Active Disturbance Rejection Control of Certain Balanced and Positioning Electro-Hydraulic Servo System Based on Neural Network
For certain balance and positioning of electro-hydraulic servo system existing nonlinear, the active disturbance rejection control (ADRC) has many adjustable parameters which are difficult to regulate, so active disturbance rejection control with neural network (NN-ADRC) is developed in this paper. The method uses neural network self-learning ability, through a single neuron adaptive configuration parameters to complete an online self-tuning parameters, while taking advantage of RBF neural network as identifier to identify the controlled object gradient information. Simulation results show that: the controller parameters are reduced significantly, and effectively inhibit the system unbalance force disturbance and realize the accurate positioning. It also has fast response speed, no overshoot, and high steady-state accuracy.
A novel active disturbance rejection-based control strategy for a gun control system
To compensate for the nonlinearity and to achieve finely-tuned tracking accuracy of a gun control system driven by an AC machine, an improved active disturbance rejection control (IADRC) strategy with neural network embedding (NN-IADRC) is developed in this paper. The proposed IADRC, which has amnestic memory effects, can be regarded as an extension of the conventional ADRC (CADRC), making it a special case of the IADRC. To further attenuate the dependence on system models and enhance the disturbance rejection capacities of the IADRC strategy, an on-line NN-based optimum updating approach is also developed in this paper. Finally, a series of experiments are conducted on the semi-physical simulation platform to estimate the performance of the control system and the effects of the memory factor on the system. The experimental results confirm that the proposed NN-IADRC is highly robust. The results also confirm that it performs more excellently than the CADRC and that its fine tuning has attained tracking accuracy.
Modeling of chemical reactors—XXIX mixing-cell model for packed bed reactors. Steady state considerations. Problems of multiplicity
Abstract The axial heat and mass transfer in tubular catalytic reactors may be described by mixing-cell model and diffusion model as well. The diffusion description yields for a strong exothermic reaction and adiabatic conditions three steady state profiles, on the other hand the mixing-cell model gives rise to more than three steady states. A simple numerical and graphical analysis for mixing-cell model is presented.