Robust fixed-time flight controller for a dual-system convertible UAV in the cruise mode

This paper investigates the attitude tracking control problem for the cruise mode of a dual-system convertible unmanned aerial vehicle(UAV)in the presence of parameter uncertainties,unmodeled uncertainties and wind disturbances.First,a fixed-time disturbance observer(FXDO)based on the bi-limit homogeneity theory is designed to estimate the lumped disturbance of the convertible UAV model.Then,a fixed-time integral sliding mode control(FXISMC)is combined with the FXDO to achieve strong robustness and chattering reduction.Bi-limit homogeneity theory and Lyapunov theory are applied to provide detailed proof of the fixed-time stability.Finally,numerical simulation experimental results verify the robustness of the proposed algorithm to model parameter uncertainties and wind disturbances.In addition,the proposed algorithm is deployed in a open-source UAV autopilot and its effectiveness is further demonstrated by hardware-in-the-loop experimental results.

Path Tracking Controller Design of Automated Parking Systems via NMPC with an Instructible Solution

Parking difficulties have become a social issue that people have to solve.Automated parking system is practicable for quick par operations without a driver which can also greatly reduces the probability of parking accidents.The paper proposes a Lyapunov-based nonlinear model predictive controller embedding an instructable solution which is generated by the modified rear-wheel feedback method(RF-LNMPC)in order to improve the overall path tracking accuracy in parking conditions.Firstly,A discrete-time RF-LNMPC considering the position and attitude of the parking vehicle is proposed to increase the success rate of automated parking effectively.Secondly,the RF-LNMPC problem with a multi-objective cost function is solved by the Interior-Point Optimization,of which the iterative initial values are described as the instructable solutions calculated by combining modified rear-wheel feedback to improve the performance of local optimal solution.Thirdly,the details on the computation of the terminal constraint and terminal cost for the linear time-varying case is presented.The closed-loop stability is verified via Lyapunov techniques by considering the terminal constraint and terminal cost theoretically.Finally,the proposed RF-LNMPC is implemented on a selfdriving Lincoln MKZ platform and the experiment results have shown improved performance in parallel and vertical parking conditions.The Monte Carlo analysis also demonstrates good stability and repeatability of the proposed method which can be applied in practical use in the near future.

On-Chip Micro Temperature Controllers Based on Freestanding Thermoelectric Nano Films for Low-Power Electronics

Multidimensional integration and multifunctional com-ponent assembly have been greatly explored in recent years to extend Moore’s Law of modern microelectronics.However,this inevitably exac-erbates the inhomogeneity of temperature distribution in microsystems,making precise temperature control for electronic components extremely challenging.Herein,we report an on-chip micro temperature controller including a pair of thermoelectric legs with a total area of 50×50μm^(2),which are fabricated from dense and flat freestanding Bi2Te3-based ther-moelectric nano films deposited on a newly developed nano graphene oxide membrane substrate.Its tunable equivalent thermal resistance is controlled by electrical currents to achieve energy-efficient temperature control for low-power electronics.A large cooling temperature difference of 44.5 K at 380 K is achieved with a power consumption of only 445μW,resulting in an ultrahigh temperature control capability over 100 K mW^(-1).Moreover,an ultra-fast cooling rate exceeding 2000 K s^(-1) and excellent reliability of up to 1 million cycles are observed.Our proposed on-chip temperature controller is expected to enable further miniaturization and multifunctional integration on a single chip for microelectronics.

Laboratory Implementation of Direct Torque Controller based Speed Loop Pseudo Derivative Feedforward Controller for PMSM Drive

This paper,evaluate the effectiveness of a proposed speed loop pseudo derivative feedforward(PDFF)controller-based direct torque controller(DTC)for a PMSM drive against the performance of existing PI speed controller-based DTC and hysteresis current controller(HCC).The proposed PDFF-based speed regulator effectively reduces oscillation and overshoot associated with rotor angular speed,electromagnetic torque,and stator current.Two case studies,one using forward-to-reverse motoring operation and the other involving reverse-to-forward braking operation,has been validated to show the effectiveness of the proposed control strategy.The proposed controller's superior performance is demonstrated through experimental verification utilizing an FPGA controller for a 1.5 kW PMSM drive laboratory prototype.

Adaptive Nonlinear PD Controller of Two-Wheeled Self-Balancing Robot with External Force

This paper proposes an adaptive nonlinear proportional-derivative(ANPD)controller for a two-wheeled self-balancing robot(TWSB)modeled by the Lagrange equation with external forces.The proposed control scheme is designed based on the combination of a nonlinear proportional-derivative(NPD)controller and a genetic algorithm,in which the proportional-derivative(PD)parameters are updated online based on the tracking error and the preset error threshold.In addition,the genetic algorithm is employed to adaptively select initial controller parameters,contributing to system stability and improved control accuracy.The proposed controller is basic in design yet simple to implement.The ANPD controller has the advantage of being computationally lightweight and providing high robustness against external forces.The stability of the closed-loop system is rigorously analyzed and verified using Lyapunov theory,providing theoretical assurance of its robustness.Simulations and experimental results show that the TWSB robot with the proposed ANPD controller achieves quick balance and tracks target values with very small errors,demonstrating the effectiveness and performance of the proposed controller.The proposed ANPD controller demonstrates significant improvements in balancing and tracking performance for two-wheeled self-balancing robots,which has great applicability in the field of robot control systems.This represents a promising solution for applications requiring precise and stable motion control under varying external conditions.

Contact detumbling toward a nutating target through deformable effectors and prescribed performance controller

Detumbling operation toward a rotating target with nutation is meaningful for debris removal but challenging. In this study, a deformable end-effector is first designed based on the requirements for contacting the nutating target. A dual-arm robotic system installed with the deformable end-effectors is modeled and the movement of the end-tips is analyzed. The complex operation of the contact toward a nutating target places strict requirements on control accuracy and controller robustness. Thus, an improvement of the tracking error transformation is proposed and an adaptive sliding mode controller with prescribed performance is designed to guarantee the fast and precise motion of the effector during the contact detumbling.Finally, by employing the proposed effector and the controller,numerical simulations are carried out to verify the effectiveness and efficiency of the contact detumbling toward a nutating target.

Hierarchical Controller Synthesis Under Linear Temporal Logic Specifications Using Dynamic Quantization

Linear temporal logic(LTL)is an intuitive and expressive language to specify complex control tasks,and how to design an efficient control strategy for LTL specification is still a challenge.In this paper,we implement the dynamic quantization technique to propose a novel hierarchical control strategy for nonlinear control systems under LTL specifications.Based on the regions of interest involved in the LTL formula,an accepting path is derived first to provide a high-level solution for the controller synthesis problem.Second,we develop a dynamic quantization based approach to verify the realization of the accepting path.The realization verification results in the necessity of the controller design and a sequence of quantization regions for the controller design.Third,the techniques of dynamic quantization and abstraction-based control are combined together to establish the local-to-global control strategy.Both abstraction construction and controller design are local and dynamic,thereby resulting in the potential reduction of the computational complexity.Since each quantization region can be considered locally and individually,the proposed hierarchical mechanism is more efficient and can solve much larger problems than many existing methods.Finally,the proposed control strategy is illustrated via two examples from the path planning and tracking problems of mobile robots.

Heuristic-Based Optimal Load Frequency Control with Offsite Backup Controllers in Interconnected Microgrids

The primary factor contributing to frequency instability in microgrids is the inherent intermittency of renewable energy sources.This paper introduces novel dual-backup controllers utilizing advanced fractional order proportional integral derivative(FOPID)controllers to enhance frequency and tie-line power stability in microgrids amid increasing renewable energy integration.To improve load frequency control,the proposed controllers are applied to a two-area interconnectedmicrogrid system incorporating diverse energy sources,such as wind turbines,photovoltaic cells,diesel generators,and various storage technologies.A novelmeta-heuristic algorithm is adopted to select the optimal parameters of the proposed controllers.The efficacy of the advanced FOPID controllers is demonstrated through comparative analyses against traditional proportional integral derivative(PID)and FOPID controllers,showcasing superior performance inmanaging systemfluctuations.The optimization algorithm is also evaluated against other artificial intelligent methods for parameter optimization,affirming the proposed solution’s efficiency.The robustness of the intelligent controllers against system uncertainties is further validated under extensive power disturbances,proving their capability to maintain grid stability.The dual-controller configuration ensures redundancy,allowing them to operate as mutual backups,enhancing system reliability.This research underlines the importance of sophisticated control strategies for future-proofing microgrid operations against the backdrop of evolving energy landscapes.

Intelligent Fractional-Order Controller for SMES Systems in Renewable Energy-Based Microgrid

An autonomous microgrid that runs on renewable energy sources is presented in this article.It has a supercon-ducting magnetic energy storage(SMES)device,wind energy-producing devices,and an energy storage battery.However,because such microgrids are nonlinear and the energy they create varies with time,controlling and managing the energy inside them is a difficult issue.Fractional-order proportional integral(FOPI)controller is recommended for the current research to enhance a standalone microgrid’s energy management and performance.The suggested dedicated control for the SMES comprises two loops:the outer loop,which uses the FOPI to regulate the DC-link voltage,and the inner loop,responsible for regulating the SMES current,is constructed using the intelligent FOPI(iFOPI).The FOPI+iFOPI parameters are best developed using the dandelion optimizer(DO)approach to achieve the optimum performance.The suggested FOPI+iFOPI controller’s performance is contrasted with a conventional PI controller for variations in wind speed and microgrid load.The optimal FOPI+iFOPI controller manages the voltage and frequency of the load.The behavior of the microgrid as a reaction to step changes in load and wind speed was measured using the proposed controller.MATLAB simulations were used to evaluate the recommended system’s performance.The results of the simulations showed that throughout all interruptions,the recommended microgrid provided the load with AC power with a constant amplitude and frequency.In addition,the required load demand was accurately reduced.Furthermore,the microgrid functioned incredibly well despite SMES and varying wind speeds.Results obtained under identical conditions were compared with and without the best FOPI+iFOPI controller.When utilizing the optimal FOPI+iFOPI controller with SMES,it was found that the microgrid performed better than the microgrid without SMES.

A Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller Model Combined with an Improved Particle Swarm Optimization Method for Fall Detection

In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible to unsafe events(such as falls)that can have disastrous consequences.However,automatically detecting falls fromvideo data is challenging,and automatic fall detection methods usually require large volumes of training data,which can be difficult to acquire.To address this problem,video kinematic data can be used as training data,thereby avoiding the requirement of creating a large fall data set.This study integrated an improved particle swarm optimization method into a double interactively recurrent fuzzy cerebellar model articulation controller model to develop a costeffective and accurate fall detection system.First,it obtained an optical flow(OF)trajectory diagram from image sequences by using the OF method,and it solved problems related to focal length and object offset by employing the discrete Fourier transform(DFT)algorithm.Second,this study developed the D-IRFCMAC model,which combines spatial and temporal(recurrent)information.Third,it designed an IPSO(Improved Particle Swarm Optimization)algorithm that effectively strengthens the exploratory capabilities of the proposed D-IRFCMAC(Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller)model in the global search space.The proposed approach outperforms existing state-of-the-art methods in terms of action recognition accuracy on the UR-Fall,UP-Fall,and PRECIS HAR data sets.The UCF11 dataset had an average accuracy of 93.13%,whereas the UCF101 dataset had an average accuracy of 92.19%.The UR-Fall dataset had an accuracy of 100%,the UP-Fall dataset had an accuracy of 99.25%,and the PRECIS HAR dataset had an accuracy of 99.07%.

Deep Learning-Based FOPID Controller for Cascaded DC-DC Converters

Smart grids and their technologies transform the traditional electric grids to assure safe,secure,cost-effective,and reliable power transmission.Non-linear phenomena in power systems,such as voltage collapse and oscillatory phenomena,can be investigated by chaos theory.Recently,renewable energy resources,such as wind turbines,and solar photovoltaic(PV)arrays,have been widely used for electric power generation.The design of the controller for the direct Current(DC)converter in a PV system is performed based on the linearized model at an appropriate operating point.However,these operating points are everchanging in a PV system,and the design of the controller is usually accomplished based on a low irradiance level.This study designs a fractional-order proportional-integrated-derivative(FOPID)controller using deep learning(DL)with quasi-oppositional Archimedes Optimization algorithm(FOPID-QOAOA)for cascaded DC-DC converters in micro-grid applications.The presented FOPIDQOAOA model is designed to enhance the overall efficiency of the cascaded DC-DC boost converter.In addition,the proposed model develops a FOPID controller using a stacked sparse autoencoder(SSAE)model to regulate the converter output voltage.To tune the hyper-parameters related to the SSAE model,the QOAOA is derived by the including of the quasi-oppositional based learning(QOBL)with traditional AOA.Moreover,an objective function with the including of the integral of time multiplied by squared error(ITSE)is considered in this study.For validating the efficiency of the FOPID-QOAOA method,a sequence of simulations was performed under distinct aspects.A comparative study on cascaded buck and boost converters is carried out to authenticate the effectiveness and performance of the designed techniques.

基于时变权重模型预测的双向DC-DC优化控制方法

双向DC-DC变换器是电动汽车的关键电压变换部件,提高其动态负载下的响应速度与鲁棒性对电动汽车低压供电系统有重要意义。提出一种基于模糊时变权重的MPC(model predictive control)控制方法。基于状态空间平均法建立变换器动态模型。基于MPC原理搭建变换器MPC模型,实现对电压的跟踪控制;在分析权重对动态负载下电压跟踪性能的影响的基础上,设计权重模糊调节器,实现权重的在线调节。基于HM-cSPACE搭建实验平台验证所提方法的准确性。在Matlab/Simulink环境下与PI控制和传统MPC控制进行对比,结果表明:在负载变化时采用时变权重MPC控制的变换器有更好的动态性能和鲁棒性,超调量降低26.1%。该方法对提高双向DC-DC变换器动态负载下的优化控制具有重要意义。

基于移相控制统一模型的双有源桥DC-DC变换器基波环流优化控制策略

为充分利用双有源桥DC-DC变换器的容量,减小无功功率,提升工作效率,提出一种基于变换器移相控制统一模型的基波环流优化控制策略。通过引入基波移相比对变换器的所有移相控制方式进行统一描述,并采用傅里叶分解法建立全桥交流电压及电感电流的统一模型。该统一模型降低了多控制变量下变换器多模态分析的复杂性,适用于不同移相控制的所有工作模式,具有普适性。基于频域分析法和功率因数角,构建变换器传输功率及无功功率的统一数学模型。在此基础上,提出考虑无功基波分量的环流优化控制策略,并对传导损耗进行建模与分析。该策略简单有效,能够很好地减小变换器无功功率和改善系统效率,更有利于工程实际的应用。搭建了实验平台,对比了所提基波环流优化控制策略与传统移相控制方式下变换器的功率因数及变换效率,仿真和实验结果验证了理论分析的正确性和所提控制策略的可行性。

三有源桥DC-DC变换器广义状态空间平均模型及控制策略研究

基于移相加占空比控制策略的三有源桥TAB(triple active bridge)DC-DC变换器具有效率高和软开关范围可扩展等优点,但其小信号建模过程复杂、闭环控制环路参数整定困难。针对该问题,提出1种TAB工作在移相加占空比控制下的全阶连续广义状态平均建模和PI控制器设计方法。首先,分析TAB的运行原理和Y型等效结构;然后,结合移相加占空比控制的特点和交流方波源等效方法,推导出TAB的广义状态空间平均模型;接着,在推得模型的基础上求得输入到输出的传递函数,设计出PI控制器参数。最后,结合数字仿真及样机实验验证了所提方法的正确性及有效性。

基于准Z源的高增益DC-DC变换器拓扑研究

为了解决传统的DC-DC升压电路电压增益较低,以及光伏发电、风力发电等新能源输出电压低且不稳定,从而影响后级逆变等操作的问题,将准Z源网络与高增益DC-DC拓扑相结合,提出一种基于准Z源的非隔离型高增益DC-DC变换器拓扑;充分利用准Z源网络的高电压增益特性,有效解决DC-DC拓扑在升压方面的不足;为了深入研究变换器的性能,对其在连续导通模式下的拓扑结构、工作原理进行详细分析;同时基于状态空间平均法,建立变换器的小信号模型,并推导其开环传递函数。通过与其他新型拓扑的性能对比,得到该变换器不仅显著提高了电压增益,同时具有较低的开关电压应力。最后搭建仿真及实验平台来验证拓扑理论分析的正确性。

三电平混合全桥DC-DC变换器回流功率优化控制

针对三电平混合全桥双向DC-DC变换器存在的回流功率较大的问题,本文以回流功率最小化为目标,提出了一种基于双重移相控制的回流功率优化控制策略。首先,建立了三电平混合全桥DC-DC变换器的数学模型,分析了变换器回流功率和传输功率的特性关系;然后,以回流功率作为目标函数,通过KKT条件法将不同模式的等式和不等式约束条件转换为约束函数,并构建拉格朗日多项式函数求最佳移相比组合;最后,为提高系统动态响应的快速性,采用了环内直接功率控制方法,实现了变换器在面对突发情况时系统能迅速实现动态响应。仿真结果表明,在输入电压和负载突变时,本文提出的改进优化控制策略可以有效减小回流功率,提高系统的动态响应性能。

基于改进LSTM-SVM的双向DC-DC电力变换器故障诊断

目的为了解决双向DC-DC电力变换器的软故障诊断精度不高的问题,方法提出基于改进LSTM-SVM的双向DC-DC电力变换器故障诊断模型。首先,分析双向DC-DC电力变换器中电容、电感和MOSFET管的故障机理,通过仿真实验模拟各元件失效后变换器的输出电气参数变化,从而确定变换器不同元件故障时对应的故障特征参数;其次,构建改进的LSTM-SVM双向DC-DC电力变换器故障诊断组合模型,在LSTM中添加Mogrifier门机制,提高LSTM提取时间序列原始数据中微弱特征的能力;最后,由于传统LSTM的末端分类器为Softmax,其主要解决单一元件诊断问题,变换器故障类型较多,维数较高,所以采用麻雀搜索算法优化的SVM代替原有的Softmax函数,对LSTM输出的数据进行故障分类,提高故障诊断的准确率。设置双向DC-DC电力变换器充放电两种状态下,包含电解电容、电感和MOSFET单双管故障在内的24组故障,分别采用本文构建的改进的LSTM-SVM和原始的LSTM-SVM双向DC-DC变换器故障诊断模型进行诊断。结果结果表明,改进的LSTM-SVM故障诊断模型诊断准确率平均值为99.71%,原始的LSTM-SVM故障诊断模型诊断准确率平均值为88.48%,改进的LSTM-SVM故障诊断模型对各元件的故障诊断正确率均高于原始的LSTM-SVM故障诊断模型的。结论基于改进LSTM-SVM的双向DC-DC电力变换器故障诊断模型实现了对双向DC-DC电力变换器中的电解电容、电感和MOSFET单双管故障的准确诊断。

一种新型准Z源高增益DC-DC升压变换器

针对传统准Z源阻抗网络DC-DC升压变换器存在电压增益低和元件电压应力高的问题,提出一种新型准Z源高增益DC-DC升压变换器(HSQZSC)。首先,分析HSQZSC变换器两种模态工作原理,并对其进行稳态分析和参数设计。其次,与现已提出的Z源变换器进行性能对比,表明HSQZSC变换器具有高电压增益和元件电压应力低的优势。然后,对其建立状态空间模型进行小信号分析、稳定性分析和控制器的设计。最后,搭建100 W实物样机,实验结果验证了HSQZSC变换器的可行性与有效性。

基于SEPIC的新型高增益DC-DC变换器及衍生拓扑分析

针对光伏并网系统中单开关升压变换器(如SEPIC、Boost、准Z源、二次型)电压增益低和半导体器件电压应力大的问题,提出1种基于SEPIC的外部升压型高增益DC-DC变换器。首先对所提变换器在电感电流连续导通模式和断续导通模式下的工作原理和稳态特性进行详细分析,并与其他新型拓扑进行性能对比。在此基础上,给出了基于外部升压网络的衍生拓扑。最后,通过搭建1台400 V/200 W的实验样机证明了理论分析的准确性。所提变换器利用阻抗网络和开关电容单元构成的外部升压网络提升了SEPIC变换器的电压增益,并改善了半导体器件电压应力大的缺点。

集成有源开关电感和Y源倍压单元的高增益DC-DC变换器

针对光伏发电系统中光伏组件电压等级的提升,提出一种集成有源开关电感和Y源倍压单元的高增益DC-DC变换器。所提变换器能够实现低开关管电压应力,具有高自由度电压调节能力,在满足一定升压条件下仍能保证电路的效率,在光伏发电中具有较大的应用潜力。对变换器的工作模态、器件电压电流应力、参数设计和损耗效率进行详细阐述。最后通过实验验证所提拓扑结构的可行性。