Novel Parameter Identification Method for Basis Weight Control Loop of Papermaking Process

The basis weight control loop of the papermaking process is a non-linear system with time-delay and time-varying.It is impractical to identify a model that can restore the model of real papermaking process.Determining a more accurate identification model is very important for designing the controller of the control system and maintaining the stable operation of the papermaking process.In this study,a strange nonchaotic particle swarm optimization(SNPSO)algorithm is proposed to identify the models of real papermaking processes,and this identification ability is significantly enhanced compared with particle swarm optimization(PSO).First,random particles are initialized by strange nonchaotic sequences to obtain high-quality solutions.Furthermore,the weight of linear attenuation is replaced by strange nonchaotic sequence and the time-varying acceleration coefficients and a mutation rule with strange nonchaotic characteristics are utilized in SNPSO.The above strategies effectively improve the global and local search ability of particles and the ability to escape from local optimization.To illustrate the effectiveness of SNPSO,step response data are used to identify the models of real industrial processes.Compared with classical PSO,PSO with timevarying acceleration coefficients(PSO-TVAC)and modified particle swarm optimization(MPSO),the simulation results demonstrate that SNPSO has stronger identification ability,faster convergence speed,and better robustness.

Positioning-control Based on Trapezoidal Velocity Curve for High-precision Basis Weight Control Valve

Traditionally, basis weight control valve is driven by a constant frequency pulse signal. Therefore, it is difficult for the valve to match the control precision of basis weight. Dynamic simulation research using Matlab/Simulink indicates that there is much more overshoot and fluctuating during the valve-positioning process. In order to improve the valve-positioning precision, the control method of trapezoidal velocity curve was studied. The simulation result showed that the positioning steady-state error was less than 0.0056%, whereas the peak error was less than 0.016% by using trapezoidal velocity curve at 10 positioning steps. A valve-positioning precision experimental device for the stepper motor of basis weight control valve was developed. The experiment results showed that the error ratio of 1/10000 positioning steps was 4% by using trapezoidal velocity curve. Furthermore, the error ratio of 10/10000 positioning steps was 0.5%. It proved that the valve-positioning precision of trapezoidal velocity curve was much higher than that of the constant frequency pulse signal control strategy. The new control method of trapezoidal velocity curve can satisfy the precision requirement of 10000 steps.

Adaptive Sliding Mode Control for Re-entry Attitude of Near Space Hypersonic Vehicle Based on Backstepping Design

Combining sliding mode control method with radial basis function neural network(RBFNN), this paper proposes a robust adaptive control scheme based on backstepping design for re-entry attitude tracking control of near space hypersonic vehicle(NSHV) in the presence of parameter variations and external disturbances. In the attitude angle loop, a robust adaptive virtual control law is designed by using the adaptive method to estimate the unknown upper bound of the compound uncertainties. In the angular velocity loop, an adaptive sliding mode control law is designed to suppress the effect of parameter variations and external disturbances. The main benefit of the sliding mode control is robustness to parameter variations and external disturbances. To further improve the control performance, RBFNNs are introduced to approximate the compound uncertainties in the attitude angle loop and angular velocity loop, respectively. Based on Lyapunov stability theory, the tracking errors are shown to be asymptotically stable. Simulation results show that the proposed control system attains a satisfied control performance and is robust against parameter variations and external disturbances.

Maximum power point tracking of a photovoltaic energy system using neural fuzzy techniques

In order to improve the output efficiency of a photovoltaic （PV） energy system, the real-time maximum power point （MPP） of the PV array should be tracked closely. The non-linear and time-variant characteristics of the photovoltaic array and the non-linear and non-minimum phase characteristics of a boost converter make it difficult to track the MPP as in traditional control strategies. A neural fuzzy controller （NFC） in conjunction with the reasoning capability of fuzzy logical systems and the learning capability of neural networks is proposed to track the MPP in this paper. A gradient estimator based on a radial basis function neural network is developed to provide the reference information to the NFC. With a derived learning algorithm, the parameters of the NFC are updated adaptively. Experimental results show that, compared with the fuzzy logic control algorithm, the proposed control algorithm provides much better tracking performance.

Radial basis function‐based exoskeleton robot controller development

The realisation of a model‐based controller for a robot with a higher degree of freedom requires a substantial amount of computational power.A high‐speed CPU is required to maintain a higher sampling rate.Multicore processors cannot boost the performance or reduce the execution time as the programs are sequentially structured.The neural network is a great tool to convert a sequentially structured program to an equivalent parallel architecture program.In this study,a radial basis function(RBF)neural network is developed for controlling 7 degrees of freedom of the human lower extremity exoskel-eton robot.A realistic friction model is used for modelling joint friction.High trajectory tracking accuracies have been obtained.Evidence of computational efficiency has been observed.The stability analysis of the developed controller is presented.Analysis of variance is used to assess the controller's resilience to parameter variation.To show the effectiveness of the developed controller,a comparative study was performe between the developed RBF network‐based controller and Sliding Mode Controller,Computed Tor-que Controller,Adaptive controller,Linear Quadratic Regulator and Model Reference Computed Torque Controller.