Catálogo de publicaciones - libros

A robust tracking controller with bound estimation based on neural network is proposed to deal with the unknown factors of nonholonomic mobile robot, such as model uncertainties and external disturbances. The neural network is to approximate the uncertainties terms and the interconnection weights of the neural network can be tuned online. And the robust controller is designed to compensate for the approximation error. Moreover, an adaptive estimation algorithm is employed to estimate the bound of the approximation error. The stability of the proposed controller is proven by Lyapunov function. The proposed neural network-based robust tracking controller can overcome the uncertainties and the disturbances. The simulation results demonstrate that the proposed method has good robustness.

NNPC has been used widely to control nonlinear systems. However traditional gradient decent algorithm (GDA) needs a large computational cost, so that NNPC is not acceptable for systems with rapid dynamics. To apply NNPC in fast control of mobile robots, the paper proposes an improved optimization technique, particle swarm optimization with controllable random exploration velocity (PSO-CREV), to replace of GDA in NNPC. Therefore for one cycle of control, PSO-CREV needs less iterations than GDA, and less population size than conventional PSO. Hence the computational cost of NNPC is reduced by using PSO-CREV, so that NNPC using PSO-CREV is more feasible for the control of rapid processes. As an example, a test of trajectory tracking using mobile robots is chosen to compare performance of PSO-CREV with other algorithms to show its advantages, especially on the aspect of computational time.

The paper proposed a method for a quadruped robot control system based Central Pattern Generator (CPG) and fuzzy neural networks (FNN). The common approach for the control of a quadruped robot includes two methods mainly. One is the CPG that is based the bionics, the other is the dynamic control that is based the model of quadruped robot. The control result of CPG is decided by the gait data of the quadruped and the parameters of the CPG are choosing manually. Modeling a quadruped robot is difficult because it is a high nonlinear system. This paper presents a much simpler method for the control of a quadruped robot. A simple CPG is adopted for a timing oscillator; it generates the motion periodic pattern of legs. The FNN is used to control the joint motion in order to get a desired stable trajectory motion.

In cooperative multiple robot systems (CMRS), dynamic knowledge representation and inference is the key in scheduling robots to fulfill the cooperation requirementsThe first goal of this work is to use rough set based rules generation method to extract dynamic knowledge of our CMRS. Kang’s rough set based rules generation method is used to get fuzzy dynamic knowledge from practical decision data. The second goal of this work is to use Fuzzy Neural Petri nets (FNPN) to represent and infer the dynamic knowledge on the base of dynamic knowledge extraction with self-learning ability. In particular, we investigate a new way to extract, represent and infer dynamic knowledge with self-learning ability in CMRS. Finally, the effectiveness of the dynamic knowledge extraction, representation and inference procedure are demonstrated.

This paper presents a method of force/position control by using the backstepping and passivity strict-feedback neural networks technique; passivity monitor can evaluate stability of a system based on the concept of passivity. The parameters estimation for the design is made by the neural networks technology, using the decouple method and matrix transforming technology, decomposing the robot system as the position subsystem and the force subsystem, then the control law of these subsystems are designed respectively. The results obtained are satisfactory by using hybrid force and position control, the error is negligible and the global stability of the system can also be obtained.

This paper discusses a mathematical model of network, which is more general than the cellular neural networks(CNNs). In this study, we discuss some dynamical properties of this type of network, such as the distribution of equilibrium points and the influence of external input on stability. Moreover, we give some criterions, which ensure the complete stability of this network.

Locally and globally asymptotical stability on equilibria of delayed neural networks with saturation activation functions are studied by the Razumikhin-type theorems, which are the main approaches to study the stability of functional differential equations, and some new stability conditions are obtained, which are constructed by the networks’ parameters. In the case of local stability conditions, the attracted fields of equilibria are also estimated. All results obtained in this paper need only to compute the eigenvalues of some matrices or to verify some inequalities to be holden.

In this paper, we study the global asymptotic stability properties of cellular neural networks with variable coefficients and time varying delays. We present sufficient conditions for the global asymptotic stability of the neural networks . The proposed conditions, which are applicable to all continuous nonmonotonic neuron activation functions and do not require the interconnection matrices to be symmetric, establish the relationships between network parameters of the neural systems and the delay parameters. Some examples show that our results are new and improve the previous results derived in the literature.

Continuous attractor is a promising model for describing the encoding of continuous stimuli in neural systems. In a continuous attractor, the stationary states of the neural system form a continuous parameter space, on which the system is neutrally stable. This property enables the neutral system to track time-varying stimulus smoothly. In this study we investigate the tracking speed of continuous attractors. In order to analyze the dynamics of a large-size network, which is otherwise extremely complicated, we develop a strategy to reduce its dimensionality by utilizing the fact that a continuous attractor can eliminate the input components perpendicular to the attractor space very quickly. We therefore project the network dynamics onto the tangent of the attractor space, and simplify it to be a one-dimension Ornstein-Uhlenbeck process. With this approximation we elucidate that the reaction time of a continuous attractor increases logarithmically with the size of the stimulus change. This finding may have important implication on the mental rotation behavior.

In this paper, the periodic solution for a class of Cohen-Grossberg neural networks with variable coefficients is discussed. By using inequality analysis technique and matrix theory, some new sufficient conditions are obtained to ensure the existence, uniqueness, global attractivity and exponential stability of the periodic solution. An example is given to show the effectiveness of the obtained results.