Active Sensing of Robot Arms Based on Zeroing Neural Networks: A Biological-Heuristic Optimization Model

Abstract

Conventional biological-heuristic solutions via zeroing neural network (ZNN) models have achieved preliminary efficiency on time-dependent nonlinear optimization problems handling. However, the investigation on finding a feasible ZNN model to solve the time-dependent nonlinear optimization problems with both inequality and equality constraints still remains stagnant because of the nonlinearity and complexity. To make new progresses on the ZNN for time-dependent nonlinear optimization problems solving, this paper proposes a biological-heuristic optimization model, i.e., inequality and equality constrained optimization ZNN (IECO-ZNN). Such a proposed IECO-ZNN breaks the conditionality that the solutions via ZNN for solving nonlinear optimization problems can not consider the inequality and equality constraints at the same time. The time-dependent nonlinear optimization problem subject to inequality and equality constraints is skillfully converted to a time-dependent equality system by exploiting the Lagrange multiplier rule. The design process for the IECO-ZNN model is presented together with its new architecture illustrated in details. In addition, the conversion equivalence, global stability as well as exponential convergence property are theoretically proven. Moreover, numerical studies, real-world applications to robot arm active sensing, and comparisons sufficiently verify the effectiveness and superiority of the proposed IECO-ZNN model for the time-dependent nonlinear optimization with inequality and equality constraints. INDEX TERMS Zeroing neural networks (ZNNs), biological-heuristic optimization, nonlinear optimization, inequality and equality constraints, robot motion control. * (t) = {(t, x * (t))|∂f (x * (t), t)/∂x * (t) = 0},

Motion Planning of Redundant Manipulator With Variable Joint Velocity Limit Based on Beetle Antennae Search Algorithm

“…Among these works, computational intelligence methods are demonstrated to avoid unnecessary nonlinearity modeling and resolution in the analytic way. As the redundancy resolution problem for motion planning can be formulated as constrained optimization problems, these intelligent algorithms such as neural network (NN), genetic algorithm (GA) and particle swarm optimization (PSO) were applied to solve the constrained optimization problems [18] [3]. NN has the characteristics of adaptive learning, which can realize parallel distributed processing, with strong robustness and fault tolerance.…”