Lihong Huang

Robust Stability Criterion for Delayed Neural Networks with Discontinuous Activation Functions

The problem of global robust stability for a class of uncertain delayed neural networks with discontinuous activation functions has been discussed. The uncertainty is assumed to be of norm-bounded form. Based on Lyapunov–Krasovskii stability theory as well as Filippov theory, the conditions are expressed in terms of linear matrix inequality, which make them computationally efficient and flexible. An illustrative numerical example is also given to show the applicability and effectiveness of the proposed results.

Global Output Convergence of a Class of Recurrent Delayed Neural Networks with Discontinuous Neuron Activations

This paper studies the global output convergence of a class of recurrent delayed neural networks with time-varying inputs. We consider non-decreasing activations which may also have jump discontinuities in order to model the ideal situation where the gain of the neuron amplifiers is very high and tends to infinity. In particular, we drop the assumptions of Lipschitz continuity and boundedness on the activation functions, which are usually required in most of the existing works. Due to the possible discontinuities of the activations functions, we introduce a suitable notation of limit to study the convergence of the output of the recurrent delayed neural networks. Under suitable assumptions on the interconnection matrices and the time-varying inputs, we establish a sufficient condition for global output convergence of this class of neural networks. The convergence results are useful in solving some optimization problems and in the design of recurrent delayed neural networks with discontinuous neuron activations.

Intelligent hybrid control strategy for trajectory tracking of robot manipulators

We address the problem of robust tracking control using a PD-plus-feedforward controller and an intelligent adaptive robust compensator for a rigid robotic manipulator with uncertain dynamics and external disturbances. A key feature of this scheme is that soft computer methods are used to learn the upper bound of system uncertainties and adjust the width of the boundary layer base. In this way, the prior knowledge of the upper bound of the system uncertainties does need not to be required. Moreover, chattering can be effectively eliminated, and asymptotic error convergence can be guaranteed. Numerical simulations and experiments of two-DOF rigid robots are presented to show effectiveness of the proposed scheme.

Equilibrium Analysis for Improved Signal Range Model of Delayed Cellular Neural Networks

In this paper, a class of delayed cellular neural networks with unbounded activation functions and described by using space invariant cloning templates are considered. The general and explicit existing regions of equilibrium points are discussed based on dissipative theory, fixed point principle of iteration mapping and Brouwer Fixed-point Theorem. The sufficient condition is obtained to ensure the existence, uniqueness, local asymptotical stability of the equilibrium point in each saturation sub-region. Moreover, we give the condition for equilibrium point to be globally exponentially stable, and the explicit existing region of the unique equilibrium point is also located. These results extend previous works on these issues for the standard delayed cellular neural networks. Two numerical examples are given to show the validity of the obtained results.

A new delay-dependent H∞ control criterion for a class of discrete-time systems with time-varying state delays and input delays

This paper deals with delay-dependent H∞ control for discrete-time systems with time-varying state delays and input delays. A new finite-sum inequality is first established to derive a delay-dependent condition, under which the resulting closed-loop system via a state feedback is asymptotically stable with a prescribed H∞ noise attenuation level. Moreover, a modified iterative algorithm according to the previously published algorithm of Ghaoui and Oustry (IEEE Trans. Autom. Control, 1997, 42(8), 1171–1176) involving convex optimization is proposed to obtain a suboptimal H∞ controller. Two numerical examples are presented which show the effectiveness of the proposed method.

On Global Robust Stability of a Class of Delayed Neural Networks with Discontinuous Activation Functions and Norm-Bounded Uncertainty

This paper considers the problem of global robust stability of a class of uncertain delayed neural networks with discontinuous activation functions. The uncertainties are assumed to be norm-bounded. In the form of linear matrix inequality (LMI), a new sufficient condition is obtained for the robust stability of this class of neural networks based on Lyapunov–Krasovskii stability theory as well as Filippov theory. Our conditions are independent of the delay and easy to check. Axa0numerical example is given to show the effectiveness and superiority of our results.

EXISTENCE AND EXPONENTIAL STABILITY OF ALMOST PERIODIC SOLUTION FOR SHUNTING INHIBITORY CELLULAR NEURAL NETWORKS WITH DISTRIBUTED DELAYS AND LARGE IMPULSES

This paper considers the problem of existence and exponen- tial stability of almost periodic solution for shunting inhibitory cellular neural networks with distributed delays and large impulses. Based on the contraction principle and Gronwall-Bellmans inequality, some su- cient conditions are obtained. The results of this paper are new and they complement previously known results.

Robust adaptive control of Cohen-Grossberg neural networks with discontinuous activation functions

In this paper, robust adaptive control of Cohen-Grossberg neural networks with discontinuous activation functions is considered. Based on differential inclusion theory and matrix inequality technique, we originally propose the adaptive controller for neural networks with discontinuous activation functions. Our objective is to design the controller to ensure neural networks be globally asymptotically stable at its equilibrium point. The designed controller is accessible. Finally, a numerical example is given to verify the effectiveness and robustness of the proposed result.

Robust H ∞ intelligent tracking control for robot manipulators

A novel robust Hinfin intelligent control (RHIC) strategy is proposed for the trajectory following problem of robot manipulators. The proposed system is comprised of a computed torque controller and neural robust controller. Based on Lyapunov stability theorem, it is shown that the proposed controller can guarantee Hinfin tracking performance of robotic system, in the sense that all variables of the closed-loop system are bounded and the effect due to the external disturbance on the tracking error can be attenuated to any pre-assigned level. The proposed approach indicates that computed torque control method is also valid for controlling uncertain robotic manipulators as long as compensative controller is appropriately designed.

Robust H ∞ decentralized intelligent control with new learning algorithm for robot manipulators

A novel robust Hinfin decentralized intelligent control (RHDIC) strategy is proposed for the trajectory following problem of robot manipulators. The proposed system is comprised of a computed torque controller and neural robust controller with new learning algorithm. Based on Lyapunov stability theorem, it is shown that the proposed controller can guarantee stability of closed-loop systems and satisfactory tracking performances. The proposed approach indicates that computed torque control method is also valid for controlling uncertain robotic manipulators as long as compensative controller is appropriately designed.