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1.
This paper deals with the modelling of highly nonlinear switching power-electronics converters using black-box identification methods. The duty cycle and the output voltage are chosen, respectively, as the input and the output of the model. A nonlinear Hammerstein-type mathematical model, consisting of a static nonlinearity and a linear time-invariant model, is considered in order to cope with the well-known limitations of the more common small-signal models, i.e. the entity of the variations of the variables around a well-defined steady-state operating point and the incorrect reproduction of the steady-state behavior corresponding to input step variations from the above steady-state operating point. The static nonlinearity of the Hammerstein model is identified from input-output couples measured at steady state for constant inputs. The linear model is identified from input-output data relative to a transient generated by a suitable pseudorandom binary sequence constructed with two input values used to identify the nonlinearity. The identification procedure is, first, illustrated with reference to a boost DC/DC converter using results of simulations carried out in the PSpice environment as true experimental results. Then, the procedure is experimentally applied on a prototype of the above converter. In order to show the utility of the Hammerstein models, a PI controller is tuned for a nominal model. Simulation and experimental results are displayed with the aim of showing the peculiarities of the approach that is followed.  相似文献   

2.
This paper studies the identification and the real-time control of an electrohydraulic servo system. The control strategy is based on the nonlinear backstepping approach. Emphasis is essentially on the tuning parameters effect and on how it influences the dynamic behavior of the errors. While the backstepping control ensures the global asymptotic stability of the system, the tuning parameters of the controller, nonetheless, do greatly affect the saturation and chattering in the control signal, and consequently, the dynamic errors. In fact, electrohydraulic systems are known to be highly nonlinear and non-differentiable due to many factors, such as leakage, friction, and especially, the fluid flow expression through the servo valve. These nonlinear terms appear in the closed loop dynamic errors. Their values are so large that in the presence of a poor design, they can easily overwhelm the effect of the controller parameters. Backstepping is used here because it is a powerful and robust nonlinear strategy. The experimental results are compared to those obtained with a real-time proportional-integral-derivative (PID) controller, to prove that classic linear controllers fail to achieve a good tracking of the desired output, especially, when the hydraulic actuator operates at the maximum load. Before going through the controller design, the system parameters are identified. Despite the nonlinearity of the system, identification is based on the recursive least squares method. This is done by rewriting the mathematical model of the system in a linear in parameters (LP) form. Finally, the experimental results will show the effectiveness of the proposed approach in terms of guaranteed stability and zero tracking error  相似文献   

3.
We present identification methods for nonlinear mechatronic systems. First, we consider a system consisting of a known linear part and an unknown static nonlinearity. With this approach, using an intelligent observer, it is possible to identify the nonlinear characteristic and to estimate all unmeasurable system states. The identification result of the nonlinearity and the estimated system states are used to improve the controller performance. Secondly, the first approach is extended to systems where both the linear parameters and the nonlinear characteristic are unknown. This is achieved by implementing the intelligent observer as a structured recurrent neural network  相似文献   

4.
The Takagi-Sugeno (TS) fuzzy modeling technique, a black-box discrete-time approach for system identification, has widely been used to model behaviors of complex dynamic systems. The analytical structure of TS fuzzy models, however, is unknown, causing at two major problems. First, the fuzzy models cannot be utilized to design controllers of the physical systems modeled. Second, there is no systematic technique for designing a controller that is capable of controlling any given TS fuzzy model to achieve the desired tracking or setpoint control performance. In this paper, we provide solutions to these problems. We have proved that a general class of TS fuzzy models is a nonlinear time-varying ARX (Auto-Regressive with eXtra input) model. We have established a simple condition for analytically determining the local stability of the general TS fuzzy dynamic model. The condition can also be used to analytically check the quality of a TS fuzzy model and invalidate the model if the condition warrants. We have developed a feedback linearization technique for systematically designing an output tracking controller so that the output of a controlled TS fuzzy system of the general class achieves perfect tracking of any bounded time-varying trajectory. We have investigated the stability of the tracking controller and established a condition, in relation to the stability of non-minimum phase systems, for analytically deciding whether a stable tracking controller can be designed using our method for any given TS fuzzy system. Three numerical examples are provided to illustrate the effectiveness and utility of our results and techniques  相似文献   

5.
Acoustic echo cancellation (AEC) is critical for telecommunication applications involving two or more locations such as teleconferencing. It is also challenging because of loudspeaker's nonlinearity, real-time implementation requirement, and multipath effects of indoor environments. This paper addresses the nonlinear AEC problem. We use a Hammerstein model to describe the memoryless nonlinearity of loudspeaker concatenated with a linear room impulse response. We propose a method using a pseudo magnitude squared coherence (MSC) function to identify the nonlinearity in the Hammerstein system and develop an on-line AEC algorithm. Our method identifies nonlinearity without knowing the linear block in the Hammerstein system, which guarantees the stability of the algorithm and leads to a faster convergence rate. Moreover, several alternative criteria based on the MSC function are also proposed for nonlinearity identification. Effectiveness of the proposed algorithms is demonstrated through computer simulations.   相似文献   

6.
针对永磁直线同步电机伺服系统存在的诸多不确定性问题,设计了模型参考离散滑模(MRDSMC)位置跟踪控制器。将伺服问题转化为调节问题,为了保证跟踪的快速性,在传统DSMC滑模面的基础上,引入遗忘因子。并采用一步延迟扰动近似来补偿未知扰动。此外,为了抑制由高频未建模动态引起的抖振,在MRDSMC设计中加入低通滤波器。仿真结果表明,该方案能有效地抑制系统未建模动特性的影响,具有很强的鲁棒跟踪性能。  相似文献   

7.
针对上肢康复训练机械臂具有强耦合、非线性和时变的特点,设计了基于SVM(支持向量机)的轨迹跟踪预测控制器。采集机械臂系统的输入和输出数据,通过SVM辨识得到广义逆系统,与原系统串联实现解耦。对解耦后的系统,采用SVM辨识预测模型和PSO优化滚动控制序列的预测函数控制方法,并从其内模结构分析了系统的稳定性和鲁棒性。实验结果表明,该方法能够平稳高精度地实现轨迹跟踪。  相似文献   

8.
赵旭楷  刘兆霆 《信号处理》2022,38(2):432-438
本论文研究了单输入单输出非线性Hammerstein系统的辨识问题,提出了一种具有变遗忘因子的递推最小二乘算法。由于Hammerstein系统模型的非线性特征,传统的递推最小二乘算法无法直接用来解决该系统的辨识问题。为此,论文将Hammerstein系统参数进行了映射变换,使得变换后的系统参数与Hammerstein系统的输入输出构成一个线性关系,从而使系统能够适用于递推最小二乘算法;另外,为解决由于参数映射带来的收敛速度下降问题,论文进一步提出了一种变遗忘因子的递推最小二乘算法。仿真结果表明,提出的算法能够获得较好的收敛性和稳定性,并具有对非线性Hammerstein系统较高的辨识精度。   相似文献   

9.
This paper considers the use of cubic splines, instead of polynomials, to represent the static nonlinearities in block structured models. It introduces a system identification algorithm for the Hammerstein structure, a static nonlinearity followed by a linear filter, where cubic splines represent the static nonlinearity and the linear dynamics are modeled using a finite impulse response filter. The algorithm uses a separable least squares Levenberg-Marquardt optimization to identify Hammerstein cascades whose nonlinearities are modeled by either cubic splines or polynomials. These algorithms are compared in simulation, where the effects of variations in the input spectrum and distribution, and those of the measurement noise are examined. The two algorithms are used to fit Hammerstein models to stretch reflex electromyogram (EMG) data recorded from a spinal cord injured patient. The model with the cubic spline nonlinearity provides more accurate predictions of the reflex EMG than the polynomial based model, even in novel data.  相似文献   

10.
This study is concerned with the position control of an induction servomotor using a recurrent-neural-network (RNN)-based adaptive-backstepping control (RNABC) system. The adaptive-backstepping approach offers a choice of design tools for the accommodation of system uncertainties and nonlinearities. The RNABC system is comprised of a backstepping controller and a robust controller. The backstepping controller containing an RNN uncertainty observer is the principal controller, and the robust controller is designed to dispel the effect of approximation error introduced by the uncertainty observer. Since the RNN has superior capabilities compared to the feedforward NN for dynamic system identification, it is utilized as the uncertainty observer. In addition, the Taylor linearization technique is employed to increase the learning ability of the RNN. Meanwhile, the adaptation laws of the adaptive-backstepping approach are derived in the sense of the Lyapunov function, thus, the stability of the system can be guaranteed. Finally, simulation and experimental results verify that the proposed RNABC can achieve favorable tracking performance for the induction-servomotor system, even with regard to parameter variations and input-command frequency variation.  相似文献   

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