The impact of formal care (co-paid by long term care (LTC) insurance) on informal care is critical to the improvement and promotion of public policy. We conducted an interview-based survey to examine how the use of formal care impacts the use of informal care in Shanghai, which was one of China’s first long-term insurance pilots in 2016. In addition to total informal care time, the following four types of informal care were considered: (1) household activities of daily living (HDL) tasks, (2) activities of daily living (ADL) tasks, (3) instrumental activities of daily living (IADL) tasks, and (4) supervision tasks. Of the 407 families, an average of 12.36 h (SD = 6.70) of informal care was crowded out each week. Among them, ADL tasks, HDL tasks, and supervision tasks were reduced an average of 4.60 (SD = 3.59), 5.50 (SD = 3.38), and 2.10 h (SD = 3.06) per week, respectively. Each additional hour of formal care reduced 0.473 h of informal care. Care recipients’ gender and health status were also determined to be associated with crowding out hours of informal care. These findings can be utilized as empirical evidence for decision-makers to consider the scope of funding for formal care, and this study provides comparable results to developing countries and regions.
A Nonlinear Proportional-Derivative (NPD) controller with gravity compensation is proposed and applied to robot manipulators in this paper. The proportional and derivative gains are changed by the nonlinear function of errors in the NPD controller. The closed-loop system, composed of nonlinear robot dynamics and NPD controllers, is globally asymptotically stable in position control of robot manipulators. The comparison of the simulation experiments in the position control (the step response) of a robot manipulator with two degrees of freedom is also presented to illustrate that the NPD controller is superior to the conventional PD controller in a position control system. The experimental results show that the NPD controller can obtain a faster response velocity and higher position accuracy than the conventional PD controller in the position control of robot manipulators because the proportional and derivative gains of the NPD controller can be changed by the nonlinear function of errors. The NPD controller provides a novel approach for robot control systems.
Multispeed transmissions are helpful for improvement of the economy and drivability of electric vehicles (EVs). In this paper, we propose a two-speed transmission based on dual planetary gear mechanism, in which shifts are realized by torque transfer between two brakes located on ring gears. To synthesize the dynamic and economic performances of the vehicle, a multiobjective optimization problem is constructed for gear ratio optimization and Pareto-optimal solutions of gear ratio combinations are obtained by Nondominated sorting genetic algorithm-II (NSGA-II). In particular, the minimum electric energy consumption of the EV is calculated with a fast Dynamic Programming (DP) in each iteration. Following this, a constant-output-torque control (COTC) scheme is adopted for the torque phase and inertia phase of gearshift process to ensure constant output torque on the wheel. To enhance transient responses, the feedforward–feedback controller structure is applied and a disturbance observer is integrated to improve robustness. Simulation results demonstrate that the two-speed transmission has much better performance in terms of acceleration time and energy economy compared to the fixed-ratio transmission, and the proposed gearshift control method is able to achieve fast and smooth gear shift robustly while maintaining constant output torque.
This paper presents a gear shift method for the dual clutch transmission (DCT) with integrated electric motor in pure electric drive mode. In contrast to clutch-to-clutch shift in conventional DCT, a good gear shifting process relies on the coordinated control of the motor and synchronizer in electric drive mode of the hybrid DCT. To shorten the torque interruption time and reduce the wear of the synchronizer during engagement, the key point is to adjust the oncoming gear speed to the output shaft speed rapidly. This study provides a speed regulation control framework based on model predictive control (MPC) and disturbance observer (DO), where the MPC controller is designed to achieve a good tracking performance and the DO is to eliminate effects from exogenous disturbances. Simulation and experimental results demonstrate that the proposed approach can attain a rapid and robust gear shifting performance.
This paper proposes a method and an algorithm to implement interpretable fuzzy reinforcement learning (IFRL). It provides alternative solutions to common problems in RL, like function approximation and continuous action space. The learning process resembles that of human beings by clustering the encountered states, developing experiences for each of the typical cases, and making decisions fuzzily. The learned policy can be expressed as human-intelligible IF-THEN rules, which facilitates further investigation and improvement.It adopts the actor-critic architecture whereas being different from mainstream policy gradient methods. The value function is approximated through the fuzzy system AnYa. The state-action space is discretized into a static grid with nodes. Each node is treated as one prototype and corresponds to one fuzzy rule, with the value of the node being the consequent. Values of consequents are updated using the Sarsa(λ) algorithm. Probability distribution of optimal actions regarding different states is estimated through Empirical Data Analytics (EDA), Autonomous Learning Multi-Model Systems (ALMMo), and Empirical Fuzzy Sets ( FS). The fuzzy kernel of IFRL avoids the lack of interpretability in other methods based on neural networks. Simulation results with
Motor speed synchronization is important in gear shifting of emerging clutchless automated manual transmissions (AMT) for electric vehicles and other kinds of parallel shaft-based powertrains for hybrid electric vehicles. This paper proposes a speed synchronization controller design for a kind of system integrating a traction motor and a dual clutch transmission (DCT), using optimal control and disturbances compensation. Based on the relativity between magnitudes of different system parameters, the optimal control law is simplified into the proportional (P) one to ease design and analysis. Relationship between the feedback gain and the duration of speed synchronization process is derived in an explicit way to facilitate model-based determination of controller parameters. To alleviate overshoot while maintaining predesigned performances, the explicit nominal speed trajectory rather than the fixed setpoint speed is chosen as the reference signal. To improve robustness of the controller, a time-domain disturbance observer (DO) is added to cancel effects from parameter drift, unmodeled dynamics, and other exogenous disturbances. As a result, the proposed controller possesses merits of few controller parameters to be determined, good transient response, and robustness. These features make it suitable for practical engineering use. Simulation and experiment results verify its effectiveness in attaining both a fast and small-overshoot speed synchronizing process.
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