An online adaptive optimal control is proposed for continuous-time nonlinear systems with co mpletely unknown dynamics, which is achieved by developing a novel identifier-critic based approximate dynamic programming (ADP) algorith m with a dual neural network (NN) appro ximation structure. Firstly, an adaptive NN identifier is designed to obviate the requirement of complete knowledge of system dynamics, and a critic NN is emp loyed to approximate the optimal value function. Then the optimal control law is co mputed based on the informat ion fro m the identifier NN and the c rit ic NN so that the actor NN is not needed. In particu lar, a novel adaptive law design method with the parameter estimation error is proposed to online update the weights of both identifier NN and critic NN simu ltaneously, which converge to small neighborhoods of their ideal values. The closed-loop system stability and the convergence to small vicinity around the optimal solution are all proved by means of the Lyapunov theory. The proposed adaptation algorithm is also improved to achieve finite-time (FT) convergence of the NN weights. Finally, simulation results are provided to exemplify the efficacy of the proposed methods.

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6D pose estimation is a common and important task in industry. Obtaining the 6D pose of objects is the basis for many other functions such as bin picking, autopilot, etc. Therefore, many corresponding studies have been made in order to improve the accuracy and enlarge the range of application of various approaches. After several years of development, the methods of 6D pose estimation have been enriched and improved. Although some predecessors have analyzed the methods and summarized them in detailed, there have been many new breakthroughs in recent years. To understand 6D pose estimation better, this paper will make a new and more detailed review of 6D pose estimation. We divided these methods into two approaches: Learning-based approaches and non-learning-based approaches, including 2D-information-based approach and 3D-information-based approach. Additionally, we introduce the challenges that exist in 6D pose estimation. Finally, we compare the performance of different methods qualitatively and discuss the future development trends of the 6D pose estimation.

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