Catálogo de publicaciones - libros

A temperature control solution is proposed in this paper, which uses a self-tuning PID controller based on flexible neural network (FNN). The learning algorithm of FNN can adjust not only the connection weights but also the sigmoid function parameters. This makes FNN characterized with online learning and high learning speed. The FNN has the following advantages when applied to temperature control problems: high learning ability, which considerably reduces the controller training time; the mathematical model of the plant is not required, which eases the design process; high control performance. These advantages are verified by its application to a practical temperature controlled box, which is used in medicinal inspection. The proposed system presents better behavior than that when using traditional back-propagation neural network.

Neural network controller using adaptation algorithm is a new and simple controller, in which a feedback network propagating the error is not required. So it can be applied to hardware easily. Nevertheless, our simulations show that while the order of controlled plant is high, some unstable phenomenon appear and we also find that sometimes the error is far from being satisfactory, although when the order of controlled plant is low. Moreover, the present adaptation algorithm can not solve this problem. In this paper we will give our derivation of adaptation algorithm used in the neural network controller and configuration of an adaptive neural network controller. Then give some simulation figures to illustrate defect for the new controller. Finally we will develop a hybrid neural network to solve the problem and improve the accuracy as well as reduce the cost to the least in the practical application.

In this paper, adaptive neural network control is proposed for a class of strict-feedback nonlinear time-delay systems. Unknown smooth function vectors and unknown time-delay functions are approximated by two neural networks, respectively, such that the requirement on the unknown time-delay functions is relaxed. In addition, the proposed systematic backstepping design method has been proven to be able to guarantee semiglobally uniformly ultimately bounded of closed loop signals, and the output of the system has been proven to converge to a small neighborhood of the desired trajectory. Finally, simulation result is presented to demonstrate the effectiveness of the approach.

In this paper, a feedforward active noise control (ANC) system using a recurrent fuzzy neural network (RFNN) controller based on simultaneous perturbation stochastic approximation (SPSA) algorithm is considered. Because RFNN can capture the dynamic behavior of a system through the feedback links, only one input node is needed, and the exact lag of the input variables need not be known in advance. The SPSA-based RFNN control algorithm employed in the ANC system is first derived. Following this, computer simulations are carried out to verify that the SPSA-based RFNN control algorithm is effective for a nonlinear ANC system. Simulation results show that the proposed scheme is able to significantly reduce disturbances without the need to model the secondary-path and has better tracking ability under variable secondary-path. This observation implies that the SPSA-based RFNN controller eliminates the need of the modeling of the secondary-path.

This paper presents a neural network based control design to handle the stabilization of a class of multiple input nonlinear systems with time varying uncertain parameters while assuming that the range of each individual uncertain parameter is known. The proposed design approach allows incorporation of complex control performance measures and physical control constraints whereas the traditional adaptive control techniques are generally not applicable. The desired system dynamics are analyzed, and a collection of system dynamics data, that represents the desired system behavior and approximately covers the region of stability interest, is generated and used in the construction of the neural controller based on the proposed neural control design. Furthermore, the theoretical aspects of the proposed neural controller are also studied, which provides insightful justification of the proposed neural control design. The simulation study is conducted on a single-machine infinity-bus (SMIB) system with time varying uncertainties on its parameters. The simulation results indicate that the proposed design approach is effective.

The molten carbonate fuel cell (MCFC) is a complex system, and MCFC modeling and control are very difficult in the present MCFC research and development because MCFC has the complicated characteristics such as nonlinearness, uncertainty and time-change. To aim at the problem, the MCFC mechanism is analyzed, and then MCFC modeling based on feedback neural networks is advanced. At last, as a result of applying the model, a new MCFC control strategy is presented in detail so that it gets rid of the limits of the controlled object, which has the imprecision, uncertainty and time-change, to achieve its tractability and robustness. The computer simulation and the experiment indicate that it is reasonable and effective.

This paper is concerned with the problem of observer-based stabilization for time-delay systems. Both the state delay and input delay under consideration are assumed to be a constant time-delays, but not known exactly. A new design method is proposed for an observer-based controller with adaptation to the time-delays. The designed controller simultaneously contains both the current state and the past information of systems. The design for adaptation law to delay constants is more concise than the existing conclusions. The controller can be derived by solving a set of linear matrix inequalities (LMIs).

A two-stage structure model was developed for the vibration control of an actuator platform and a controller based on a three-layer neural network was applied to realize high performance control for the kickstand disturbance of a block forming machine. This paper presents a survey of the basic theory of the back-propagation(BP) neural network architecture including its architectural design, BP algorithm, the root mean square error (RMSE) and optimal model establishment. The situ-test data of the control system were measured by acceleration transducer and the experimental results indicates that the proposed method was effective.

A class of Internet congestion control algorithms with communication delays is studied. The algorithm is a pieced continuous function that will be switched on the rate of the source. Based on the Lyapunov theorem, the Lyapunov stability of the system is analyzed. By applying Barbalat Lemma, the global asymptotical stability (GAS) of the algorithm is proved, and a more concise criterion is presented.

A class of window-based network congestion control system with communication delays is studied. By analyzing the network system with communication delay, a critical value of the window size to ensure the stability of network is obtained, and a critical value of the delay to ensure the system stability is presented. Enlarging the delay across the critical value, we find that the congestion control system exhibits Hopf bifurcation.