Improving DC Power Supply Efficiency with Neural Network Controller

This article analyses the dynamic behaviour of a beam structure containing multiple transverse cracks using neural network controller. The first three natural frequencies and mode shapes have been calculated using theoretical, finite-element, and experimental analysis for the cracked and un-cracked beam. Comparisons of the results among theoretical, finite-element, and experimental analysis have also been presented. The calculated vibration signatures were used to train the feed-forward multi-layered neural network controller with back-propagation technique for the prediction of cracks. Relative crack locations and relative crack depths are the output of the neural controller. Results obtained from the various analyses are validated using the developed experimental set-up. Results from neural controller have been presented for comparison with the output from theoretical, finite-element, and experimental analysis. From the evaluation of the performance of the neural network controller it is observed that the developed method can be used as a crack diagnostic tool in the domain of dynamically vibrating structures.

This study aims to evaluate the behavior of neural network control method for digitally controlled dc-dc converters when the circuit parameter condition varies from which is in the training of the neural network. Learning based methods, which include the neural network control, are constructed and tuned using the training data to control given plants. They can provide the effective control since they are tuned as the dedicated one, however, they have difficulties when the parameters of components in the plant are changed from original ones, which are used to obtain the training data. Therefore, they may lose the effectivity in control if the the parameters in plant are changed. In the case of controlling dc-dc converters, the circuit parameter condition such as capacitance, reactor and so forth, are varied in the case that the specification of the circuit design is changed. Moreover they are changed by the environmental condition such as temperature, aging degradation and so forth. In this study, we study the effectivity of neural network control method for dc-dc converters when the circuit parameters are changed from the ones which are used in the training. When the circuit parameters are changed from original one, we evaluate that whether the neural network can control without any tuning, such as re-training of the neural network. From evaluation results, we confirm that the neural network control can work effectively even in such situation and it reveals that the neural network control has the robustness against the change of the circuit parameter condition.

Objective: BLDC motor specifically used for high speed applications and needs sophisticated speed control. Conventional controls techniques require more physical involvement for assisting BLDC motor to operate with precise speed value. However, soft tuning based controller may give comparatively better results for that and the same has been elaborated in present paper using neural controller of speed as per requirements.the main objective of this study may be summarized as (i) MATLAB simulink model for BLDC motor incorporated with soft tuned controller and inverter simultaneously, (ii) development of neural controller for BLDC motor control, and (iii) validation of results developed through proposed controller with the available results using PI, PID and fuzzy logic controller. Methods: The speed obtained through simulink model with input supply voltage variations are used as a reference signal for the training and testing purpose of neural controller. With the help of these reference signals, neural control executes and gives optimum values of external parameters such as speed and electromagnetic torque. Result: The study outcome provides dynamic execution of proposed controllerbased on neural network. The proposed controller’s results are validated with the reference results obtained mathematically for the system. The controller efficiency and sensitivity has been checked by MATLAB-Simulink software. Novelty: comparative study for mathematically obtained reference values and neural controller based results are proposed in this paper. The results are validated with the help of Matlab produces tables and figures. Keywords: BLDC Motor; speed control; neural network controller

This paper describes the design and implementation of a neural network controller capable of printing designs and drawings on paper using a Cartesian Plotter. The Cartesian plotter uses two dc motors with incremental encoders for motion along x and y-axis while third dc motor along z-axis controls the pen assembly for drawing the text and graphics data. The motion commands for the motors are generated with the help of a simple feed forward neural network controller trained with back propagation algorithm. The nine inputs, two output neural controller employs four neurons in the hidden layer with tangent sigmoid being activation function for hidden neurons while linear function is used for output neurons. The proposed neural controller can drive the pen assembly in one of the eight possible directions and thus produces similar results to the famous Bresenham's algorithm. Simulations and practical results validate the neural controller for use with Cartesian plotters.

End milling operations are essential to machine complex-contoured parts which are widely used in automobile and aerospace industries. Controlling the cutting force is important in these operations. But this is a difficult task for a number of reasons. A neural network control scheme is developed in this study to control the average resultant cutting force. The control scheme involves a neural identifier and a neural controller. The neural identifier is trained to represent the process accurately, and the neural controller is trained to give an input to the process which will yield the desired output. Recurrent neural networks are employed for both the neural identifier and neural controller, and training is accomplished using a recursive least squares algorithm. Results validating the methodology are presented for one-dimensional milling.

In this Chapter we provide the reader with some background material on neural control strategies and we discuss neural optimal control in more detail. The Chapter is organized as follows. In Section 4.1 the basic principles of existing methods in neural control are presented, including direct and indirect adaptive control, reinforcement learning, neural optimal control, internal model control and model predictive control. In Section 4.2 neural optimal control is discussed with respect to classical theory of nonlinear optimal control. The emphasis in this Section is on the formulation of control problems as parametric optimization problems, where static and dynamic nonlinear controllers are parametrized by multilayer perceptrons. Furthermore an efficient way for including a priori results from linear control theory into the neural controller is highlighted. The latter is illustrated on the examples of swinging up an inverted and double inverted pendulum system. New contributions are stated in Section 4.2.6.

This paper develops a neural network (NN) vector controller for an interior mounted permanent magnet (IPM) motor by using a Texas Instrument TMS320F28335 digital signal processor (DSP). The NN controller is developed based on the complete state-space equation of an IPM motor and it is trained to achieve optimal control according to approximate dynamic programming (ADP). A DSP-based NN control system is built for an IPM motor drives system, and a high efficient DSP program is developed to implement the NN control algorithm while considering the limited memory and computing capability of the TMS320F28335 DSP. The DSP-based NN controller is able to manage IPM motor control in linear, over, and six-step modulation regions to improve the efficiency of IPM drives and to allow for the full utilization of DC bus voltage with space-vector pulse-width modulation (SVPWM). The experiment results show that the proposed NN controller is able to operate with a sampling period of 0.1ms, even with limited DSP resources of up to 150 MHz cycle time, which is applicable in practical motor industrial implementations. The NN controller has demonstrated a better current and speed tracking performance than the conventional standard vector controller for IPM operation in both the linear and over-modulation regions.

Adaptive growing-and-pruning neural network control for a linear piezoelectric ceramic motor

A new diagnostic method for the dc-dc converters is proposed in this paper. The shape of voltage across the magnetic component is used as the diagnostic criterion. The Fast Fourier Transform (FFT) is utilized to extract the features of the waveform and the neural network (NN) is applied to realize the state classification. The voltage sensor is needless because the required voltage signatures can be obtained easily by adding a winding in the magnetic component, or fixing a magnetic near field probe near the magnetic component. The diagnostic system is isolated from the power stage naturally; all the A/D conversion, FFT and NN are realized in a single DSP chip TMS320F2812; using the inner A/D channels of the DSP, up to sixteen dc-dc converters can be monitored synchronously. For the illustrative purpose, the diagnostic process of one A/D channel will be described in this paper and the phase shift full bridge (PSFB) converter is chosen as the diagnostic object. Based on the discussion, the proposed method can be easily extended to the other types of dc-dc converters.

The article discusses the prerequisites and natural consequences of the control methods development in engineering systems: (1) a simple deviation and perturbation controller, (2) a fuzzy logic controller with a fuzzifier and a rule base, (3) a neural network controller for dynamically adjusting the coefficients of the corresponding links, (4) a discrete neural network controller with a neural approximator and controller. The experience gained by researchers and engineers since the first description of the principles of regulation in 1910 and the level of information technologies design, in particular the neural network method of machine learning and the enormous computing potential of computer devices, today can be integrated into a fundamentally new method of discrete neural network regulation.
 The review carried out in the article is aimed at identifying and demonstrating the significance of experimental and operational data, which must be properly structured and marked up at the stage of their collection and archiving. It is this approach that will allow us to quickly implement neural network controllers in engineering systems, since the most important stage for their creation is the process of learning and optimizing the architecture of neural networks.
 The principle of operation, advantages and disadvantages, the optimal stages of a neural network controller improvement based on two neural networks for the formation of a control strategy, taking into account the most probable state of the system at the next point in time, are given.

The present paper investigates an advanced control system of the wind energy conversion using an Artificial Neural Network (ANN) controller, which is developed in two modes: the offline mode, required for testing different sets of neural network parameters to find the optimal neural network controller (structure, activation function, and training algorithm), and the online mode where the optimal ANN controller is used in the five-phase PMSG based wind turbine system. The required data to generate the ANN model is obtained from a conventional PI controller. The controller inputs are the error between reference and actual speed (e), and the error change (de = e(k)-e(k-1)). As for the ANN output, it is the decreasing or increasing electromagnetic torque (dTe*). The studied neural network controller has been tested and validated using different wind speed values. Results and analysis are presented, and the contribution has been demonstrated.

DC-DC converters are often used in electrical systems to keep the output voltage constant. Boost converters are utilized for a variety of purposes, including regenerative braking of direct current motors, portable device applications, and regulated power supplies. Buck converters, on the other hand, are used in sophisticated communications, data communication, and self-regulating power supplies. One of the most crucial elements in the power conversion process is managing the DC-DC converters. This study aims to find out which nonlinear controller, fuzzy or neural network, works best when the output load or boost converter characteristics change. So, in this, we create a boost converter controller using a fuzzy and neural network. The fuzzy controller for this study's converter employs a standard set of rules, whereas the neural network controller employs two hidden layer networks. MATLAB software is then used to reconstruct both controllers. The simulation results show that the fuzzy logic controller has a very long transient and settling period with no steady-state error in both transient and steady-state situations. On the other hand, the neural network controller has a short transient and settling period with a steady-state error.

The aim of this paper is to propose a neural network based reference modified PID control which has a simple duration design method for transient characteristics improvement of digitally controlled dc-dc converters. In renewable energy network systems, various types of dc-dc converters are widely used for power conversion and such converters require a superior control method for a stable operation. Especially, transient characteristics should be improved since they heavily affect the stability of the system. For such purposes, designing of conventional control methods becomes a difficult task since the optimization of control parameters needs complicated analysis and it is affected from variations of circuit components of converters. Therefore, simple and easy design of control is widely required for a stable operation of power converters. The neural network can provide a suitable control methodology for such situation since it treats the plant as a black box and it can realize a non-linear control based on training of the input-output relation without complicated modelling and analysis. On the other hand, the neural network based method has a disadvantage caused from the fact that the neural network is trained with data obtained in advance and an overcompensation phenomenon occurs in the transient response. In this paper, the neural network control is adopted to control the dc-dc converter in coordination with a conventional PID control. The neural network predicts the output voltage of the converter and the reference value in the PID control is modified with the predictions to reduce the error of the output voltage. To avoid overcompensation, a simple duration design for the neural network control is also provided to improve the transient response effectively. From prototype testing in simulation and experiment, it is revealed that the proposed method contributes to obtain a superior transient performance compared with the conventional PID control.

This paper proposes neural network controllers using a reference model and investigates their characteristics. Since the neural network compensates the incorrectness of the plant such as nonlinearity and uncertainty against the reference model, a conventional controller can be designed based on the known dynamics of the reference model. The neural network controller can be classified into three types in terms of the connection between the neural network controller and the conventional controller. In the D-type controller, the output from the conventional controller inputs to the neural network and the reference model in parallel. In the Y-type controller, the output from the conventional controller inputs to the neural network, the plant and the reference model in parallel. The neural network of both controllers learns the combination of the inverse characteristics of the plant and the forward characteristics of the reference model. In the I-type controller, the output from the conventional controller inputs to the reference model whose output inputs to the neural network. The neural network leams the inverse characteristics of the plant. Simulation results using nonlinear plants show the achievement of the neural controllers and confirm their effectiveness for controlling the nonlinear plants.

The paper presents a neural network controller for trajectory tracking for a two D.O.F. SCARA robot mechanism. Two types of neural network controllers were built: a joint space neural network controller and a task space neural network controller. The two controllers were compared with a computed torque method controller in a joint as well as task space. The four controllers were tested on a real robot mechanism. The Lyapunov theory for deriving the adaptation law, or the learning algorithm of neural networks, was used to prove the robot system stability with a neural network controller. >