Shiqi Zheng

Stable adaptive PI control for permanent magnet synchronous motor drive based on improved JITL technique

In this paper, a stable adaptive PI control strategy based on the improved just-in-time learning (IJITL) technique is proposed for permanent magnet synchronous motor (PMSM) drive. Firstly, the traditional JITL technique is improved. The new IJITL technique has less computational burden and is more suitable for online identification of the PMSM drive system which is highly real-time compared to traditional JITL. In this way, the PMSM drive system is identified by IJITL technique, which provides information to an adaptive PI controller. Secondly, the adaptive PI controller is designed in discrete time domain which is composed of a PI controller and a supervisory controller. The PI controller is capable of automatically online tuning the control gains based on the gradient descent method and the supervisory controller is developed to eliminate the effect of the approximation error introduced by the PI controller upon the system stability in the Lyapunov sense. Finally, experimental results on the PMSM drive system show accurate identification and favorable tracking performance.

Robust stability of fractional order system with general interval uncertainties

Abstract This paper focuses on the analysis of robust stability of fractional order system with general interval uncertainties. The concept of general interval uncertainties means that the interval uncertainties exist both in the coefficients and orders of the fractional order system. Necessary and sufficient conditions are proposed to check the robust stability of general interval fractional order system. According to the proposed stability criterion, it is interesting to find that the Edge Theorem, which was initially proposed for integer order system, cannot be directly extended to test the stability of fractional order system with general interval uncertainties. Examples are followed to verify the validity of the proposed method.

Adaptive two-degree-of-freedom PI for speed control of permanent magnet synchronous motor based on fractional order GPC.

In this paper, an adaptive two-degree-of-freedom (2Dof) proportional-integral (PI) controller is proposed for the speed control of permanent magnet synchronous motor (PMSM). Firstly, an enhanced just-in-time learning technique consisting of two novel searching engines is presented to identify the model of the speed control system in a real-time manner. Secondly, a general formula is given to predict the future speed reference which is unavailable at the interval of two bus-communication cycles. Thirdly, the fractional order generalized predictive control (FOGPC) is introduced to improve the control performance of the servo drive system. Based on the identified model parameters and predicted speed reference, the optimal control law of FOGPC is derived. Finally, the designed 2Dof PI controller is auto-tuned by matching with the optimal control law. Simulations and real-time experimental results on the servo drive system of PMSM are provided to illustrate the effectiveness of the proposed strategy.

Fractional order controllers tuning strategy for permanent magnet synchronous motor servo drive system based on genetic algorithm–wavelet neural network hybrid method

In this paper, a novel tuning strategy for the fractional order proportional integral and fractional order [proportional integral] controllers is proposed for the permanent magnet synchronous motor servo drive system. The tuning strategy is based on a genetic algorithm–wavelet neural network hybrid method. Firstly, the initial values of the control parameters of the fractional order controllers are selected according to a new global tuning rule, which is based on the genetic algorithm and considers both the time- and frequency-domain specifications. Secondly, the wavelet neural network is utilized to update the control parameters based on the selected initial values in an online manner which improves the capability of handling parameter variations and time-varying operating conditions. Furthermore, to improve the computational efficiency, a recursive least squares algorithm, which provides information to the wavelet neural network, is used to identify the permanent magnet synchronous motor drive system. Finally, experimental results on the permanent magnet synchronous motor drive system show both of the two proposed fractional order controllers work efficiently, with improved performance comparing with their traditional counterpart.

Graphical tuning method for non-linear fractional-order PID-type controllers free of analytical model

The main focus of this paper is on a graphical tuning method of non-linear fractional-order PID (FOPID)-type controllers, i.e. a class of FOPID-type controllers that non-linearly depend on the control parameters, e.g. FO[PI], FO[PD] etc. Firstly, a method is proposed to determine the stabilizing region of non-linear FOPID-type controllers, namely the complete sets of FOPID-type controllers providing stability of the control system. Secondly, two different approaches are proposed to determine the H∞ region of these FOPID-type controllers, namely the complete sets achieving H∞ robust performance specifications. The first approach maps the H∞ constraints into the parameter space by solving a series of non-linear equations. The second approach transforms the original H∞ region problem into simultaneous stabilization of a family of characteristic polynomials. It turns out that these two approaches are both very flexible, and the second approach is more efficient than the former. The main advantage of our proposed graphical tuning method is that the exact mathematical model of the controlled plant is not needed. The stabilizing and H∞ regions can be computed only from the frequency response data of the plant. Finally, numerical and experimental results are presented to demonstrate the proposed graphical tuning method.

Stabilizing region of PDμ controller for fractional order system with general interval uncertainties and an interval delay

Abstract This paper concentrates on computing the stabilizing region of PDμ controller for fractional order system with general interval uncertainties and an interval delay. The stabilizing region means the complete/approximate set of PDμ controllers that stabilize the given closed-loop control system. General interval uncertainties refer to both coefficients and orders of the fractional system suffer from interval uncertainties. Interval delay indicates that the delay also vary in a specified interval. Firstly, a method is presented to calculate the stabilizing region for general interval fractional system with an interval time-constant delay. Based on a novel mapping function and the concept of critical controller parameters, the stabilizing region can be determined numerically. Secondly, the stabilizing region computation problem for general interval fractional system with an interval time-varyingdelay is considered. By applying a revised small-gain theorem, the stabilizing region can be calculated like the time-constant delay case. Thirdly, two alternative methods are proposed to improve the computational efficiency of stabilizing region calculation. Both methods can reduce the number of polynomials which are used to determine the stabilizing region. Examples are followed to illustrate the proposed results.

Adaptive control for switched nonlinear systems with coupled input nonlinearities and state constraints

Abstract This paper focuses on the trajectory tracking problem for switched non-strict feedback nonlinear systems with arbitrary switching. Difficulties exist because coupled input nonlinearities and full state time-varying constraints. To solve this problem, we propose a new adaptive backstepping control strategy. This strategy has three distinguishing features: (1) Based on the concept of novel F -class functions, the proposed adaptive control strategy can deal with many input nonlinearities, including coupled unknown time-varying and state-dependent input nonlinearities. (2) By using a newsystem transformation technique, the proposed adaptive control method is suitable for very general systems, i.e., non-strict feedback nonlinear systems with arbitrary switching and time-varying state constraints. (3) The “explosion of complexity” problem in traditional backstepping design is avoided by using the approximation capability of fuzzy logic. It turns out that the proposed controller contains only one adaptive parameter. Practical examples are provided to illustrate the effectiveness of the proposed method.

Adaptive pseudo-derivative feedback with a feed-forward gain controller for permanent magnet synchronous motor servo system based on integrated iterative learning control

This article proposes an adaptive pseudo-derivative feedback with a feed-forward gain controller based on the integrated iterative learning control for the permanent magnet synchronous motor servo system. At first, the improved just-in-time online model identification method is adopted to identify and linearize the nonlinear servo system to obtain the model information for the control strategy. Second, a model-based iterative learning control strategy is presented for the tracking control of permanent magnet synchronous motor servo system. Meanwhile, to guarantee the robust convergence of the iterative learning control system, a new tuning methodology considering the model uncertainties is proposed to select the weighting matrices of the iterative learning control. Third, to further improve control performance, an online generalized predictive control is integrated in the iterative learning control framework, referred to as integrated iterative learning control. By combining generalized predictive control and iterative learning control, the integrated iterative learning control can complement both control methods to obtain good performance, because online generalized predictive control can respond to disturbances immediately and iterative learning control can correct bias left uncorrected by the online controller. Finally, an adaptive pseudo-derivative feedback with a feed-forward gain controller is designed based on the integrated iterative learning control. Since the integrated iterative learning control can be expressed by the pseudo-derivative feedback with a feed-forward gain parameter, the design can achieve both performance improvement and simple controller structure. Experiments confirm the effectiveness of the proposed adaptive pseudo-derivative feedback with a feed-forward gain controller.

Current compensation control for low-cost servo system

Three-phase current is very important in the vector control scheme of permanent magnet synchronous motor, and its sampling precision affects the servo control performance directly. In the low-cost servo system, to simplify the structure and reduce the volume, three-phase current is often not accurately detected because the current sensors are usually replaced by the sampling resistors, which will cause the undesirable torque ripple. Therefore, to enhance the closed-loop control performance for the low-cost servo system, an adaptive control method is proposed to compensate the current measurement error. In this approach, first, the estimated currents can be obtained online by a current state observer. Then, a three-phase current error compensator is designed based on the mathematical relation between three-phase current error and torque current. Next, based on the model reference adaptive control algorithm, two proportional–integral controllers are used to further strengthen the compensation effect. Finally, experimental results confirm that this method is effective and precise, and three-phase current error does not affect the servo control performance.