An adaptive CEEMD-ANN algorithm and its application in pneumatic conveying flow pattern identification

The accurate measurement of dust concentration using electrostatic sensor is serious affected by two-phase flow patterns in practice. In this paper, the electrostatic sensor signals of flow in a pneumatic conveying pipeline were collected, and the electrostatic fluctuation signals of three typical flow patterns of gas–solid two-phase flow in the horizontal pipe were obtained. By combining complementary ensemble empirical mode decomposition (CEEMD) and a back propagation (BP) neural network, an algorithm for flow pattern identification is proposed. This algorithm can adaptively determine the number of layers of the intrinsic mode function (IMF) decomposition and the number of input vectors for the neural network, ensuring the minimum size vector is used. The selected IMF energy feature as the input of the BP neural network can effectively ensure that an accurate flow pattern discrimination rate is obtained. The experimental results show that the algorithm proposed in the paper can guarantee the recognition rate of the flow pattern to reach more than 99%, yet through adaptive adjustment ensure that the size of trained BP neural network input is as small as possible, and the guaranteed algorithm calculation is kept at a minimum.     

Hybrid control of an active suspension system with full-car model using H∞ and nonlinear adaptive control methods

This paper presents hybrid control of an active suspension system with a full-car model by using H_∞ and nonlinear adaptive control methods. The full-car model has seven degrees of freedom including heaving, pitching and rolling motions. In the active suspension system, the controller shows good performance: small gains from the road disturbances to the heaving, pitching and rolling accelerations of the car body. Also the controlled system must be robust to system parameter variations. As the control method, H_∞ controller is designed so as to guarantee the robustness of a closed-loop system in the presence of uncertainties and disturbances. The system parameter variations are taken into account by multiplicative uncertainty model and the system robustness is guaranteed by small gain theorem. The active system with H_∞ controller can reduce the accelerations of the car body in the heaving, pitching and rolling directons. The nonlinearity of a hydraulic actuator is handled by nonlinear adaptive control based on the back-stepping method. The effectiveness of the controllers is verified through simulation results in both frequency and time domains.     

Analysis of robust stability and performance for two-degree of freedom control structure with dynamic controller

In the two-degree of freedom control, the performance of good command following and disturbance rejection are considered separately. Qualitatively, good performance is equivalent to minimizing the energy of the error for any inputs. In this work, usingH _∞-formulation in the frequency domain, robust stability and robust performance specifications have been analyzed for the two-degree of freedom control structure with a dynamic controller. When the two-degree of freedom system having a feed-forward loop is controlled by a dynamic controller, two different performance weight functions are imposed and the robust performance specification is proposed in terms of the return ratio and feed-forward loop. The design algorithm in the frequency domain is illustrated for the simplified retail model of Industrial Dynamics to compare three kinds of control laws, which are the output feedback control scheme and two additional dynamic control ones. Numerical simulation results show that the dynamic control laws provide a larger robust stability margin than the output feeback control one and has good performance robustness for disturbance rejection at low frequencies.