Liu Ding

Bifurcation behaviours of peak current controlled PFC boost converter

Bifurcation behaviours of the peak current controlled power-factor-correction (PFC) boost converter, including fast-scale instability and low-frequency bifurcation, are investigated in this paper. Conventionally, the PFC converter is analysed in continuous conduction mode (CCM). This prevents us from recognizing the overall dynamics of the converter. It has been pointed out that the discontinuous conduction mode (DCM) can occur in the PFC boost converter, especially in the light load condition. Therefore, the DCM model is employed to analyse the PFC converter to cover the possible DCM operation. By this way, the low-frequency bifurcation diagram is derived, which makes the route from period-double bifurcation to chaos clear. The bifurcation diagrams versus the load resistance and the output capacitance also indicate the stable operation boundary of the converter, which is useful for converter design.

Synchronization of chaos using radial basis functions neural networks

The Radial Basis Functions Neural Network (RBFNN) is used to establish the model of a response system through the input and output data of the system. The synchronization between a drive system and the response system can be implemented by employing the RBFNN model and state feedback control. In this case, the exact mathematical model, which is the precondition for the conventional method, is unnecessary for implementing synchronization. The effect of the model error is investigated and a corresponding theorem is developed. The effect of the parameter perturbations and the measurement noise is investigated through simulations. The simulation results under different conditions show the effectiveness of the method.

Hopf bifurcation analysis of Chen circuit with direct time delay feedback

Direct time delay feedback can make non-chaotic Chen circuit chaotic. The chaotic Chen circuit with direct time delay feedback possesses rich and complex dynamical behaviours. To reach a deep and clear understanding of the dynamics of such circuits described by delay differential equations, Hopf bifurcation in the circuit is analysed using the Hopf bifurcation theory and the central manifold theorem in this paper. Bifurcation points and bifurcation directions are derived in detail, which prove to be consistent with the previous bifurcation diagram. Numerical simulations and experimental results are given to verify the theoretical analysis. Hopf bifurcation analysis can explain and predict the periodical orbit (oscillation) in Chen circuit with direct time delay feedback. Bifurcation boundaries are derived using the Hopf bifurcation analysis, which will be helpful for determining the parameters in the stabilisation of the originally chaotic circuit.

Breaking chaotic shift key communication via adaptive key identification

This paper proposes an adaptive parameter identification method for breaking chaotic shift key communication from the transmitted signal in public channel. The sensitive dependence property of chaos on parameter mismatch is used for chaos adaptive synchronization and parameter identification. An index function about the synchronization error is defined and conjugate gradient method is used to minimize the index function and to search the transmitters parameter (key). By using proposed method, secure key is recovered from transmitted signal generated by low dimensional chaos and hyper chaos switching communication. Multi-parameters can also be identified from the transmitted signal with noise.

Chaos Control and Anti-control via a Fuzzy Neural Network Inverse System Method

We propose a new method for chaos control and anti-control, which is referred to as the fuzzy-neural network inverse system method (FNNIS). The Sugeno-type fuzzy-neural network (FNN) is employed to learn the kinetics of the system to be controlled. Then the FNN model is used with the inverse system method to make the system to be controlled to track the reference input. If the system to be controlled is chaotic and the reference input is non-chaotic, chaos control can be implemented via the FNNIS method. If the system to be controlled is non-chaotic and the reference input is chaotic, chaos anti-control can be implemented. Theorems about the effect of the FNN model error upon control are established. The simulation results show that this method is feasible and effective for chaos control and anti-control.

A novel digital position servo system using DSP and fuzzy PID

The paper presents a cost effective and increased performance position servo system using the TMS320F240 digital signal processor (DSP) produced by Texas Instruments as microprocessor and brushless direct current motor (BLDCM) as executor. In order to make up for the drawback of conventional PID controls, the fuzzy PID is employed. The result of simulations and experiments has confirmed that the whole system is simple and reliable; the robustness of the system is improved by using fuzzy PID.

Chaos prediction and inverse system control based on fuzzy-neural network

The Sugeno fuzzy-neuron network (FNN) is employed to establish the inverse system model of chaotic systems, and the inverse system method is used to control chaos. The characteristics of this method is learning the motion principles of the chaotic system by FNN and controlling chaos effectively with learned principles instead of establishing the exact analytical model of the chaotic system. Moreover, this method does not require the control objective to be a stationary point or a periodic trajectory. Theoretical analysis and simulations with Logistic and Henon mappings prove that this method is effective.