IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 54, NO. 1, FEBRUARY 2007 671 Sliding Mode Neuro-Adaptive Control of Electric Drives Andon Venelinov Topalov, Member, IEEE, Giuseppe Leonardo Cascella, Member, IEEE, Vincenzo Giordano, Francesco Cupertino, and Okyay Kaynak, Fellow, IEEE Abstract—An innovative variable-structure-systems-based ap- proach for online training of neural network (NN) controllers as applied to the speed control of electric drives is presented. The proposed learning algorithm establishes an inner sliding motion in terms of the controller parameters, leading the command error towards zero. The outer sliding motion concerns the controlled electric drive, the state tracking error vector of which is simultane- ously forced towards the origin of the phase space. The equivalence between the two sliding motions is demonstrated. In order to eval- uate the performance of the proposed control scheme and its practical feasibility in industrial settings, experimental tests have been carried out with electric motor drives. Crucial problems such as adaptability, computational costs, and robustness are discussed. Experimental results illustrate that the proposed NN-based speed controller possesses a remarkable learning capability to control electric drives, virtually without requiring a priori knowledge of the plant dynamics and laborious startup procedures. Index Terms—Adaptive control, electric drives, neural networks (NNs), variable structure systems. I. INTRODUCTION T HE POSSIBILITY of achieving high-performance goals when controlling dynamic systems is usually directly re- lated to the degree of the model accuracy that can be achieved. In those applications where the knowledge of the system to be controlled is fragmentary or obtainable only in a costly way through complex offline experiments, artificial neural networks (NNs) can be an effective instrument to learn from input–output data and efficiently catch information about the most appropriate control action to apply [1]. However, the application of NNs in feedback control systems requires the study of their properties such as stability and robustness to environmental disturbances and structural uncertainties before drawing conclusions about the performances of the overall Manuscript received July 9, 2004. Abstract published on the Internet November 30, 2006. The work of A. V. Topalov was supported in part by the Bogazici University Research Fund Project 03A202 and in part by the TUBITAK Project 100E042. The work of O. Kaynak was supported by the Ministry of Education and Science of Bulgaria Research Fund Project BY-TH-108/2005. A. V. Topalov is with the Control Systems Department, Technical University of Sofia, 4000 Plovdiv, Bulgaria (e-mail: topalov@tu-plovdiv.bg). G. L. Cascella, V. Giordano, and F. Cupertino are with the Dipartimento di Elettrotecnica ed Elettronica, Politecnico di Bari, via Re David 200-70125 Bari, Italy (e-mail: cascella@deemail.poliba.it; cupertino@deemail.poliba.it; giordano@ deemail.poliba.it). O. Kaynak is with the Department of Electrical and Electronic Engineering, Mechatronics Research and Application Center, Bogazici University, Bebek, 34342 Istanbul, Turkey (e-mail: okyay.kaynak@boun.edu.tr). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TIE.2006.888930 system [2], [3]. Moreover, in neuro-adaptive systems, in order to compensate for the existing variable and unpredictable dis- turbances and changes in the plant parameters, robust and fast online learning of the neural controller is a key issue. It is, there- fore, essential to provide a tuning mechanism that guarantees stability and ensures high speed of convergence and robustness. Gradient-based learning methods have been frequently used in NN-based control applications [4]–[7], but they can very often lead to suboptimal performances in terms of the convergence speed, robustness, and computational burden. Furthermore, the stability of the learning process is not guaranteed. Recently, variable structure systems (VSSs)-based al- gorithms have been proposed for online tuning of NNs. Implementations on several NN and fuzzy inference system models have appeared in the literature [8]–[15], showing very interesting properties and proving to be faster and more robust than the traditional techniques. One of the first studies on adaptive learning in simple network architectures known as adaptive linear elements (ADALINEs) is due to Ramirez et al. [8], in which the inverse dynamics of a Kapitsa pendulum is identified by assuming constant bounds for uncertainties. Yu et al. [9] extend the results of [8] by introducing adaptive uncertainty bound dynamics and focus on the same example as the application, the drawback of the strategy being the existence of noise on the measured variables. In another paper [10], the existence of a relation between the sliding surface for the plant to be controlled and the zero learning error level of the parameters of the ADALINE neurocontroller is discussed and the control applications of the method considered in [8] and [9] are studied with constant uncertainty bounds. Differently from [8]–[10], the sliding mode algorithms pro- posed in [11] and [12] are for online training of multilayer NNs. As is well known, multilayer feedforward networks structures (MFNNs) are commonly used for online modeling, identifica- tion, and adaptive control purposes in case variations in process dynamics or in disturbance characteristics are present. They do not have the limited approximation capabilities of the early proposed Perceptron and ADALINE networks [16]. In the ap- proach presented in [11], separate sliding surfaces are defined for each network layer, taking into account the learning error variable and its time derivative. In [12], the ideas developed in [8] are further extended to allow online learning in MFNNs, with one sliding surface being defined using only the learning error, which makes it computationally simpler and suitable for real-time applications. The online learning capabilities of these algorithms in applications demanding adaptation to constantly changing environmental parameters, such as adaptive real-time control, are investigated in [13]–[15]. 0278-0046/$25.00 © 2007 IEEE Authorized licensed use limited to: ULAKBIM UASL - BOGAZICI UNIVERSITESI. Downloaded on February 19, 2009 at 06:36 from IEEE Xplore. Restrictions apply.