Trajectory optimization of a reentry vehicle based on artificial emotion memory optimization

The trajectory optimization of an unpowered reentry vehicle via artificial emotion memory optimization(AEMO)is discussed.Firstly,reentry dynamics are established based on multiple constraints and parameterized control variables with finite dimensions are designed.If the constraint is not satisfied,a distance measure and an adaptive penalty function are used to address this scenario.Secondly,AEMO is introduced to solve the trajectory optimization problem.Based on the theories of biology and cognition,the trial solutions based on emotional memory are established.Three search strategies are designed for realizing the random search of trial solutions and for avoiding becoming trapped in a local minimum.The states of the trial solutions are determined according to the rules of memory enhancement and forgetting.As the iterations proceed,the trial solutions with poor quality will gradually be forgotten.Therefore,the number of trial solutions is decreased,and the convergence of the algorithm is accelerated.Finally,a numerical simulation is conducted,and the results demonstrate that the path and terminal constraints are satisfied and the method can realize satisfactory performance.

Artificial emotion model based on reinforcement learning mechanism of neural network

A hierarchical-processed frame construction of artificial emotion model for intelligent system is proposed in the paper according to the basic conclusion of emotional psychology. The general method of emotion processing, which considers only one single layer, has been changed in the presented construction. An artificial emotional development model is put forward based on reinforcement learning mechanism of neural network. The new model takes the emotion itself as reinforcement signal and describes its different influences on action learning efficiency corresponding to different individualities. In the end, simulation result based on child playmate robot is discussed and the effectiveness of the model is verified.

Individual difference of artificial emotion applied to a service robot

In order to enable personalized natural interaction in service robots, artificial emotion is needed which helps robots to appear as individuals. In the emotion modeling theory of emotional Markov chain model （eMCM） for spontaneous transfer and emotional hidden Markov model （eHMM） for stimulated transfer, there are three problems： 1） Emotion distinguishing problem： whether adjusting parameters of the model have any effects on individual emotions; 2） How much effect the change makes; 3） The problem of different initial emotional states leading to different resultant emotions from a given stimuli. To solve these problems, a research method of individual emotional difference is proposed based on metric multidimensional scaling theory. Using a dissimilarity matrix, a scalar product matrix is calculated. Subsequently, an individual attribute reconstructing matrix can be obtained by principal component factor analysis. This can display individual emotion difference with low dimension. In addition, some mathematical proofs are carried out to explain experimental results. Synthesizing the results and proofs, corresponding conclusions are obtained. This new method provides guidance for the adjustment of parameters of emotion models in artificial emotion theory.