the first-order Sugeno fuzzy model. The...
Optimal output-tracking control of unknown nonlinear motion systems with multivariable structure is designed by solving the linear optimal tracker integrated with an adaptive fuzzy compensator. The adaptive fuzzy compensator performs on-line learning to approximate and compensate the unknown nonlinear dynamics of the system so that minimizing a quadratic performance index can obtain the optimal tracker...
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