Singularity-Free Adaptive Controller for Uncertain Hysteresis Suspension Using Magnetorheological Elastomer-Based Absorber

The magnetorheological elastomer (MRE) is a smart material widely used in recent vibration systems. A system using these materials often faces difficulties designing the controller such as unknown parameters, hysteresis state, and input constraints. First, a model is designed for the MRE-based absorber to portray the behavior of MRE and predict the appropriate electric current supplied. The conventional adaptive controller often suffers from so-called control singularities. The singularity-free adaptive controller is proposed to eliminate the singularity with parametric uncertainty. The proposed controller consists of four components: an adaptive linearizing controller, a deputy adaptive neural network controller, an auxiliary part designed for the controller to overcome the input constraint problem, and a smooth switching algorithm used to exchange the takeover rights of the two controllers. Moreover, the controller is designed to obtain the stabilization of hysteretic state estimation for the vibration system. The adaptive algorithms are proposed to update the unknown system parameters and to observe the unmeasurable hysteretic state. Meanwhile, closed-loop system stability is comprehensively assessed. Finally, the simulation performed on a quarter-car suspension with an MRE-based absorber shows the proposed controller's efficiency.

An Online Parameter Estimation Using Current Injection with Intelligent Current-Loop Control for IPMSM Drives

An online parameter estimation methodology using the d-axis current injection, which can estimate the distorted voltage of the current-controlled voltage source inverter (CCVSI), the varying dq-axis inductances, and the rotor flux, is proposed in this study for interior permanent magnet synchronous motor (IPMSM) drives in the constant torque region. First, a d-axis current injection-based parameter estimation methodology considering the nonlinearity of a CCVSI is proposed. Then, during current injection, a simple linear model is developed to model the cross- and self-saturation of the dq-axis inductances. Since the d-axis unsaturated inductance is difficult to obtain by merely using the recursive least square (RLS) method, a novel tuning method for the d-axis unsaturated inductance is proposed by using the theory of the maximum torque per ampere (MTPA) with the combination of the RLS method. Moreover, to improve the bandwidth of the current loop, an intelligent proportional-integral-derivative (PID) neural network controller with improved online learning algorithm is adopted to replace the traditional PI controller. The estimated the dq-axis inductances and the rotor flux are adopted in the decoupled control of the current loops. Finally, the experimental results at various operating conditions of the IPMSM in the constant torque region are given.

Design and Simulation of an Off-Grid Photovoltaic System with Duty Cycle Prediction Using Neural Network Controller

Global concerns over the inappropriate utilization of abundant renewable energy sources, the damages due to instability of fuel prices, and fossil fuels' effect on the environment have led to an increased interest in green energy (natural power generation) from renewable sources. In renewable energy, photovoltaic is relatively the dominant technique and exhibits non-linearities, leading to inefficiencies. Maximum Power Point is required to be tracked rapidly and improve the power output levels. The target is to use a Neural network controller by training historical data of ambient irradiance and temperature levels as inputs and voltage levels as output for the photovoltaic module to predict duty cycles across the DC-DC converter. The DC-DC converter is the electrical power conditioner at the Botswana International University of Science and Technology, Palapye Off-Grid photovoltaic system. Perturb and Observe algorithm on PSIM environment is only implemented to acquire the historical data for the training and Matlab for the modeling of the network. Relatively long period ambient irradiance and temperature data of Palapye were acquired from the Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) WeatherNet in Botswana. Matlab environment was used for the simulation of the backpropagation algorithm for training. The Neural network's feedforward to optimize the non-linear nature of the PV module input and output relationship with relatively fewer processes is required. The results show promising, and the Mean Errors appear to be typically about 0.1 V, and the best performance is 193.5812 at Epoch 13, while the regression delivered a relatively low measured error. The maximum power delivered by the duty cycles from the model with 90 % prediction accuracy. The article demonstrates Neural Network controller is more efficient than the conventional Perturb and Observe Maximum Power Point algorithm.