Model-Free Adaptive Frequency Calibration for Voltage-Controlled Oscillators

The operating frequency accuracy of the local oscillators is critical for the overall system performance in the communication systems.However,the high-precision oscillators could be too expensive for civil applications.In this paper,we propose a model-free adaptive frequency calibration framework for a voltage-controlled crystal oscillator(VCO)equipped with a time to digital converter(TDC),which can significantly improve the frequency accuracy of the VCO thus calibrated.The idea is to utilize a high-precision TDC to directly measure the VCO period which is then passed to a model-free method for working frequency calibration.One advantage of this method is that the working frequency calibration employs the system history of input/output(I/O)data,instead of establishing an accurate VCO voltagecontrolled oscillator model.Another advantage is the lightweight calibration method with low complexity such that it can be implemented on an MCU with limited computation capabilities.Experimental results show that the proposed calibration method can improve the frequency accuracy of a VCO from±20 ppm to±10 ppb,which indicates the promise of the modelfree adaptive frequency calibrator for VCOs.

Event-Triggered Asymmetric Bipartite Consensus Tracking for Nonlinear Multi-Agent Systems Based on Model-Free Adaptive Control

In this paper,an asymmetric bipartite consensus problem for the nonlinear multi-agent systems with cooperative and antagonistic interactions is studied under the event-triggered mechanism.For the agents described by a structurally balanced signed digraph,the asymmetric bipartite consensus objective is firstly defined,assigning the agents'output to different signs and module values.Considering with the completely unknown dynamics of the agents,a novel event-triggered model-free adaptive bipartite control protocol is designed based on the agents'triggered outputs and an equivalent compact form data model.By utilizing the Lyapunov analysis method,the threshold of the triggering condition is obtained.Subsequently,the asymptotic convergence of the tracking error is deduced and a sufficient condition is obtained based on the contraction mapping principle.Finally,the simulation example further demonstrates the effectiveness of the protocol.

Event-triggered model-free adaptive control for a class of surface vessels with time-delay and external disturbance via state observer

This paper provides an improved model-free adaptive control(IMFAC)strategy for solving the surface vessel trajectory tracking issue with time delay and restricted disturbance.Firstly,the original nonlinear time-delay system is transformed into a structure consisting of an unknown residual term and a parameter term with control inputs using a local compact form dynamic linearization(local-CFDL).To take advantage of the resulting structure,use a discrete-time extended state observer(DESO)to estimate the unknown residual factor.Then,according to the study,the inclusion of a time delay has no effect on the linearization structure,and an improved control approach is provided,in which DESO is used to adjust for uncertainties.Furthermore,a DESO-based event-triggered model-free adaptive control(ET-DESO-MFAC)is established by designing event-triggered conditions to assure Lyapunov stability.Only when the system’s indicator fulfills the provided event-triggered condition will the control input signal be updated;otherwise,the control input will stay the same as it is at the last trigger moment.A coordinate compensation approach is developed to reduce the steady-state inaccuracy of trajectory tracking.Finally,simulation experiments are used to assess the effectiveness of the proposed technique for trajectory tracking.

A Fractional-Order Ultra-Local Model-Based Adaptive Neural Network Sliding Mode Control of n-DOF Upper-Limb Exoskeleton With Input Deadzone

This paper proposes an adaptive neural network sliding mode control based on fractional-order ultra-local model for n-DOF upper-limb exoskeleton in presence of uncertainties,external disturbances and input deadzone.Considering the model complexity and input deadzone,a fractional-order ultra-local model is proposed to formulate the original dynamic system for simple controller design.Firstly,the control gain of ultra-local model is considered as a constant.The fractional-order sliding mode technique is designed to stabilize the closed-loop system,while fractional-order time-delay estimation is combined with neural network to estimate the lumped disturbance.Correspondingly,a fractional-order ultra-local model-based neural network sliding mode controller(FO-NNSMC) is proposed.Secondly,to avoid disadvantageous effect of improper gain selection on the control performance,the control gain of ultra-local model is considered as an unknown parameter.Then,the Nussbaum technique is introduced into the FO-NNSMC to deal with the stability problem with unknown gain.Correspondingly,a fractional-order ultra-local model-based adaptive neural network sliding mode controller(FO-ANNSMC) is proposed.Moreover,the stability analysis of the closed-loop system with the proposed method is presented by using the Lyapunov theory.Finally,with the co-simulations on virtual prototype of 7-DOF iReHave upper-limb exoskeleton and experiments on 2-DOF upper-limb exoskeleton,the obtained compared results illustrate the effectiveness and superiority of the proposed method.

Model-free adaptive control of space manipulator under different gravity environment

Due to the release of gravity in the space environment, the dynamic characteristics of the space manipulator have changed compared with that of the ground, which results in the change of its tracking precision. This paper presents a model-free adaptive control(MFAC) strategy to track the desired trajectory under different gravity environment. A dynamic transformation method and full form dynamic linearization(FFDL) approach are selected to dynamicly linearize the system, which can better eliminate the complex dynamics that may exist in the original system. The controlled object uses the two degrees of freedom of space manipulator and the controller only depends on the desired angle and torque of each joint of the space manipulator. Moreover, the proof of stability is also provided. Finally, simulation results are presented to demonstrate the effectiveness of the proposed strategy. It is shown that the proposed approach can achieve better trajectory tracking performance under different gravity environment without changing the control parameters, and the tracking precision can be significantly improved as compared with the proportional differential(PD) control results.

Model-free Adaptive Control for Spacecraft Attitude

A model-free adaptive control method is proposed for the spacecrafts whose dynamical parameters change over time and cannot be acquired accurately. The algorithm is based on full form dynamic linearization.A dimension reduction matrix is introduced to construct an augmented system with the same dimension input and output. The design of the controller depends on the system input and output data rather than the knowledge of the controlled plant. The numerical simulation results show that the improved controller can deal with different models with the same set of controller parameters,and the controller performance is better than that of PD controller for the time-varying system with disturbance.

Model-free adaptive robust control method for high-speed trains

Aiming at the robustness issue in high-speed trains(HSTs)operation control,this article proposes a model-free adaptive control(MFAC)scheme to suppress disturbance.Firstly,the dynamic linearization data model of train system under the action of measurement disturbance is given,and the Kalman filter(KF)based on this model is derived under the minimum variance estimation criterion.Then,according to the KF,an anti-interference MFAC scheme is designed.This scheme only needs the input and output data of the controlled system to realize the MFAC of the train under strong disturbance.Finally,the simulation experiment of CRH380A HSTs is carried out and compared with the traditional MFAC and the MFAC with attenuation factor.The proposed control algorithm can effectively suppress the measurement disturbance,and obtain smaller tracking error and larger signal to noise ratio with better applicability.

New adaptive quasi-sliding mode control for nonlinear discrete-time systems

A new adaptive quasi-sliding mode control algorithm is developed for a class of nonlinear discrete-time systems, which is especially useful for nonlinear systems with vaguely known dynamics. This design is model-free, and is based directly on pseudo-partial-derivatives derived on-line from the input and output information of the system using an improved recursive projection type of identification algorithm. The theoretical analysis and simulation results show that the adaptive quasi-sliding mode control system is stable and convergent.

基于改进MFAC的交直流微电网分布式二次控制

针对现有交直流微电网分布式二次控制(distributed secondarycontrol,DSC)受分布式电源一次控制参数影响较大、通信拓扑结构复杂、二次控制参数难以整定等问题,提出基于改进MFAC的交直流微电网DSC(MFAC-DSC),利用分布式电源的历史采样数据在线估计其动态线性化参数,解决了传统方法受一次控制参数影响较大的问题;通过优化DSC的通信结构和控制策略,避免了互联变流器参与二次控制通信,解决了互联变流器通信失效影响DSC的问题;推导了MFAC-DSC系统收敛性,给出了稳定收敛条件。基于交直流微电网实验平台在多种工况下验证了MFAC-DSC的可行性,与传统的DSC策略的对比结果表明:MFAC-DSC方法有效减少分布式电源下垂系数变化带来的功率波动,保证下垂系数有更大的调节范围,同时解决了互联变流器通信失效造成的控制失效和母线电压偏移等问题。

Fuzzy Model Free Adaptive Control for Rotor Blade Full-Scale Static Testing

To eliminate the node traction coupling during wind turbine blade full-scale static testing,a model free adaptive control algorithm is presented based on fuzzy control performance function compensation. Based on the universal model theory,the fuzzy model free adaptive control( FMFAC) algorithm is designed by configuring the spot static testing experiences as compensation function F( ·). Then the algorithm implementation process is provided and its quick convergence is proved. Using software to establish static load coupling model of multi-nodes,simulate and verify the validity of FMFAC algorithm,which is applied to wind turbines blade full-scale static testing. The results show that the adaptive decoupling ability of FMFAC is better. The traction of four load points can stay steady and change coordinately. Process error is not over ± 6 k N. The error rate is lower than 1% in special phase.This algorithm effectively eliminates the traction coupling of the static testing process,and makes wind turbine blade testing steadily.

基于MFAC附加阻尼控制器的次同步振荡抑制方法

为解决当前次同步振荡抑制措施中常用的传统附加阻尼控制器存在建模精度不足、适应性差等问题,本文引入了一种基于无模型自适应控制的附加阻尼控制器。该控制器采用偏格式或全格式动态线性化数据模型,通过系统I/O数据估算系统伪梯度,由预测器输出附加阻尼控制信号,作用在双馈风机转子侧变流器的q轴参考电压信号中,并提出了基于粒子群算法的控制器参数优化设计方法。仿真验证了所提控制器具有较强的适应性,在线路串补度、风速等工况条件改变时能维持良好的次同步振荡抑制效果,在所有测试工况下均优于传统附加阻尼控制器和采用紧格式动态线性化的无模型自适应控制器。

On disturbance rejection proportional-integral-differential control with model-free adaptation

In this paper,an adaptive disturbance-rejection proportional–integral–differential(PID)control method is proposed for a class of nonlinear systems.First,PID-type criterion is introduced in a model-free adaptive control(MFAC)framework,which gives an optimal control interpretation for PID controller.Then,the design of adaptive disturbance rejection PID is proposed based on this new interpretation to realize controller gain auto-tuning.Due to the ingenious integration of active disturbance rejection and adaptive mechanism,the proposed adaptive disturbance rejection PID control scheme exhibits better control performance than MFAC case.Furthermore,the boundedness of controller gain,the convergence of tracking error and the bounded-input–bounded-output stability are proved for the proposed control system.Finally,the effectiveness of the proposed method is verified by numerical simulation.

基于LabVIEW的无模型自适应控制算法实现与应用

为了有效地将无模型自适应控制算法(MFAC)应用于实际场景,该文研究LabVIEW开发环境中实现基于紧格式动态线性化的无模型自适应控制(MFAC-CFDL)。实验结果表明,对于相同的被控对象,LabVIEW实现与Matlab的仿真运行结果一致,证明该方法准确性。进一步与传统的PID控制方法相比较,MFAC-CFDL显示出更强的鲁棒性,并能保证闭环系统的稳定性以及输出误差跟踪的收敛性,通过实验说明该方法工程实践应用有效性和可行性。

Model-free adaptive control for three-degree-of-freedom hybrid magnetic bearings

Mathematical models are disappointing due to uneven distribution of the air gap magnetic field and significant un- modeled dynamics in magnetic bearing systems. The effectiveness of control deteriorates based on an inaccurate mathematical model, creating slow response speed and high jitter. To solve these problems, a model-free adaptive control （MFAC） scheme is proposed for a three-degree-of-freedom hybrid magnetic bearing （3-DoF HMB） control system. The scheme for 3-DoF HMB depends only on the control current and the objective balanced position, and it does not involve any model information. The design process of a parameter estimation algorithm is model-free, based directly on pseudo-partial-derivative （PPD） derived online from the input and output data information. The rotor start-of-suspension position of the HMB is regulated by auxiliary bearings with different inner diameters, and two kinds of operation situations （linear and nonlinear areas） are present to analyze the validity of MFAC in detail. Both simulations and experiments demonstrate that the proposed MFAC scheme handles the 3-DoF HMB control system with start-of-suspension response speed, smaller steady state error, and higher stability.

Composite Model-free Adaptive Predictive Control for Wind Power Generation Based on Full Wind Speed

Aiming at the problem that the existing model-based control strategy cannot fully reflect stochastic fluctuations of wind power,this paper presents a model-free adaptive predictive controller(MFAPC)for variable pitch systems with speed disturbance suppression.First,an improved small-world neural network with topology optimization is used for 15-second-ahead forecasting of wind speed,whose rolling time is 1s,and the predicted value serves as a feedforward to obtain the early compensation variation of the pitch angle.Second,a function of the multi-objective optimization at full wind speed with optimal power point tracking and minimum control variation is constructed,and an advanced one-step adaptive predictive control algorithm for wind power is proposed based on the online estimation and prediction of the time-varying pseudo partial derivative(PPD).In addition,the compound MFAPC framework is synthetically obtained,whose closed-loop effectiveness is verified by a BP-built pitch system based on the SCADA data with all working conditions.Robustness of the schemes has been analyzed in terms of parametric uncertainties and different operating conditions,and a detailed comparison is finally presented.The results show that the proposed MFAPC can not only effectively suppress the random disturbance of wind speed,but also meet the stability of wind power and the security of grid-connections for all operating conditions.

PSO Optimal Control of Model-free Adaptive Control for PVC Polymerization Process

Polyvinyl chloride （PVC） polymerizing process is a typical complicated industrial process with the characteristics of large inertia, big time delay and nonlinearity. Firstly, for the general nonlinear and discrete time system, a design scheme of model-free adaptive （MFA） controller is given. Then, particle swarm optimization （PSO） algorithm is applied to optimizing and setting the key parameters for controller tuning. After that, the MFA controller is used to control the system of polymerizing temperature. Finally, simulation results are given to show that the MAC strategy based on PSO obtains a good controlling performance index.

Data Driven Model-Free Adaptive Control Method for Quadrotor Trajectory Tracking Based on Improved Sliding Mode Algorithm

In order to solve the problems of dynamic modeling and complicated parameters identification of trajectory tracking control of the quadrotor,a data driven model-free adaptive control method based on the improved sliding mode control(ISMC)algorithm is designed,which does not depend on the precise dynamic model of the quadrotor.The design of the general sliding mode control(SMC)algorithm depends on the mathematical model of the quadrotor and has chattering problems.In this paper,according to the dynamic characteristics of the quadrotor,an adaptive update law is introduced and a saturation function is used to improve the SMC.The proposed control strategy has an inner and an outer loop control structures.The outer loop position control provides the required reference attitude angle for the inner loop.The inner loop attitude control ensures rapid convergence of the attitude angle.The effectiveness and feasibility of the algorithm are verified by mathematical simulation.The mathematical simulation results show that the designed model-free adaptive control method of the quadrotor is effective,and it can effectively realize the trajectory tracking control of the quadrotor.The design of the controller does not depend on the kinematic and dynamic models of the unmanned aerial vehicle(UAV),and has high control accuracy,stability,and robustness.

猪舍环境的多参数无模型自适应控制算法设计

【目的】针对猪舍环境控制中场景复杂、多参数控制难以及系统呈现耦合特性的问题,基于无模型自适应控制(Model-free adaptive control,MFAC)算法,设计了适用于猪舍的多参数无模型自适应控制(Multi-parameter model-free adaptive control,MMFAC)算法。【方法】通过将MFAC算法与用于尺度伸缩变换的权值矩阵相融合,设计MMFAC算法。该算法利用紧格式动态线性化(Compact form dynamic linearization,CFDL)技术和最优化数学方法,进行伪雅可比矩阵(Pseudo-jacobian matrix,PJM)的参数辨识和调控装置的控制量计算。【结果】仿真试验结果表明,MMFAC算法具备对多参数进行自适应控制的能力,并可通过调整权重矩阵来降低关键参数的误差。真实猪舍环境控制试验结果表明,该算法可根据实时环境参数测量值计算风机控制量,并根据该控制量驱动风机,以保持环境参数在舒适区间范围内的稳定控制。【结论】MMFAC算法展现出良好的多参数自适应控制能力,能够适应复杂的猪舍环境,有效解决多参数控制难以及系统耦合的问题,在猪舍环境控制中具有较好的应用前景。

Event-triggered distributed optimization for model-free multi-agent systems

In this paper,the distributed optimization problem is investigated for a class of general nonlinear model-free multi-agent systems.The dynamical model of each agent is unknown and only the input/output data are available.A model-free adaptive control method is employed,by which the original unknown nonlinear system is equivalently converted into a dynamic linearized model.An event-triggered consensus scheme is developed to guarantee that the consensus error of the outputs of all agents is convergent.Then,by means of the distributed gradient descent method,a novel event-triggered model-free adaptive distributed optimization algorithm is put forward.Sufficient conditions are established to ensure the consensus and optimality of the addressed system.Finally,simulation results are provided to validate the effectiveness of the proposed approach.