Discrete ZNN models of Adams-Bashforth (AB) type solving various future problems with motion control of mobile manipulator

Abstract Zhang neural network (ZNN), being a special type of recurrent neural network, has shown powerful abilities to solve a great variety of continuous time-varying problems. In order to solve future problems (or termed discrete time-varying problems), discrete ZNN (DZNN) models should be developed. In this paper, four kinds of future problems, i.e., future linear system (FLS), future division (FD), future quadratic minimization (FQM), and future equality-constrained quadratic programming (FECQP), are investigated. The DZNN models of Adams-Bashforth (AB) type are thus proposed for solving four kinds of future problems. Compared with conventional DZNN models of Euler type and Taylor type, the performances of DZNN models of AB type in terms of accuracy are better. Meanwhile, the numerical results substantiate the efficacy and superiority of DZNN models of AB type for solving future problems. Furthermore, the motion control of mobile robot manipulator is conducted to substantiate the efficacy of DZNN models of AB type for solving future problems.

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