Catálogo de publicaciones - libros

The variable frequency speed-regulating system which consists of an induction motor and a general inverter, and controlled by PLC is widely used in industrial field. However, for the multivariable, nonlinear and strongly coupled induction motor, the control performance is not good enough to meet the needs of speed-regulating. The mathematic model of the variable frequency speed-regulating system in vector control mode is presented and its reversibility has been proved. By constructing a neural network inverse system and combining it with the variable frequency speed-regulating system, a pseudo-linear system is completed, and then a linear close-loop adjustor is designed to get high performance. Using PLC, a neural network inverse system can be realized in actural system. The results of experiments have shown that the performances of variable frequency speed-regulating system can be improved greatly and the practicability of neural network inverse control was testified.

The Loss-Free resistor (LFR) is applied to DC motor speed control system. A compensation Control algorithm based on LFR is proposed. The LFR is realized by means of switching network whose characteristics are nonlinear, thus, a neural network was designed and used. The switching on-off time can be instantaneously calculated by using the Neural-network-based algorithm proposed. The varies of the motor speed is realized by controlling the output of the LFR, the energy loss in the system is reduced compared with using a conventional power amplifier, and the dynamic characteristics of the system are also improved. The validation of the simulation results is proposed as well.

In this paper, a practical adaptive robust nonlinear controller is proposed for motion control of an SISO nonlinear mechanical system, where the distrubances due to ripple force and friction are compensated by the RBF networks. Rigorous analysis of transient performance and ultimate bound is given. Numerical examples are included to verify the theoretical results.

Reheat steam circle system is usually used in modern super-high parameters unit of power plant, which has the characteristics of long process channel, large inertia and long time lag, etc. Thus conventional PID control strategy cannot achieve good control performance. Prompted by the feedback regulation mechanism of biology immune response and the virtues of CMAC neural network, a composite control strategy based on CMAC neural network and immune PID controller is presented in this paper, which has the effect of feed-forward control for load changes as the unit load channel signal of reheat steam temperature is transmitted to the CMAC neural network to take charge of load change effects. The input signal of the controlled system are weighted and integrated by the output signals of CMAC neural network and immune PID controller, and then a variable parameter robust controller is constituted to act on the controlled system. Thus, good regulating performance is guaranteed in the initial control stage and also in case of characteristic deviations of the controlled system. Simulation results show that this control strategy is effective, practicable and superior to conventional PID control.

This paper presents the application of a Grey Box Neural Model (GNM) in adaptive-predictive control of the combustion chamber temperature of a pilot-scale vibrating fluidized dryer. The GNM is based upon a phenomenological model of the process and a neural network that estimates uncertain parameters. The GNM was synthesized considering the energy balance and a radial basis function neural network (RBF) trained on-line to estimate heat losses. This predictive model was then incorporated into a predictive control strategy with one step look-ahead. The proposed system shows excellent results with regard to adaptability, predictability and control when subject to setpoint and disturbances changes.

For stochastic systems with non-Gaussian variables, a descriptor nonlinear system model based on linear B-spline approximation is first established. A new tracking strategy based on state feedback control for the descriptor nonlinear system is proposed, with which the probability density functions (PDFs) tracking control problem of the non-Gaussian stochastic systems can be solved. Necessary and sufficient condition for the existence of state feedback controller of the problem is presented by linear-matrix-inequality (LMI). Furthermore, simulations on particle distribution control problems are given to demonstrate the efficiency of the proposed approach and encouraging results have been obtained.

The B-spline neural networks are used to model probability density function (PDF) with least square algorithm, the controllers are designed accordingly. Both the modeling and control methods are tested with molecular weight distribution (MWD) through simulation.

This paper presents a model predictive control (MPC) based on a neural network (NN) model for air/fuel ration (AFR) control of automotive engines. The novelty of the paper is that the severe nonlinearity of the engine dynamics are modelled by a NN to a high precision, and adaptation of the NN model can cope with system uncertainty and time varying effects. A single dimensional optimization algorithm is used in the paper to speed up the optimization so that it can be implemented to the engine fast dynamics. Simulations on a widely used mean value engine model (MVEM) demonstrate effectiveness of the developed method.

Dynamic programming (DP) is a useful tool for solving many control problems, but for its complexity in computation, traditional DP control algorithms are not satisfactory in fact. So we must look for a new method which not only has the advantages of DP but also is easier in computation. In this paper, approximate dynamic programming (ADP) based controller system has been used to solve a ship heading angle keeping problem. The ADP controller comprises successive adaptations of two neural networks, namely action network and critic network which approximates the Bellman equations associated with DP. The Simulation results show that the ship keeps the desired heading satisfactorily.

This paper discusses a new application of adaptive dynamic programming (ADP). Meanwhile, traffic control as an important factor in social development is a valuable research topic. Considering with advancement of ADP and importance of traffic control, this paper present a new signal control in a single intersection. Simulation results show that the proposed signal control is valid.