Position Control for PM Synchronous Motor Using Fuzzy Neural Network

Background: The conventional method using one-degree-of-freedom (1DOF) controller for Permanent Magnet Synchronous Motor (PMSM) servo system has the trade-off problem between the dynamic performance and the robustness. Methods: In this paper, by using H∞ control theory, a novel robust two-degree-of-freedom (2DOF) controller has been proposed to improve the position control performance of PMSM servo system. Using robust control theory and 2DOF control theory, a H∞ robust position controller has been designed and discussed in detail. Results: The trade-off problem between the dynamic performance and robustness which exists in one-degree-of-freedom (1DOF) control can be dealt with by the application of 2DOF control theory. Then, through H∞ control theory, the design of robust position controller can be translated to H∞ robust standard design problem. Moreover, the control system with robust controller has been proved to be stable. Conclusion: Further simulation results demonstrate that compared with the conventional PID control, the designed control system has better robustness and attenuation to the disturbance of load impact.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> A recurrent functional link (FL)-based fuzzy neural network (FNN) controller is proposed in this study to control the mover of a permanent-magnet linear synchronous motor (PMLSM) servo drive to track periodic reference trajectories. First, the dynamic model of the PMLSM drive system is derived. Next, a recurrent FL-based FNN controller is proposed in this study to control the PMLSM. Moreover, the online learning algorithms of the connective weights, means, and standard deviations of the recurrent FL-based FNN are derived using the back-propagation (BP) method. However, divergence or degenerated responses will result from the inappropriate selection of large or small learning rates. Therefore, an improved particle swarm optimization (IPSO) is adopted to adapt the learning rates of the recurrent FL-based FNN online. Finally, the control performance of the proposed recurrent FL-based FNN controller with IPSO is verified by some simulated and experimental results. </para>

The electrical energy from the sun can be extracted using solar photovoltaic (PV) modules. This energy can be maximized if the connected load resistance matches that of the PV panel. In search of the optimum matching between the PV and the load resistance, the maximum power point tracking (MPPT) technique offers considerable potential. This paper aims to show how the modelling process of an efficient PV system with a DC load can be achieved using a fuzzy neural network (FNN) controller. This is applied via an innovative methodology, which senses the irradiance and temperature of the PV panel and produces an optimal value of duty ration for the boost converter to obtain the MPPT. The coefficients of this controller have been refined based upon previous data sets using the irradiance and temperature. A gradient descent algorithm is employed to improve the parameters of the FNN controller to achieve an optimal response. The validity of the PV system using the MPPT technique based on the FNN controller is further demonstrated via a series of experimental tests at different ambient conditions. The simulation results show how the MPPT technique based on the FNN controller is more effective in maintaining the optimal power values compared with conventional techniques.

This paper presents design, implementation and experimental results of active vibration control of adaptive truss structure using fuzzy neural method. An adaptive truss structure with self-learning active vibration control system is developed. A fuzzy neural network (FNN) controller with adaptive membership functions is presented. The experimental setup of a two-bay truss structure with active members is constructed, and the FNN controller is applied to vibration suppression of the truss. The controller first senses the output of the accelerometer as an error to activate the adaptation of the weights of the controller, and then a control command signal is calculated based on the FNN inference mechanism to drive the active members. Experimental results demonstrate that the active FNN controller can effective reduce the truss vibration.

This paper investigates the warp let-off and take-up mechanism of rapier looms to solve the problem that the warp tension of rapier looms fluctuates greatly and the warp let-off is difficult to maintain constant. The design and hardware implementation of a let-off and take-up control system based on fuzzy neural network (FNN) and vector control (VC) are presented to improve the control level of warp tension and drive performance of the let-off and take-up system. Firstly, the spring-damper dynamic model of the warp is established according to the mechanical properties. The parametric expression of warp tension and the control strategy of fixed angle interval based on let-off and take-up motions are constructed according to the generation mechanism and fluctuation law of warp tension. Then, based on fuzzy reasoning mechanism and neural network model, the fusion theory of fuzzy neural network is introduced, and a tension controller based on T-S fuzzy neural network (FNN) is designed. FNN is trained by introducing genetic optimization and the backpropagation fusion algorithm (GA-BP). In addition, a specialized let-off and take-up hardware circuit is constructed through embedded technology, and the SVPWM algorithm is used as the driving scheme of the hardware circuit. Finally, simulation and actual weaving experiments test the proposed let-off and take-up control system and hardware circuit. The results show that, compared to PID and fuzzy PID, the proposed fuzzy neural network algorithm has higher tension control accuracy and can effectively restrain the rapier loom’s warp tension undulation. The designed hardware circuit and SVPWM algorithm have a fast and stable driving ability, which ensures the constant let-off amount.

Evaluation of fuzzy neural network run-to-run controller using numerical simulation analysis for SISO process

In this article, robust attitude control schemes are proposed for the near-space vehicle with the backlash-like hysteresis and the time-varying unknown disturbance using backstepping technique. In the robust attitude control design, the backlash-like hysteresis is described by a dynamic equation which is decomposed as a linear term and a nonlinear term. To handle the nonlinear term in the dynamic equation of the backlash-like hysteresis and the time-varying unknown disturbance, they are treated as a compounded disturbance and the nonlinear disturbance observer is designed to estimate them. Based on the developed nonlinear disturbance observer, the robust backstepping attitude control is firstly developed for the near-space vehicle. Then, the robust sliding mode attitude control scheme is presented for the near-space vehicle to further enhance the robust attitude control performance. The convergence of all closed-loop signals is rigorously proved via Lyapunov analysis method under the developed robust attitude control schemes. Finally, simulation results are presented to illustrate the effectiveness of the proposed attitude control approaches.

A new instantaneous torque-control strategy is presented for high-performance control of a permanent magnet (PM) synchronous motor. In order to deal with the torque pulsating problem of a PM synchronous motor in a low-speed region, new torque estimation and control techniques are proposed. The linkage flux of a PM synchronous motor is estimated using a model reference adaptive system technique, and the developed torque is instantaneously controlled by the proposed torque controller combining a variable structure control (VSC) with a space-vector pulse-width modulation (PWM). The proposed control provides the advantage of reducing the torque pulsation caused by the nonsinusoidal flux distribution. This control strategy is applied to the high-torque PM synchronous motor drive system for direct-drive applications and implemented by using a software of the digital signal processor (DSP) TMS320C30. The simulations and experiments are carried out for this system, and the results demonstrate the effectiveness of the proposed control.

Parameter estimation of fuzzy neural network controller based on a modified differential evolution

On the basis of previous research, This article studies the structure and working principle of recurrent fuzzy neural network. The traditional fuzzy controller is not precise enough, does not have simple algorithm but poor timely traceability. The use of the fuzzy neural network is good for fully integrating the fuzzy control feature and neural network control feature. And we can improve the fuzzy neural network into recurrent fuzzy neural network, and apply this in the power flow control of the power converter. Through simulating and contrasting, the experiments show that it can achieve the UPFC trend of dynamic real-time monitoring, it has better tracking control capability and robustness, and preferable effect of power control.

This paper presents the implementation of adaptive fuzzy neural network controller (FNNC) for accurate speed control of a permanent magnet synchronous motor (PMSM). FNNC includes neural network controller (NC) and fuzzy logic controller (FC). It combines the capability of fuzzy reasoning in handling uncertain information and the capability of neural network in learning from processes. The initial weights and biases of the artificial neural network (ANN) are obtained by offline training method. Using the output of the fuzzy controller (FC), online training is carried out to update the weights and biases of the ANN. Several results of simulation are provided to demonstrate the effectiveness of the proposed FNNC under the occurrence of parameter variations and external disturbance.KeywordsFuzzy ControllerFuzzy Logic ControllerProportional IntegralReference SpeedFuzzy Logic RuleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Adaptive fuzzy neural network control for motor-toggle servomechanism

The dynamic response of an adaptive fuzzy neural network (FNN) controlled quick-return mechanism, which is driven by a permanent magnet (PM) synchronous servo motor, is described in this study. The crank and disk of the quick-return mechanism are assumed to be rigid. First, Hamilton’s principle and Lagrange multiplier method are applied to formulate the mathematical model of motion. Then, based on the principle of computed torque, an adaptive controller is developed to control the position of a slider of the quick-return servomechanism. Moreover, since the selection of control gain of the adaptive controller has a significant effect on the system performance, an adaptive FNN controller is proposed to control the quick-return servomechanism. In the proposed adaptive FNN controller, an FNN is adopted to facilitate the adjustment of control gain on line. Simulated and experimental results due to periodic step and sinusoidal commands show that the dynamic behavior of the proposed adaptive FNN control system are robust with regard to parametric variations and external disturbances.

This paper deals with theoretical analysis and design of permanent magnet (PM) synchronous motors (PMSMs) with minimized cogging force. Recently, many optimal designs for the PMSMs have been done by finite element (FE) analysis, but such analysis generally is time consuming. In this study, the equation of magnetic flux lines existing between PMs and iron cores is expressed geometrically and the cogging force is calculated theoretically without FE analysis. The form of equation is assumed to be the second-order polynomial and the virtual core is used to express the cogging force in analytical model. The cogging force can be calculated by applying the solved flux line equation and the flux density equation to the Lorentz force equation by using the Maxwell stress tensor. The theoretical analysis of minimized cogging force is applied to several prototypes such as synchronous PM planar motor (SPMPM), 2-DOF PMSM with screw motion, and axial flux PM (AFPM) brushless dc motor in this paper, and the analytical results are validated by FE analyses and experiments.

A model reference control structure using a fuzzy neural network