Solving time-varying quadratic programs based on finite-time Zhang neural networks and their application to robot tracking

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

“…Differing from the time-invariant problems, time-dependent problems are much more difficult to be addressed for the fact that the problems involved, the system coefficients as well as the solutions are all time-dependent [8]- [11]. The conventional time-invariant methods and models generating the related time-invariant solutions would be invalidation due to the inevitable delay (or to say, the lagging behind) errors [12], [13]. Note that the conventional time-invariant methods commonly generate solutions at current moment based on the present known information.…”

“…The ZNN is able to selectively define a scalar valued, vector valued, or even matrix valued indefinite error function, which fully utilize the time-derivative information for handling the time-dependent problems [42]- [44]. Conventional solutions via ZNN models have achieved preliminary efficiency on time-dependent nonlinear optimization, and many systems and models on the basis of ZNNs have been generalized and developed [12], [21], [35], [45]- [47]. For instances, as an early attempt, Jin and Zhang [47] introduced an effective discrete time ZNN model to handle the time-dependent nonlinear optimization problems without consideration of inequality constraint or equality constraint.…”

“…Finally, numerical studies, comparisons as well as the real-world applications to robot arm active sensing sufficiently verify the effectiveness and superiority of the proposed IECO-ZNN model for the time-dependent nonlinear optimization with both inequality and equality constraints. For better illustration, comprehensive comparisons of the proposed IECO-ZNN model with existing ones [1]- [3], [12], [19], [21], [33], [34] are presented in Table 1. To the best of the authors' knowledge, there is no an existing method or model for handling the time-dependent nonlinear optimization problem subject to both inequality and equality constraints, and successfully applying to robot active sensing with outstanding features of the proposed one.…”

Distributed observer‐based cooperative control for output regulation in multi‐agent linear parameter‐varying systems

“…This estimation error can be reduced to zero by applying recurrent neural networks [33] that have restrictions for real time implementation since the estimation error does not converge to zero in finite time [35]. The estimation error can, however, converge to zero in a finite time if sign-bi-power or Li activation functions are used [35,36], but the estimated upper bound of the convergence time is conservative [37]. In this paper, an analytical solution to a time-varying Sylvester equation is obtained for the special case that time-varying coefficients of the Sylvester equation have affine structures.…”

Finite-time recurrent neural networks for solving nonlinear optimization problems and their application

“…However, the neural networks proposed in [23] only deal with convex optimization problems. In this paper, in order to overcome these problems, we design a finite-time recurrent neural network based on the authors' previous work [24,25,26]…”