AC voltage regulation of a bidirectional high-frequency link converter using a deadbeat controller

This paper presents a digital controller for AC voltage regulation of a bidirectional high-frequency link (BHFL) inverter using Deadbeat control. The proposed controller consists of inner current loop, outer voltage loop and a feed-forward controller, which imposes a gain scheduling effect according to the reference signal to compensate the steady-state error of the system. The main property of the proposed controller is that the current- and the voltage-loop controllers have the same structure, and use the same sampling period. This simplifies the design and implementation processes. To improve the overall performance of the system, additional disturbance decoupling networks are employed. This takes into account the model discretization effect. Therefore, accurate disturbance decoupling can be achieved, and the system robustness towards load variations is increased. To avoid transformer saturation due to low frequency voltage envelopes, an equalized pulse width modulation (PWM) technique has been introduced. The proposed controller has been realized using the DS1104 digital signal processor (DSP) from dSPACE. Its performances have been tested on a one kVA prototype inverter. Experimental results showed that the proposed controller has very fast dynamic and good steady-state responses even under highly nonlinear loads

Study on control strategy of the rotary synchronous fixed-length cutting system

According to the characteristics of rotating synchronous fixed-length cutting system and the principle of vector coordinate transformation, it respectively analyzes the mathematical model of three loops which are the position loop, speed loop and current loop of the servo fixed-length cutting system in this paper. In view of the different working conditions of the system and its nonlinear problem, it puts forward that the function of the speed loop is realized by the parameter adaptive fuzzy algorithm; the function of the position loop using is realized by feed forward proportional control algorithm; the function of the current loop is realized by the conventional PI control algorithm. It uses MATLAB to make simulation and verification, the results show that the combined control algorithm can make that the fixed-length cutting system has characteristics of fast speed, high precision and strong robustness properties

Fpga-based control of piezoelectric actuators

In many industrial applications like semiconductor production and optical inspection systems, the availability of positioning systems capable to follow trajectory paths in the range of several centimetres, featuring at the same time a nanometre-range precision, is demanding. Pure piezoelectric stages and standard positioning systems with motor and spindle are not able to meet such requirements, because of the small operation range and inadequacies like backlash and friction. One concept for overcoming these problems consists of a hybrid positioning system built through the integration of a DC-drive in series with a piezoelectric actuator. The wide range of potential applications enables a considerable market potential for such an actuator, but due to the high variety of possible positioned objects and dynamic requirements, the required control complexity may be significant. In this paper, a real-time capable state-space control concept for the piezoelectric actuators, embedded in such a hybrid micropositioning system, is presented. The implementation of the controller together with a real-time capable hysteresis compensation measure is performed using a low-budget FPGA-board, whereas the superimposed integrated controller is realized with a dSPACE RCP-system. The advantages of the designed control over a traditional proportional-integral control structure are proven through experimental results using a commercially available hybrid micropositioning system. Positioning results by different dynamic requirements featuring positioning velocities from 1 μm/s up to 5 cm/s are given

AC Voltage Regulation of a Bidirectional High-Frequency Link Converter Using a Deadbeat Controller

This paper presents a digital controller for ac voltage regulation of a bidirectional high-frequency link (BHFL) inverter using deadbeat control. The proposed controller consists of inner current loop, outer voltage loop and a feed-forward controller, which imposes a gain scheduling effect according to the reference signal to compensate the steady-state error of the system. The main property of the proposed controller is that the current- and the voltage-loop controllers have the same structure, and use the same sampling period. This simplifies the design and implementation processes. To improve the overall performance of the system, additional disturbance decoupling networks are employed. This takes into account the model discretization effect. Therefore, accurate disturbance decoupling can be achieved, and the system robustness towards load variations is increased. To avoid transformer saturation due to low frequency voltage envelopes, an equalized pulse width modulation (PWM) technique has been introduced. The proposed controller has been realized using the DS1104 digital signal processor (DSP) from dSPACE. Its performances have been tested on a one kVA prototype inverter. Experimental results showed that the proposed controller has very fast dynamic and good steady-state responses even under highly nonlinear load

Enhanced Fuzzy Sliding Mode Control to MotionController of Linear Induction Motor Drives

[[abstract]]In this paper, an enhanced fuzzy sliding mode control system (EFSMC) is proposed for a linear induction motor (LIM) to achieve the position tracking. First, the dynamic model of LIM is investigated for considering the end effect and the friction force into the observer-based compensation design to cope with the time-varying uncertainties. Then, a sliding mode control (SMC) based on the backstepping control technique is presented with the combination of two fuzzy logic controllers. The first fuzzy logic controller is proposed, through a dynamic tune of the sliding surface slope constant of the SMC according to the controlled system states by a fuzzy logic unit. To relax the need of the upper bound of the lumped uncertainties in the SMC, the second fuzzy logic controller is presented, in which the upper bound of the lumped uncertainties can be estimated by a fuzzy inference mechanism. Finally, the experiments for several scenarios are conducted to demonstrate the effectiveness and robustness of the designed controller.[[conferencetype]]國際[[conferencedate]]20140711~20140713[[booktype]]電子版[[iscallforpapers]]

Optimal greenhouse cultivation control: survey and perspectives

Abstract: A survey is presented of the literature on greenhouse climate control, positioning the various solutions and paradigms in the framework of optimal control. A separation of timescales allows the separation of the economic optimal control problem of greenhouse cultivation into an off-line problem at the tactical level, and an on-line problem at the operational level. This paradigm is used to classify the literature into three categories: focus on operational control, focus on the tactical level, and truly integrated control. Integrated optimal control warrants the best economical result, and provides a systematic way to design control systems for the innovative greenhouses of the future. Research issues and perspectives are listed as well

Design and control technique for single phase bipolar H-bridge inverter connected to the grid

The power quality injected into the grid and the performance of the converter system depend on the quality of the inverter current control. This paper proposes a design and control technique for a photovoltaic inverter connected to the grid based on the digital pulse-width modulation (DSPWM) which can synchronise a sinusoidal output current with a grid voltage and control a power factor. The current injected must be sinusoidal with reduced harmonic distortion. The connected PV system is based on H-Bridge inverter controlled by bipolar PWM Switching. The current control technique and functional structure of this system are presented and simulated. Detailed analysis, Simulations results of output voltage and current waveform demonstrate the contribution of this approach to determinate the suitable control of the system. A digital design of a generator PWM using VHDL is proposed and implemented on an Xilinx FPGA and it has been validated with experimental results. As a result, the proposed inverter implementation is simple, and it becomes an attractive solution for low power grid connected applications

Autonomously Reconfigurable Artificial Neural Network on a Chip

Artificial neural network (ANN), an established bio-inspired computing paradigm, has proved very effective in a variety of real-world problems and particularly useful for various emerging biomedical applications using specialized ANN hardware. Unfortunately, these ANN-based systems are increasingly vulnerable to both transient and permanent faults due to unrelenting advances in CMOS technology scaling, which sometimes can be catastrophic. The considerable resource and energy consumption and the lack of dynamic adaptability make conventional fault-tolerant techniques unsuitable for future portable medical solutions. Inspired by the self-healing and self-recovery mechanisms of human nervous system, this research seeks to address reliability issues of ANN-based hardware by proposing an Autonomously Reconfigurable Artificial Neural Network (ARANN) architectural framework. Leveraging the homogeneous structural characteristics of neural networks, ARANN is capable of adapting its structures and operations, both algorithmically and microarchitecturally, to react to unexpected neuron failures. Specifically, we propose three key techniques --- Distributed ANN, Decoupled Virtual-to-Physical Neuron Mapping, and Dual-Layer Synchronization --- to achieve cost-effective structural adaptation and ensure accurate system recovery. Moreover, an ARANN-enabled self-optimizing workflow is presented to adaptively explore a "Pareto-optimal" neural network structure for a given application, on the fly. Implemented and demonstrated on a Virtex-5 FPGA, ARANN can cover and adapt 93% chip area (neurons) with less than 1% chip overhead and O(n) reconfiguration latency. A detailed performance analysis has been completed based on various recovery scenarios

ESSE 2017. Proceedings of the International Conference on Environmental Science and Sustainable Energy

Environmental science is an interdisciplinary academic field that integrates physical-, biological-, and information sciences to study and solve environmental problems. ESSE - The International Conference on Environmental Science and Sustainable Energy provides a platform for experts, professionals, and researchers to share updated information and stimulate the communication with each other. In 2017 it was held in Suzhou, China June 23-25, 2017

Neurofuzzy controller based full vehicle nonlinear active suspension systems

To design a robust controller for active suspension systems is very important for guaranteeing the riding comfort for passengers and road handling quality for a vehicle. In this thesis, the mathematical model of full vehicle nonlinear active suspension systems with hydraulic actuators is derived to take into account all the motions of the vehicle and the nonlinearity behaviours of the active suspension system and hydraulic actuators. Four robust control types are designed and the comparisons among the robustness of those controllers against different disturbance types are investigated to select the best controller among them. The MATLAB SIMULINK toolboxes are used to simulate the proposed controllers with the controlled model and to display the responses of the controlled model under different types of disturbance. The results show that the neurofuzzy controller is more effective and robust than the other controller types. The implementation of the neurofuzzy controller using FPGA boards has been investigated in this work. The Xilinx ISE program is employed to synthesis the VHDL codes that describe the operation of the neurofuzzy controller and to generate the configuration file used to program the FPGA. The ModelSim program is used to simulate the operation of the VHDL codes and to obtain the expected output data of the FPGA boards. To confirm that FPGA the board used as the neurofuzzy controller system operated as expected, a MATLAB script file is used to compare the set of data obtained from the ModelSim program and the set of data obtained from the MATLAB SIMULINK model. The results show that the FPGA board is effective to be used as a neurofuzzy controller for full vehicle nonlinear active suspension systems. The active suspension system has a great performance for vibration isolation. However the main drawback of the active suspension is that it is high energy consumptive. Therefore, to use this suspension system in the proposed model, this drawback should be solved. Electromagnetic actuators are used to convert the vibration energy that arises from the rough road to useful electrical energy to reduce the energy consumption by the active suspension systems. The results show that the electromagnetic devices act as a power generator, i.e. the vibration energy excited by the rough road surface has been converted to a useful electrical energy supply for the actuators. Furthermore, when the nonlinear damper models are replaced by the electromagnetic actuators, riding comfort and the road handling quality are improved. As a result, two targets have been achieved by using hydraulic actuators with electromagnetic suspension systems: increasing fuel economy and improving the vehicle performance