Establishing improved convergence properties for the adaptive learning control

The adaptive learning approach proposed in Del Vecchio, Marino, and Tomei (2003) and Liuzzo, Marino, and Tomei (2007a), which guarantees output tracking of sufficiently smooth periodic reference signals (of known period) for classes of time-invariant nonlinear systems with sufficiently smooth unstructured uncertainties, is considered. By using a slightly different analysis (and in particular less conservative bounds), we show how improved convergence properties can be established while maintaining the same learning control structures. The delayed first arithmetic mean of the Fourier series of the uncertain periodic time function f (of known period) can be used instead of the partial sums of the Fourier series of f to further improve the result: a trigonometric polynomial, whose approximation to f is almost as good as the best approximation of f, is obtained.     

基于扩张状态观测器的无速度传感器容错逆变器驱动永磁同步电机系统自抗扰模型预测转矩控制

针对三相四开关逆变器驱动永磁同步电机(PMSM)系统,基于扩张状态观测器(ESO)技术,提出了无速度传感器的自抗扰模型预测转矩控制(ADRMPTC)策略.建立了三相四开关逆变器驱动PMSM系统的数学模型;采用ESO技术构造了PMSM系统转速观测器,以实现对转速快速准确地实时估计;用自抗扰控制器(ADRC)作为系统的转速调节器,以提高系统的鲁棒性;利用模型预测转矩控制(MPTC)方法,以达到减小转矩和磁链脉动的目的.所设计基于ESO的无速度传感器ADRMPTC策略能够使三相四开关逆变器驱动的PMSM系统可靠稳定运行,达到满意的转矩和转速控制效果.与基于PI的MPTC策略相比,本文控制策略使PMSM系统不仅具有良好的动态性能,而且具有较强的抗负载干扰能力.仿真结果验证了所提方法的正确性和有效性.     

Nonlinear speed tracking control for sensorless PMSMs with unknown load torque: From theory to practice

In this paper the speed tracking control problem for sensorless (nonsalient-pole surface) permanent magnet synchronous motors (PMSMs) with unknown load torque is addressed. Simulation and experimental results are presented for the first nonlinear adaptive control which (i) relies on a closed loop stability proof using nonlinear stability tools; (ii) feeds back stator current and voltage measurements only without using non-robust open loop integration of motor dynamics. Satisfactory performances are obtained in practice in conditions which can be inferred by deeply analyzing the reported theoretical analysis.     

Adaptive learning control design for robotic manipulators driven by permanent magnet synchronous motors

On the basis of the ideas recently presented in Tomei and Verrelli (Tomei, P., and Verrelli, C.M. (2010 Tomei, P and Verrelli, CM. 2010. Learning Control for Induction Motor Servo Drives with Uncertain Rotor Resistance. International Journal of Control, 83: 1515–1528. [Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]), ‘Learning Control for Induction Motor Servo Drives with Uncertain Rotor Resistance’, International Journal of Control, 83, 1515–1528) and Marino et al. (Marino, R., Tomei, P., and Verrelli, C.M. (2011 Marino, R, Tomei, P and Verrelli, CM. 2011. Robust Adaptive Learning Control for Nonlinear Systems with Extended Matching Unstructured Uncertainties. International Journal of Robust and Nonlinear Control, Early View, doi: 10.1002/rnc.1720 [Google Scholar]), ‘Robust Adaptive Learning Control for Nonlinear Systems with Extended Matching Unstructured Uncertainties’, International Journal of Robust and Nonlinear Control, Early View, doi: 10.1002/rnc.1720), we briefly show how the adaptive learning control design proposed in Liuzzo and Tomei (Liuzzo, S., and Tomei, P. (2009 Liuzzo, S and Tomei, P. 2009. Global Adaptive Learning Control of Robotic Manipulators by Output Error Feedback. International Journal of Adaptive Control and Signal Processing, 23: 97–109.  [Google Scholar]), Global Adaptive Learning Control of Robotic Manipulators by Output Error Feedback, International Journal of Adaptive Control and Signal Processing, 23, 97–109) can be extended to robotic manipulators driven by nonsalient-pole (surface) permanent magnet synchronous motors. Unstructured uncertain dynamics (that is no parameterisation is available for the uncertainties) of the rigid robot with rotational joints are considered as well as uncertainties in stator resistances of the synchronous motors are taken into account. Two solutions with clear stability proofs are presented: a global decentralised control via state feedback and a semi-global control via output feedback. Output tracking of known periodic reference signals and learning of corresponding uncertain input reference signals are achieved. Available results in the literature are thus improved since no simplification concerning negligible electrical motor dynamics is used.     

A survey on sliding mode control strategies for induction motors

A state of the art review of control and estimation methods for induction motor (IM) based on conventional approaches, sliding mode control (SMC) and sensorless SMC is presented. The objective of this survey paper is to summarize the different control approaches for IMs including field oriented control (FOC), direct torque control (DTC), speed observer, observer based flux estimation, sliding mode (SM) flux and speed observer, current regulation by SMC, sensorless SMC, etc. The applications of SMC to IMs has been widespread in recent years. The increasing interest in SMC is because of its interesting features such as invariance, robustness, order reduction and control chattering. Particularly robustness of SM approach with respect to parameter variations and external disturbance is vital for the control system. The review covers the sensorless SMC schemes by integrating controller and observer design to guarantee convergence of the estimates to the real states. It also covers the chattering problems, encountered often in SMC area dealt by using an asymptotic observer.     

Adaptive Lyapunov-based neural network sensorless control of permanent magnet synchronous machines

In this paper, an adaptive neural network sensorless control scheme is introduced for permanent magnet synchronous machines (PMSMs). The control strategy consists of an adaptive speed controller that capitalizes on the machine’s inverse model to achieve accurate tracking, two artificial neural networks (ANNs) for currents control, and an ANN-based observer for speed estimation to overcome the drawback associated with the use of mechanical sensors while the rotor position is obtained by the estimated rotor speed direct integration to reduce the effect of the system noise. A Lyapunov stability-based ANN learning technique is also proposed to insure the ANNs’ convergence and stability. Unlike other sensorless control strategies, no a priori offline training, weights initialization, voltage transducer, or mechanical parameters knowledge is required. Results for different situations highlight the performance of the proposed controller in transient, steady-state, and standstill conditions.     

基于改进容积卡尔曼的PMLSM无传感控制研究

在永磁直线同步电机(PMLSM)的无传感器控制系统中,需要对动子的位置和速度进行实时状态估计.针对标准的容积卡尔曼滤波算法在PMLSM无传感控制中存在状态协方差矩阵易失去非负定性,以及噪声统计特性未知时变导致的滤波精度降低甚至发散的问题,提出一种容积卡尔曼滤波的改进算法.该算法结合平方根滤波和改进的渐消型记忆时变噪声估...     

A speed-sensorless indirect field-oriented control for induction motors based on high gain speed estimation

The authors design a new speed sensorless output feedback control for the full-order model of induction motors with unknown constant load torque, which guarantees local asymptotic tracking of smooth speed and rotor flux modulus reference signals and local asymptotic field orientation, on the basis of stator current measurements only. The proposed nonlinear controller exploits the concept of indirect field orientation (no flux estimation is required) in combination with a new high-gain speed estimator based on the torque current tracking error. The estimates of unknown load torque and time-varying rotor speed converge to the corresponding true values under a persistency of excitation condition with a physically meaningful interpretation, basically equivalent to non-null synchronous frequency. Stability analysis of the overall dynamics has been performed exploiting the singular perturbation method. The proposed control algorithm is a “true” industrial sensorless solution since no simplifying assumptions (flux and load torque measurements) are required. Simulation and experimental tests show that the proposed controller is suitable for medium and high performance applications.     

Climatic sensorless maximum power point tracking in PV generation systems

The problem of maximum power point tracking (MPPT) is addressed for photovoltaic (PV) arrays considered in a given panel position. The PV system includes a PV panel, a PWM boost power converter and a storing battery. Although the maximum power point (MPP) of PV generators varies with solar radiation and temperature, the MPPT is presently sought without resorting to solar radiation and temperature sensors in order to reduce the PV system cost. The proposed sensorless control solution is an adaptive nonlinear controller involving online estimation of uncertain parameters, i.e. those depending on radiation and temperature. The adaptive control problem at hand is not a standard one because parameter uncertainty affects, in addition to system dynamics, the output-reference trajectory (expressing the MPPT purpose). Therefore, the convergence of parameter estimates to their true values is necessary for MPPT achievement. It is formally shown, under mild assumptions, that the developed adaptive controller actually meets the MPPT objective.     

Adaptive control of permanent magnet synchronous machines with disturbance estimation

In this paper,an adaptive control scheme is introduced for permanent magnet synchronous machines (PMSMs)as an alternative to classical control techniques.The adaptive control strategy capitalizes on the machine’s inverse dynamics to achieve accurate tracking by using an observer to approximate disturbance in the form of friction and load torque.The controller’s output is then fed to a space vector pulse width modulation(SVPWM)algorithm to produce duty cycles for the inverter.The control scheme is validated through a set of simulations on an experimentally validated PMSM model.Results for different situations highlight its high speed tracking accuracy and high performance in compensating for friction and load disturbances of various magnitudes.     

Global output feedback tracking control for a class of Lagrangian systems

The problem of global tracking control without velocity measurement of the so-called Euler–Lagrange systems has been paid a lot of attention for the past several years. In this note, a nice property of one-degree-of-freedom Euler–Lagrange systems is highlighted, which in particular allows us to obtain a new solution to the problem of tracking control for this class of systems. The solution is under the form of a linear-like observer-based controller which gives fairly good results, as illustrated in the simulation. The method can be generalized to any system having the same property.     

Output feedback control of wind energy conversion system involving a doubly fed induction generator

This paper deals with the problem of controlling a wind energy conversion system (WECS) based on the doubly fed induction generator (DFIG), by IGBT‐based back‐to‐back rectifier‐inverter. The goal of control is to maximize wind energy extraction letting the wind turbine rotor operate in a variable‐speed mode. Interestingly, the present study features the achievement of the above energetic goal without resorting to sensors for wind velocity. The control strategy involves: (i) an output feedback non‐linear regulator designed by the backstepping technique and based on the use of a high gain observer; (ii) a sensorless online reference‐speed optimizer designed using the turbine power characteristic to achieve the maximum power point tracking (MPPT) requirement. It is formally shown that the proposed controller actually meets its control objectives. This theoretical result is confirmed by several simulations.     

On the selection of control configurations for uncertain systems using gramian-based Interaction Measures

A critical step in the control design of industrial processes is the Control Configuration Selection (CCS), where each actuator is associated with a set of measurements which will be used in the calculation of the control action.A possible solution to the CCS problem is given by the gramian-based Interaction Measures (IMs), which are derived from nominal process models. This paper introduces the derivation of uncertainty bounds for a gramian-based IM using models with uncertainty described in multiplicative form. An alternative to this model-based approach is presented, where uncertainty bounds are estimated from a tailored experiment.In addition, a procedure for robust CCS is introduced. This procedure integrates the calculated uncertainty bounds in the design of the control configuration.     

基于LPV观测器的永磁同步电机反推控制

针对永磁同步电机(Permanent Magnet Synchronous Motor,PMSM)无速度传感器转速跟踪控制精度问题,提出了一种基于线性变参数(Lineeo Parameteo Varying,LPV)转速观测器的永磁同步电机反推控制方法。该方法首先根据PMSM的LPV模型,推导出电机的凸胞形顶点方程;然后利用Lyapunov稳定性理论,获得了基于线性矩阵不等式(Lineeo Matrix Inequality,LMI)的观测器设计方法,构造了PMSM的LPV观测器,实现对电机转速及定子交轴电流的重构;最后运用反推控制策略,设计电机闭环系统控制器,实现对电机转速的高精度跟踪控制。仿真结果表明,该方法相较与传统PI矢量控制,跟踪精度高、响应快、抗负载扰动强,对实现PMSM的无速度传感器高精度转速跟踪控制具有重要意义。     

Virtual control strategy and conditioning technique for tracking and learning controls under input restrictions

The virtual control strategy for mechanical systems has been recently proposed (Gnucci and Marino, 2021) in the context of under-actuated mechanical systems. Such a strategy views and represents an under-actuated mechanical system as a fully actuated system with virtually added inputs and outputs having to satisfy, through a suitable choice of the virtual output reference signals, the virtual input zero-equality constraint: the related adaptive tracking control problem is then solved through standard design techniques. This paper exhibits a twofold aim. The first one is: to enlarge the concept of zero-input constraint and thus naturally adapt the virtual control approach to the case in which an actuator fault can occur. The second aim is: to show how the application and transposition of such an adaptation to two well-known classes of nonlinear systems (special systems in multi-variable tracking form with two inputs and outputs under actuator faults; one-relative-degree, single-input, single-output systems in output feedback form under input saturation) not only own strong connections with the conditioning technique, originally conceived in the context of anti-windup problems under input constraints, but they also gain original results.     

Adaptive state observers for sensorless control of switched reluctance motors

In this paper, we address the problem of state observation for sensorless control of switched reluctance motors (SRMs), that is, the regulation of the motor measuring only the voltage and the current of the electrical supply. Instrumental for the construction of the observer is the derivation of algebraic relations, which define regression models, between the unknown rotor flux and the measured quantities. With the knowledge of the flux, it is shown that the mechanical coordinates can be estimated with suitably tailored adaptive nonlinear observers. Replacing the observed states, in a certainty equivalent manner, with a full information stabilizing law completes the sensorless controller design. In contrast with other motors, there is no universally accepted mathematical model to describe the dynamics of SRMs. To widen our target audience, we present the results for four different mathematical models reported in the literature. Simulation results with a precise (finite element) model of the SRM are used to illustrate the performance of the proposed observer and sensorless control.     

Adaptive hybrid force/position control of robot manipulators using an adaptive force estimator in the presence of parametric uncertainty

This study is devoted to sensorless adaptive force/position control of robot manipulators using a position-based adaptive force estimator (AFE) and a force-based adaptive environment compliance estimator. Unlike the other sensorless method in force control that uses disturbance observer and needs an accurate model of the manipulator, in this method, the unknown parameters of the robot can be estimated along with the force control. Even more, the environment compliance can be estimated simultaneously to achieve tracking force control. In fact, this study deals with three challenging problems: No force sensor is used, environment stiffness is unknown, and some parametric uncertainties exist in the robot model. A theorem offers control laws and updating laws for two control loops. In the inner loop, AFE estimates the exerted force, and then, the force control law in the outer loop modifies the desired trajectory of the manipulator for the adaptive tracking loop. Besides, an updating law updates the estimated compliance to provide an accurate tracking force control. Some experimental results of a PHANToM Premium robot are provided to validate the proposed scheme. In addition, some simulations are presented that verify the performance of the controller for different situations in interaction.     

Robust passivity‐based control of switched‐reluctance motors

We propose a state‐feedback controller for switched‐reluctance motors as a preliminary step toward the solution of the sensorless control problem (without measurement of rotor variables). We establish global exponential stability. Furthermore, our controller renders the closed‐loop system robust to external disturbances, that is, input‐to‐state stable. Although there exist some works on sensorless control of switched‐reluctance motors, these consist mainly of ad hoc solutions without theoretical foundation. The few theoretically validated results in the literature are established under more stringent conditions such as knowledge of the load torque. Copyright © 2014 John Wiley & Sons, Ltd.     

Neurodynamic programming and tracking control scheme of constrained-input systems via a novel event-triggered PI algorithm

In this paper, a new event-triggered approach for neurodynamic programming and optimal tracking control problem of constrained-input systems is proposed, where the desired trajectory can be generated as a large class of useful command trajectories. Firstly, the complex tracking problem is converted to a stabilizing control optimization problem by reconstructing a novel augmented tracking system with discounted performance index. Secondly, instead of the conventional time-driven control, an event-triggered policy iteration (PI) algorithm is designed to drive the system dynamic to track the reference trajectory, which requires less computation and fewer transmissions during the solving. Thirdly, the novel tracking control can be bounded as desired, which overcomes the unconstrained steady-state control from the general adaptive dynamic programming based tracking solution. Moreover, only critic neural network is used in the implementation of iteration learning, which simplifies the actor-critic architecture and reduces computational load. And by the means of Lyapunov method, the ultimately boundedness of the tracking system under the event-triggered PI algorithm is proved. Finally, the developed approach is applied to track a sinusoidal waveform and a periodic rectangular step signal in the simulations, where the effectiveness is also demonstrated.     

初级永磁直线电机双动子电流镜像容错控制

为解决双动子无电流传感器控制算法存在参数依赖性高、鲁棒性差等问题,提出一种电流镜像容错控制(current mirror fault-tolerant control,CMFC)策略,在仅采用单动子相电流传感器的条件下完成两台级联动子的控制。双动子系统中动子1为正常动子,采用传统的有限集模型预测控制(finite control set-model predictive control,FCS-MPC)完成电流的跟踪控制。动子2为故障动子,所有电流传感器均发生故障。CMFC控制将动子2虚拟电流与动子1的虚拟电流作为FCS-MPC控制器的输入并完成电流跟踪控制。通过完成两台动子的虚拟电流跟踪,由镜像原理实现两台动子的实际电流跟踪。理论分析和仿真实验证明了CMFC控制能实现动子2实际电流的稳定控制,且具有鲁棒性高的特点。