An Adaptive Strategy via Reinforcement Learning for the Prisoner＇s Dilemma Game

The iterated prisoner's dilemma(IPD) is an ideal model for analyzing interactions between agents in complex networks. It has attracted wide interest in the development of novel strategies since the success of tit-for-tat in Axelrod's tournament. This paper studies a new adaptive strategy of IPD in different complex networks, where agents can learn and adapt their strategies through reinforcement learning method. A temporal difference learning method is applied for designing the adaptive strategy to optimize the decision making process of the agents. Previous studies indicated that mutual cooperation is hard to emerge in the IPD. Therefore, three examples which based on square lattice network and scale-free network are provided to show two features of the adaptive strategy. First, the mutual cooperation can be achieved by the group with adaptive agents under scale-free network, and once evolution has converged mutual cooperation, it is unlikely to shift. Secondly, the adaptive strategy can earn a better payoff compared with other strategies in the square network. The analytical properties are discussed for verifying evolutionary stability of the adaptive strategy.

Second-order terminal sliding mode control for hypersonic vehicle in cruising flight with sliding mode disturbance observer

This paper focuses on the design of nonlinear robust controller and disturbance observer for the longitudinal dynamics of a hypersonic vehicle （HSV） in the presence of parameter uncertainties and external disturbances. First, by combining terminal sliding mode control （TSMC） and second-order sliding mode control （SOSMC） approach, the second- order terminal sliding control （2TSMC） is proposed for the velocity and altitude tracking control of the HSV. The 2TSMC possesses the merits of both TSMC and SOSMC, which can provide fast convergence, continuous control law and high- tracking precision. Then, in order to increase the robustness of the control system and improve the control performance, the sliding mode disturbance observer （SMDO） is presented. The closed-loop stability is analyzed using the Lyapunov technique. Finally, simulation results illustrate the effectiveness of the proposed method, as well as the improved overall performance over the conventional sliding mode control （SMC）.

Leptin signaling and leptin resistance

Leptin is secreted into the bloodstream by adipocytes and is required for the maintenance of energy homeostasis and body weight.Leptin deficiency or genetic defects in the components of the leptin signaling pathways cause obesity.Leptin controls energy balance and body weight mainly through leptin receptor b(LEPRb)-expressing neurons in the brain,particularly in the hypothalamus.These LEPRb-expressing neurons function as the first-order neurons that project to the second-order neurons located within and outside the hypothalamus,forming a neural network that controls the energy homeostasis and body weight.Multiple factors,including inflammation and endoplasmic reticulum(ER)stress,contribute to leptin resistance.Leptin resistance is the key risk factor for obesity.This review is focused on recent advance about leptin action,leptin signaling,and leptin resistance.

On-line RNN compensated second order nonsingular terminal sliding mode control for hypersonic vehicle

Purpose–The purpose of this paper is to design a robust control scheme to achieve robust tracking of velocity and altitude commands for a general hypersonic vehicle(HSV)in the presence of parameter variations and external disturbances.Design/methodology/approach–The robust control scheme is composed of nonsingular terminal sliding mode control(NTSMC),super twisting control algorithm(STC)and recurrent neural network(RNN).First,by combing a novel NTSMC and STC algorithm,a second order NTSMC approach for HSV is proposed to provide fast,continuous and high precision tracking control.Second to relax the requirements for the bounds of the lumped uncertainties in control design,a RNN disturbance observer is presented to increase the robustness of the control system.The weights of RNN are updated by adaptive laws based on Lyapunov theorem,thus the closed-loop stability can be guaranteed.Findings–Simulation results demonstrate that the proposed method is effective,leading to promising performance.Originality/value–The main contributions of this work are:first,both parameter variations and external disturbances are considered in control design for the longitudinal dynamic model of HSV;and second,the proposed controller can remove chattering and achieve more favorable tracking performances than conventional sliding mode control.