In this paper, an adaptive full-order terminal sliding mode (FOTSM) control method is designed for fault-tolerant control of second-order non-linear multi-agent system (MAS) under actuator faults. Firstly, for the algebraic loop problem of the control system, an adaptive switching gain is added to the sliding mode control law, which effectively avoids the interference of the algebraic loop. Secondly, an integral type control law is designed to completely eliminates the chattering in the traditional distributed sliding mode control. Finally, the simulation is performed in an MAS consisting of one leader and six followers, and the simulation results verify the effectiveness of the method.

In this paper a novel permanent magnet synchronous motor speed controller based on high order terminal sliding mode is proposed, which could enhance system's robustness against disturbance and improve the response speed. Based on vector control theory, a non-singular fast terminal sliding mode control strategy is designed to guarantee the error of motor speed, d- and q- current to converge to zero in finite time. Chattering is solved by applying high order sliding mode control, thus the system stability is ensured. Simulation results show that the proposed control strategy possesses better dynamic response and has stronger robustness to load variation compared with PI.

in photovoltaic medium voltage three-phase high-power power generation, the multi-level cascaded 3-phase-bridge (C3PB) converter is used to replace the traditional single-phase cascaded H-bridge (CHB) converter in this paper. The virtual synchronous generator is proposed to control the cascaded three-phase bridge converter to solve the problem of frequency fluctuation in isolated island operation of microgrid. In addition, in order to further improve the waveform quality, the carrier phase-shifted PWM (CPS-PWM) is used as the switching strategy of the converter. VSG based CPS-PWM realizes the small fluctuation of system frequency, the smoothness and stability of voltage and current waveforms when large load switching or power changes. Finally, the feasibility and practicability of the method are verified by simulations.

A full-order terminal sliding mode (FTSM) control strategy is proposed in this paper for the virtual synchronous generator (VSG) based inverter. VSG is designed for micro-grid to provide stable power to the grid or maintain stable operation in island state. The variable and nonlinear characteristics of micro-grid demand the voltage control scheme must satisfy with VSG power loop, and the control strategy should have strong robustness to resistant the external disturbance and parameters perturbance. FTSM can enhance the robustness and tracking speed of the system. Besides, the FTSM control strategy could reduce chattering problems caused by high-frequency switching. Simulations by MATLAB/Simulink show that the VSG algorithm combined with the FTSM control strategy can significantly enhance the output efficiency of the inverter, improve the output of the inverter, and deal with the load fluctuation.

To emulate the interactivity of in-person math instruction, we developed MathBot, a rule-based chatbot that explains math concepts, provides practice questions, and offers tailored feedback. We evaluated MathBot through three Amazon Mechanical Turk studies in which participants learned about arithmetic sequences. In the first study, we found that more than 40% of our participants indicated a preference for learning with MathBot over videos and written tutorials from Khan Academy. The second study measured learning gains, and found that MathBot produced comparable gains to Khan Academy videos and tutorials. We solicited feedback from users in those two studies to emulate a real-world development cycle, with some users finding the lesson too slow and others finding it too fast. We addressed these concerns in the third and main study by integrating a contextual bandit algorithm into MathBot to personalize the pace of the conversation, allowing the bandit to either insert extra practice problems or skip explanations. We randomized participants between two conditions in which actions were chosen uniformly at random (i.e., a randomized A/B experiment) or by the contextual bandit. We found that the bandit learned a similarly effective pedagogical policy to that learned by the randomized A/B experiment while incurring a lower cost of experimentation. Our findings suggest that personalized conversational agents are promising tools to complement existing online resources for math education, and that data-driven approaches such as contextual bandits are valuable tools for learning effective personalization.

Public and private institutions must often allocate scarce resources under uncertainty. Banks, for example, extend credit to loan applicants based in part on their estimated likelihood of repaying a loan. But when the quality of information differs across candidates (e.g., if some applicants lack traditional credit histories), common lending strategies can lead to disparities across groups. Here we consider a setting in which decision makers---before allocating resources---can choose to spend some of their limited budget further screening select individuals. We present a computationally efficient algorithm for deciding whom to screen that maximizes a standard measure of social welfare. Intuitively, decision makers should screen candidates on the margin, for whom the additional information could plausibly alter the allocation. We formalize this idea by showing the problem can be reduced to solving a series of linear programs. Both on synthetic and real-world datasets, this strategy improves utility, illustrating the value of targeted information acquisition in such decisions. Further, when there is social value for distributing resources to groups for whom we have a priori poor information---like those without credit scores---our approach can substantially improve the allocation of limited assets.