Reliable Memory Feedback Design for a Class of Nonlinear Fuzzy Systems with Time-varying Delay

This paper is concerned with the robust reliable memory controller design for a class of fuzzy uncertain systems with timevarying delay. The system under consideration is more general than those in other existent works. The controller, which is dependent on the magnitudes and derivative of the delay, is proposed in terms of linear matrix inequality （LMI）. The closed-loop system is asymptotically stable for all admissible uncertainties as well as actuator faults. A numerical example is presented for illustration.

Controlling Entropic Uncertainty in the Presence of Quantum Memory by Non-Markovian Effects and Atom-Cavity Couplings

Based on the time-convolutionless master-equation approach, the entropic uncertainty in the presence of quantum memory is investigated for a two-atom system in two dissipative cavities. We find that the entropic uncertainty can be controlled by the non-Markovian effect and the atom-cavity coupling. The results show that increasing the atom-cavity coupling can enlarge the oscillating frequencies of the entropic uncertainty and can decrease the minimal value of the entropic uncertainty. Enhancing the non-Markovian effect can reduce the minimal value of the entropic uncertainty. In particular, if the atom-cavity coupling or the non-Markovian effect is very strong, the entropic uncertainty will be very dose to zero at certain time points, thus Bob can minimize his uncertainty about Alice＇s measurement outcomes,

Non-fragile memory feedback control for uncertain time-varying delay switched fuzzy systems with unknown nonlinear disturbance

Currently,the feedback control rate of most nonlinear systems is realised by the memoryless state feedback controller which cannot affect the impact of time delay on the systems,and the general processing method of the Lyapunov–Krasovskii functional for the time-varying delay switched fuzzy systems(SFS)is more conservative.Therefore,this paper addresses the problem of nonfragile robust and memory state feedback control for switched fuzzy systems with unknown nonlinear disturbance.Non-fragile memory state feedback robust controller which has two controller gains different from each other,and switching law are designed to keep the proposed systems asymptotically stable for all admissible parameter uncertainties.Delay-dependent less conservative sufficient conditions are obtained through using the Lyapunov–Krasovskii functional method and free-weighting matrices depending on Leibniz–Newton,guaranteeing that the SFS can be asymptotically stable.A numerical example is given to illustrate the proposed controller performs better than the classic memoryless state feedback controller.

On the Current Error Based Sampled-data Iterative Learning Control with Reduced Memory Capacity

The design of iterative learning controller(ILC) requires to store the system input, output or control parameters of previous trials for generating the input of the current trial. In order to apply the iterative learning controller for a real application and reduce the memory size for implementation, a current error based sampled-data proportional-derivative(PD) type iterative learning controller is proposed for control systems with initial resetting error, input disturbance and output measurement noise in this paper.The proposed iterative learning controller is simple and effective. The first contribution in this paper is to prove the learning error convergence via a rigorous technical analysis. It is shown that the learning error will converge to a residual set if a forgetting factor is introduced in the controller. All the theoretical results are also shown by computer simulations. The second main contribution is to realize the iterative learning controller by a digital circuit using a field programmable gate array(FPGA) chip applied to repetitive position tracking control of direct current(DC) motors. The feasibility and effectiveness of the proposed current error based sampleddata iterative learning controller are demonstrated by the experiment results. Finally, the relationship between learning performance and design parameters are also discussed extensively.

Velocity forecasts using a combined deep learning model in hybrid electric vehicles with V2V and V2I communication

Vehicle velocity forecast is an important clue in improving the performance of energy management in hybrid electric vehicles(HEV). This paper presents a new combined model for predicting vehicle’s velocity time series. The main features of the model are to combine the feature extraction capability of deep restricted Boltzmann machines(DBM) and sequence pattern predicting capability of bidirectional long short-term memory(BLSTM). Hence, the model is named as DBMBLSTM. In addition, the DRMBLSTM model utilizes the vehicle driving information and roadside infrastructure information provided respectively through vehicle-to-vehicle(V2V) and vehicle-to-infrastructure(V2I) communication channels to predict vehicle velocity at various length of prediction horizon. Furthermore, the predictions results of this study are compared with the state of the art of vehicle velocity forecasts. The root mean square error(RMSE) is used as an evaluation criteria of predictions accuracy. Finally,these compared prediction model are applied in model predictive control(MPC) energy management strategy for the verifications of fuel economy improvement of a HEV. Simulation results confirm that the proposed combined deep learning model performs better than other five prediction methods. Therefore, it is a means of arriving at a reliable forecast model for HEV.