Adaptive Control Based on Neural Networks for an Uncertain 2-DOF Helicopter System With Input Deadzone and Output Constraints

In this paper, a study of control for an uncertain2-degree of freedom(DOF) helicopter system is given. The2-DOF helicopter is subject to input deadzone and output constraints. In order to cope with system uncertainties and input deadzone, the neural network technique is introduced because of its capability in approximation. In order to update the weights of the neural network, an adaptive control method is utilized to improve the system adaptability. Furthermore, the integral barrier Lyapunov function(IBLF) is adopt in control design to guarantee the condition of output constraints and boundedness of the corresponding tracking errors. The Lyapunov direct method is applied in the control design to analyze system stability and convergence. Finally, numerical simulations are conducted to prove the feasibility and effectiveness of the proposed control based on the model of Quanser's 2-DOF helicopter.

Adaptive controller design based on input-output signal selection for voltage source converter high voltage direct current systems to improve power system stability

An input-output signal selection based on Phillips-Heffron model of a parallel high voltage alternative current/high voltage direct current(HVAC/HVDC) power system is presented to study power system stability. It is well known that appropriate coupling of inputs-outputs signals in the multivariable HVDC-HVAC system can improve the performance of designed supplemetary controller. In this work, different analysis techniques are used to measure controllability and observability of electromechanical oscillation mode. Also inputs–outputs interactions are considered and suggestions are drawn to select the best signal pair through the system inputs-outputs. In addition, a supplementary online adaptive controller for nonlinear HVDC to damp low frequency oscillations in a weakly connected system is proposed. The results obtained using MATLAB software show that the best output-input for damping controller design is rotor speed deviation as out put and phase angle of rectifier as in put. Also response of system equipped with adaptive damping controller based on HVDC system has appropriate performance when it is faced with faults and disturbance.

An on-line data acquisition system of oscilloscope-embedded input/output controller for cavity BPM measurement

Cavity beam position monitor(BPM) is widely used in a precise electron beam position measurement. Based on high performance oscilloscope-embedded EPICS input/output controller,we developed an on-line cavity BPM signal processing system for fast data acquisition solution when designing a cavity BPM.Also,methods for extracting the position information from cavity pickup signals and calibration algorithm are included in this solution.

Tool Condition Monitoring Based on Nonlinear Output Frequency Response Functions and Multivariate Control Chart

Tool condition monitoring(TCM)is a key technology for intelligent manufacturing.The objective is to monitor the tool operation status and detect tool breakage so that the tool can be changed in time to avoid significant damage to workpieces and reduce manufacturing costs.Recently,an innovative TCM approach based on sensor data modelling and model frequency analysis has been proposed.Different from traditional signal feature-based monitoring,the data from sensors are utilized to build a dynamic process model.Then,the nonlinear output frequency response functions,a concept which extends the linear system frequency response function to the nonlinear case,over the frequency range of the tooth passing frequency of the machining process are extracted to reveal tool health conditions.In order to extend the novel sensor data modelling and model frequency analysis to unsupervised condition monitoring of cutting tools,in the present study,a multivariate control chart is proposed for TCM based on the frequency domain properties of machining processes derived from the innovative sensor data modelling and model frequency analysis.The feature dimension is reduced by principal component analysis first.Then the moving average strategy is exploited to generate monitoring variables and overcome the effects of noises.The milling experiments of titanium alloys are conducted to verify the effectiveness of the proposed approach in detecting excessive flank wear of solid carbide end mills.The results demonstrate the advantages of the new approach over conventional TCM techniques and its potential in industrial applications.

Controlling Chaotic Cscillations with Input-Output Linearization

In this paper, the nonlinear control of chaotic oscillations was investigated by using the input output linearization control method. The chaotic attitude of a kind of spacecraft was discussed. It is demonstrated that the input output linearization control law is the nonlinear version of the parametric open plus closed loop control law.

Discrete Sliding Mode Control of an Input-output System with Stochastic Disturbance

这份报纸论述输入产量的分离适应滑动模式控制非最小的阶段系统面对随机的骚乱。非最小的阶段系统能被一个操作员转变成一个最小的阶段系统。根据最小的阶段系统，控制器和我们设计了的适应算法保证系统的稳定性并且认为从滑动表面的吝啬平方的偏差被最小化。

Application of Linear Model Predictive Control and Input-Output Linearization to Constrained Control of 3D Cable Robots

Cable robots are structurally the same as parallel robots but with the basic difference that cables can only pull the platform and cannot push it. This feature makes control of cable robots a lot more challenging compared to parallel robots. This paper introduces a controller for cable robots under force constraint. The controller is based on input-output linearization and linear model predictive control. Performance of input-output linearizing (IOL) controllers suffers due to constraints on input and output variables. This problem is successfully tackled by augmenting IOL controllers with linear model predictive controller (LMPC). The effecttiveness of the proposed method is illustrated by numerical simulation.

ADRC based control for a class of input time delay systems

This paper is concerned with the control design and the theoretical analysis for a class of input time-delay systems with stable, critical stable or unstable poles. In order to overcome the time delay, a novel feed-forward compensation active disturbance rejection control(FFC-ADRC) approach is proposed. It combines advantages of the Smith predictor and the traditional active disturbance rejection control(ADRC). The tracking differentiator(TD) is designed to predict the control signal, which adds an anticipatory control to the control signal and allows a higher observer bandwidth to obtain better disturbance rejection. The modified extended state observer(ESO) is designed to estimate both system states and the total disturbances(internal disturbance, uncertainties and delayed disturbance). Then the Lyapunov theory and the theory of the input-output stability are applied to prove the asymptotic stability of the closed-loop control system. Finally, numerical simulations show the effectiveness and practicality of the proposed design.

A multi-input and multi-output design on automotive engine management system

Lookup table is widely used in automotive industry for the design of engine control units(ECU).Together with a proportional-integral controller,a feed-forward and feedback control scheme is often adopted for automotive engine management system(EMS).Usually,an ECU has a structure of multi-input and single-output(MISO).Therefore,if there are multiple objectives proposed in EMS,there would be corresponding numbers of ECUs that need to be designed.In this situation,huge efforts and time were spent on calibration.In this work,a multi-input and multi-out(MIMO) approach based on model predictive control(MPC) was presented for the automatic cruise system of automotive engine.The results show that the tracking of engine speed command and the regulation of air/fuel ratio(AFR) can be achieved simultaneously under the new scheme.The mean absolute error(MAE) for engine speed control is 0.037,and the MAE for air fuel ratio is 0.069.

考虑输出约束和输入饱和的弹仓自适应控制

针对存在参数不确定性和外部扰动的某链式回转弹仓的位置跟踪控制问题,提出一种自适应控制方案。该方案基于神经模糊系统(Neuro-Fuzzy System,NFS)、非线性扰动观测器(Nonlinear Disturbance Observer,NDO)及障碍Lyapunov函数(Barrier Lyapunov Function,BLF),在实现高精度控制的同时满足输出约束和输入饱和的工程条件。通过BLF的设计,保证弹仓的位移跟踪误差约束在给定范围内。结合神经网络的函数逼近能力和模糊逻辑系统的推理能力,用于估计系统中的不确定性,减小对模型的依赖;两者结合的NFS作为NDO的一部分,进一步补偿估计误差和外部扰动,提高控制性能。此外,控制器设计考虑了运动过程中的执行器输入饱和问题。仿真结果表明,所设计的控制器在3种典型工况以及系统参数变化情况下均可实现弹仓的高精度位置跟踪控制,满足系统的约束条件。

鲁棒参数自适应微振动控制算法

递推最小二乘(Recursive Least Squares,RLS)算法因其简单、快速的特点,在微振动自适应控制领域被广泛应用。由于微振动主动控制系统中扰动环境的特殊性及复杂性,需要重点考虑微振动控制中所采用的参数自适应算法在参数估计过程中的鲁棒性。针对多输入多输出(Multiple Input Multiple Output,MIMO)微振动主动控制系统,基于无限冲激响应(Infinite Impulse Response,IIR)滤波器,提出一种结合死区和归一化的MIMO鲁棒参数自适应算法,并给出其详细的算法推导与收敛性分析。在此基础上,通过构建三自由度微振动主动振动控制实验系统,针对单频窄带扰动、双频窄带扰动展开了对比实验分析,相关的实验结果验证了所提出鲁棒参数自适应算法的可行性和鲁棒性。

基于改进下垂控制的多模块谐振变换器均衡控制策略研究

当前,光伏、海上风电等新能源场站的直流汇流应用得到越来越多的关注,在新能源发电低压输入中高压汇流输出的场合,为了提升直流输电电压并进一步减小器件应力,多采用输入并联输出串联(IPOS)结构。虽然这种模块化串并联结构有着许多优势,但由于模块间器件参数误差,导致各模块存在电压电流不均衡的问题,影响整个功率变换系统的稳定运行。为此提出了面向直流汇流应用的IPOS多模块谐振变换器的改进下垂控制均衡方法,各模块间无需加入通信就实现各模块的输出电压和功率的完全均衡,改进了传统下垂控制电压与参考值不符的问题,并进一步提升了系统的动态响应性能。基于该控制方式,搭建了三路模块化IPOS谐振变换器原理样机,通过实验验证了所提改进下垂控制方法对IPOS系统的均衡效果。

带有双球面摆和变绳长效应的桥式起重机轨迹规划

带有双球面摆和变绳长效应的桥式起重机具有多输入多输出以及欠驱动的动力学特性,目前仍缺乏有效的控制策略.在台车移动、桥架移动、负载升降同步作业过程中,吊钩和负载两级球面摆动特性更为复杂,各状态量之间的非线性耦合关系更强,桥式起重机的防摆控制更具挑战性.不仅如此,现有方法无法保证桥式起重机系统全状态量的暂态控制性能.为解决上述问题,提出一种基于多项式的优化轨迹规划方法.首先,在未进行近似简化的前提下,使用拉格朗日方法建立带有双球面摆和变绳长效应的7自由度(Seven degree-of-freedom,7-DOF)桥式起重机的精确动力学模型.在此基础上,构造一组包含各状态量的辅助信号,将施加在台车、桥架、绳长以及吊钩、负载摆动上的约束转化为对辅助信号的约束,从而将桥式起重机的轨迹规划问题转化为与辅助信号相关的时间优化问题,并使用二分法求解.该轨迹规划方法不仅缩短了吊运时间,而且确保了全状态量满足约束条件.最后,仿真结果证明了动力学模型的准确性和轨迹规划方法的有效性.

ErlangShen:基于流水线低访问开销的图式区块链高效事务执行机制

基于有向无环图(DAG)的图式区块链能够显著提升系统性能,已成为近年来业界的研究热点。相较于传统串行化的链式区块链,图式区块链可在单位时间内并发处理大量事务从而提升吞吐量。随着事务量的激增,图式区块链面临事务执行效率低的瓶颈问题,即海量事务执行对状态数据访问的需求大幅增加,导致高昂的输入/输出(I/O)开销。实现低I/O访问主要包括两方面的全新挑战:一方面,图式区块链若直接采用传统的事务预取机制,将因执行逻辑不一致引入大量的陈旧读;另一方面,针对不同账户的状态访问会在默克尔树的高层节点中造成重复的I/O开销。为此,本文设计基于流水线的图式区块链高效事务执行机制—ErlangShen,包括Epoch粒度的状态预取机制和默克尔高层路径缓存机制来分别减少陈旧读的数量和重复的I/O开销。具体而言,ErlangShen充分分析并利用了事务访问频次的冷热特征,将访问热事务的逻辑执行与冷事务的状态预取并行化,以避免状态预取对事务执行的影响。此外,为了进一步提升事务执行的吞吐量,根据访问冷热状态事务的特性设计了定制化的并发控制方法。实验结果表明,ErlangShen机制能够减少约90%的陈旧读数量,与最新图式区块链事务处理机制Nezha相比,可将性能提升3~4倍。

基于多智能体深度强化学习的解耦控制方法

[目的]在现代工业生产过程中,实现复杂非线性多输入多输出系统的解耦控制对于生产过程的操作和优化都具有至关重要的意义.[方法]本文基于多智能体深度确定性策略梯度(MADDPG)算法,提出了一种解决复杂非线性多输入多输出系统解耦控制问题的设计方案,并通过连续搅拌反应过程的解耦控制仿真计算,验证了设计方案的有效性.[结果]验证结果表明:本文所提出的方案能够同时对连续搅拌反应过程中反应温度、产物摩尔流量两个被控量的设定控制目标进行跟踪调节,且在同样的控制目标下,该设计方案比单智能体方案和PID(proportional-integral-derivative control)控制方案都具有更好的稳定性与更小的稳态控制误差.[结论]仿真结果表明:针对复杂非线性多输入多输出系统的解耦控制问题,多智能体强化学习算法能够在不依赖于过程模型的基础上,实现复杂非线性多输入多输出系统的解耦控制,并保证较好的控制性能.

低冗余高可靠模块化输入并联输出串联电源系统及其控制方法

为了使输入并联输出串联电源系统同时具备模块级和系统级冗余能力,提出低冗余高可靠超宽电压范围电路结构及其控制方法。基于两级式电路和多模块串并联实现电源系统超宽电压输出,通过模块级部分子电路冗余设计,实现模块级和系统级N+X双重冗余,使得功率模块部分失效或多个模块全部失效时系统仍具有全电压、全功率输出能力。进一步提出模块化电源系统的分布-集中混合式控制策略,分布式自主控制保证了模块间应力均衡、并使得输出电压在目标值范围内,集中控制确保总输出电压精度。该文详细分析所提电路结构及其控制策略的原理、特性和实现方案,并通过0~1000 V/50 kW输出电源系统实验,验证了所提方案的有效性。

基于逆系统解耦的三电平Buck变换器反步滑模控制

多电平变换器因其可以降低开关管承受的电压应力、减小滤波电感和滤波电容体积,被广泛应用在直流微电网中。三电平Buck变换器的飞跨电容电压和输出电压存在耦合,是一个多输入多输出、强耦合的非线性系统。针对这一问题,提出一种逆系统解耦反步滑模控制方法。采用逆系统方法实现输出电压控制和飞跨电容电压控制的解耦,采用反步滑模法控制保证输出电压的稳定性和鲁棒性,通过状态反馈控制将飞跨电容电压平衡在输入电压的1/2处。仿真和实验结果表明,所提控制策略能使得飞跨电容电压和输出电压均具有良好的稳态和动态特性。

基于输入输出线性化的三电平Boost PFC控制策略

针对传统双闭环控制动态调节时间过长的问题,提出了一种基于输入输出线性化的三电平Boost PFC变换器控制策略。首先,分析了三电平Boost PFC变换器的工作模态,建立了状态空间方程;然后,推导出三电平Boost PFC变换器输入输出非线性模型,判断线性化的可行性,进而对系统进行线性化;最后,利用经典控制理论设计控制器,并增加电压平衡控制,保证变换器的中点电位平衡。仿真结果表明:负载恒定时,输入电流可跟随输入电压相位变化并保持正弦波形,电流波形更为平滑;负载跳变时,电压电流调节迅速,输出电压除纹波稍有变化外,电压平均值保持恒定。证明了所提控制策略具备良好的动、静态特性及强鲁棒性。