DSP-based self-tuning IP speed controller with load torquecompensation for rolling mill DC drive

This paper describes the design and the implementation of a self-tuning integral-proportional (IP) speed controller for a rolling mill DC motor drive system, based on a 32-bit floating point digital signal processor (DSP)-TMS 320C30. To get a better transient response than conventional proportional-integral (PI) and/or integral-proportional (IP) speed control in the presence of transient disturbance and/or parameter variations, an adaptive self-tuning IP speed control with load torque feedforward compensation was used. The model parameters, related to motor and load inertia and damping coefficient, were estimated online by using recursive extended least squares (RELS) estimation algorithm. On the basis of the estimated model parameters and a pole-placement design, a control signal was calculated. Digital simulation and experimental results showed that the proposed controller possesses excellent adaptation capability under parameter change and a better transient recovery characteristic than a conventional PI/IP controller under load change     

Neural-network-based self-tuning PI controller for precise motioncontrol of PMAC motors

In general, proportional plus integral (PI) controllers used in computer numerically controlled machines possess fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness of fixed-gain PI controllers, we propose a new neural-network-based self-tuning PI control system. In this new approach, a well-trained neural network supplies the PI controller with suitable gain according to each operating condition pair (torque, angular velocity, and position error) detected. To demonstrate the advantages of our proposed neural-network-based self-tuning PI control technique, both computer simulations and experiments were executed in this research. During the computer simulation, the direct experiment method was adopted to better model the problem of hysteresis in the AC servo motor. In real experiments, a PC-based controller was used to carry out the control tasks. Results of both computer simulations and experiments show that the newly developed dynamic PI approach outperforms the fixed PI scheme in rise time, precise positioning, and robustness     

New antiwindup PI controller for variable-speed motor drives

The windup phenomenon appears and results in performance degradation when the proportional-integral (PI) controller output is saturated. A new antiwindup PI controller is proposed to improve the control performance of variable-speed motor drives, and it is experimentally applied to the speed control of a vector-controlled induction motor driven by a pulsewidth modulated (PWM) voltage-source inverter (VSI). The integral state is separately controlled, corresponding to whether the PI controller output is saturated or not. The experimental results show that the speed response has much improved performance, such as small overshoot and fast settling time, over the conventional antiwindup technique. Although the operating speed command is changed, similar control performance can be obtained by using the PI gains selected in the linear region     

Design of incremental fuzzy PI controllers for a gas-turbine plant

In this paper, incremental fuzzy proportional integral (PI) speed and temperature controllers for a heavy-duty gas-turbine plant are presented. To improve performance, an analysis of incremental fuzzy PI control is provided, and new fuzzy control rules are proposed. In applying the fuzzy PI control to a gas-turbine plant, all gains are optimized by an adaptive genetic algorithm. We show the performance improvement of the proposed controller compared with conventional PI controller via simulations.     

New hybrid fuzzy controller for direct torque control induction motor drives

A new hybrid fuzzy controller for direct torque control (DTC) induction motor drives is presented in this paper. The newly developed hybrid fuzzy control law consists of proportional-integral (PI) control at steady state, PI-type fuzzy logic control at transient state, and a simple switching mechanism between steady and transient states, to achieve satisfied performance under steady and transient conditions. The features of the presented new hybrid fuzzy controller are highlighted by comparing the performance of various control approaches, including PI control, PI-type fuzzy logic control (FLC), proportional-derivative (PD) type FLC, and combination of PD-type FLC and I control, for DTC-based induction motor drives. The pros and cons of these controllers are demonstrated by intensive experimental results. It is shown that the presented induction motor drive is with fast tracking capability, less steady state error, and robust to load disturbance while not resorting to complicated control method or adaptive tuning mechanism. Experimental results derived from a test system are presented confirming the above-mentioned claims.     

Vibration Suppression Using Single Neuron-Based PI Fuzzy Controller and Fractional-Order Disturbance Observer

An approach is proposed for vibration suppression in a two-inertia system using an integration of a fractional-order disturbance observer and a single neuron-based PI fuzzy controller. The former is used to obtain disturbance estimate and generate compensation signal, and the latter is utilized to realize outer loop control. Fractional-order disturbance observer has a wider range to select a suitable tradeoff between robustness and vibration suppression, because introduction of fractional calculus makes universe of relative degree of Q-filter is expanded from integer domain to real-number domain. For the single neuron-based PI fuzzy controller, a single neuron makes up a PI controller and such a controller is embedded in each cell of the fuzzy control table. Thus, the fuzzy control table is changed into a controller matrix and it constructs a nonlinear adaptive controller with parameter self-tuning property. Experimental results illustrate that the integration of fractional-order disturbance observer and single neuron-based PI fuzzy controller can improve the performance of disturbance attenuation and system robustness     

Hybrid fuzzy proportional-integral plus conventional derivativecontrol of linear and nonlinear systems

This paper presents a new approach toward the optimal design of a hybrid proportional-integral-derivative (PID) controller applicable for controlling linear as well as nonlinear systems using genetic algorithms (GAs). The proposed hybrid PID controller is derived by replacing the conventional PI controller by a two-input normalized linear fuzzy logic controller (FLC) and executing the conventional D controller in an incremental form. The salient features of the proposed controller are as follows: (1) the linearly defined FLC can generate nonlinear output so that high nonlinearities of complex systems can be handled; (2) only one well-defined linear fuzzy control space is required for both linear and nonlinear systems; (3) optimal tuning of the controller gains is carried out by using a GA; and (4) it is simple and easy to implement. Simulation results on a temperature control system (linear system) and a missile model (nonlinear system) demonstrate the effectiveness and robustness of the proposed controller     

Dynamic control of electronic differential in the field weakening region

A simple and dynamic electronic differential control method for an outer rotor motor driven electric vehicle based on fuzzy gain scheduling of PI gains method is proposed for constant torque and power region operation using brushless direct current (BLDC) machine. The proposed method is quite insensitive to torque fluctuations and transient speed oscillations due to surface mounted permanent magnet (SMPM) BLDC machines constraints in the field weakening region. To improve the dynamics and stability of the electronic differential system and eliminate the skidding of the wheels and reduce the heating of electric machine in the wide speed range operation, a robust control method is developed. Moreover, PI controller gains are updated continuously by fuzzy gain scheduling approach which has phase advance angle, steering angle and measured speed as controller input parameters in order to eliminate the errors caused from the variable road conditions and torque oscillations in the field weakening region. The proposed method is implemented with 2 × 1.5 kW BLDC motor drive controlled by a TMS320F28335 digital signal processor (DSP). The experimental results show that the proposed method exhibits greater stability under various load, road and vehicle speed conditions.     

Fuzzy adaptive vector control of induction motor drives

This paper deals with the design and experimental realization of a model reference adaptive control (MRAC) system for the speed control of indirect field-oriented (IFO) induction motor drives based on using fuzzy laws for the adaptive process and a neuro-fuzzy procedure to optimize the fuzzy rules. Variation of the rotor time constant is also accounted for by performing a fuzzy fusion of three simple compensation strategies. A performance comparison between the new controller and a conventional MRAC control scheme is carried out by extensive simulations confirming the superiority of the proposed fuzzy adaptive regulator. A prototype based on an induction motor drive has been assembled and used to practically verify the features of the proposed control strategy     

同步电动机智能励磁调节器的仿真研究

针对同步电动机励磁系统的非线性等特点,提出智能励磁调节器。文章对模糊控制和神经网络控制以及两者的结合进行了分析研究,提出神经网络模糊控制器,并进行了带参数自调整的神经网络模糊控制器的设计。最后对理论进行了对比仿真验证,证明了其优越性。     

High-Speed Control of IPMSM Drives Using Improved Fuzzy Logic Algorithms

This paper presents an improved fuzzy logic controller (FLC) for an interior permanent magnet synchronous motor (IPMSM) for high-performance industrial drive applications. In the proposed control scheme for high-speed operations above the rated speed, the operating limits of IPMSM are expanded by incorporating the maximum torque per ampere operation in constant torque region and the flux-weakening operation in constant power region. The power ratings of the motor and the inverter are considered in developing the control algorithm. A new and simple FLC is utilized as a speed controller. The FLC is developed to have less computational burden, which makes it suitable for real-time implementation, particularly at high-speed operating conditions. The complete drive is implemented in real-time using digital signal processor (DSP) controller board DS 1102 on a laboratory 1-hp IPM motor. The efficiency of the proposed control scheme is evaluated through both experimental and computer simulation results. The proposed controller is found to be robust for high-speed applications     

Speed control system design and experimentation for interior PMSM drives

This paper presents a robust speed-control strategy using a Takagi–Sugeno fuzzy model for interior permanent magnet synchronous motor (IPMSM) drives. The sufficient conditions of linear matrix inequalities, which can guarantee the existence of the fuzzy controller gains, are derived from a common quadratic Lyapunov function. Moreover, the maximum torque per ampere control is incorporated to improve the torque production in the constant torque region and the efficiency of the IPMSM drive. The global stability of an observer-based control system is analytically proven. Simulations and experiments are conducted to demonstrate the feasibility of the proposed approach through a prototype IPMSM drive system. Consequently, the proposed fuzzy control methodology can achieve less steady-state error and less sensitivity than the conventional feedback linearisation control method under motor parameter variations and external disturbances.     

Fuzzy Logic-Based Torque Control System for Milling Process Optimization

This paper focuses on the design and implementation of a fuzzy-logic-based torque control system, embedded in an open-architecture computer numerical control (CNC), in order to provide an optimization function for the material removal rate. The control system adjusts the feed rate and spindle speed simultaneously as needed, to regulate the cutting torque using the CNC's own resources without requiring additional hardware overheads. The control system consists of two inputs (i.e., torque error and change of error), two outputs (i.e., the feed rate and spindle speed increment) fuzzy controller, and a self-tuning mechanism, all of which are embedded within the kernel of a standard open control. The self-tuning strategy is based on the measured peaks in the torque error signal of the closed-loop system response. The self-tuning fuzzy controller is applied to the milling process in a production environment in order to demonstrate the improvements in performance and effectiveness. Two approaches are tested, and their performance is assessed using several performance measurements. These approaches are the two-input/two-output for the fuzzy controller and a single-output fuzzy controller (i.e., only feed-rate modification), with and without the self-tuning mechanism. The results demonstrate that the proposed control strategy provides better transient performance, accuracy, and machining cycle time than the others, thus, increasing the metal removal rate.     

A fuzzy logic sliding mode controlled electronic differential for a direct wheel drive EV

In this study, a direct wheel drive electric vehicle based on an electronic differential system with a fuzzy logic sliding mode controller (FLSMC) is studied. The conventional sliding surface is modified using a fuzzy rule base to obtain fuzzy dynamic sliding surfaces by changing its slopes using the global error and its derivative in a fuzzy logic inference system. The controller is compared with proportional–integral–derivative (PID) and sliding mode controllers (SMCs), which are usually preferred to be used in industry. The proposed controller provides robustness and flexibility to direct wheel drive electric vehicles. The fuzzy logic sliding mode controller, electronic differential system and the overall electrical vehicle mechanism are modelled and digitally simulated by using the Matlab software. Simulation results show that the system with FLSMC has better efficiency and performance compared to those of PID and SMCs.     

Fuzzy adaptive speed control of a permanent magnet synchronous motor

A fuzzy adaptive speed controller is proposed for a permanent magnet synchronous motor (PMSM). The proposed fuzzy adaptive speed regulator is insensitive to model parameter and load torque variations because it does not need any accurate knowledge about the motor parameter and load torque values. The stability of the proposed control system is also proven. The proposed adaptive speed regulator system is implemented by using a TMS320F28335 floating point DSP. Simulation and experimental results are presented to verify the effectiveness of the proposed fuzzy adaptive speed controller under uncertainties such as motor parameter and load torque variations using a prototype PMSM drive system.     

Fuzzy two-degrees-of-freedom speed controller for motor drives

A fuzzy two-degrees-of-freedom (2-DOF) controller and its application to the speed control of an induction motor drive are presented in this paper. The proposed controller is composed of two fuzzy controllers to obtain good tracking and regulating responses. Unlike the conventional fuzzy controller, the error between the outputs of a reference model and the controlled drive is used to drive the proposed fuzzy controller. The drive rotor speed response can closely follow the trajectory produced by the reference model, and good load speed regulating response can also be obtained simultaneously owing to the possession of two-degrees-of-freedom in structure. Moreover, these performances are rather insensitive to the operating condition changes. The dynamic signal analysis as well as the construction of fuzzy control algorithms are described in detail. Some simulated and measured results are provided to demonstrate the effectiveness of the proposed fuzzy controller     

A high-performance sensorless indirect stator flux orientation control of induction motor drive

A new method for the implementation of a sensorless indirect stator-flux-oriented control (ISFOC) of induction motor drives with stator resistance tuning is proposed in this paper. The proposed method for the estimation of speed and stator resistance is based only on measurement of stator currents. The error of the measured q-axis current from its reference value feeds the proportional plus integral (PI) controller, the output of which is the estimated slip frequency. It is subtracted from the synchronous angular frequency, which is obtained from the output integral plus proportional (IP) rotor speed controller, to have the estimated rotor speed. For current regulation, this paper proposes a conventional PI controller with feedforward compensation terms in the synchronous frame. Owing to its advantages, an IP controller is used for rotor speed regulation. Stator resistance updating is based on the measured and reference d-axis stator current of an induction motor on d-q frame synchronously rotating with the stator flux vector. Experimental results for a 3-kW induction motor are presented and analyzed by using a dSpace system with DS1102 controller board based on the digital signal processor (DSP) TMS320C31. Digital simulation and experimental results are presented to show the improvement in performance of the proposed method.     

A new fuzzy logic-based controller design method for DC and ACimpressed-voltage drives

This paper deals with the design of fuzzy logic-based controllers (FLBC) for DC and AC electric drives. Industrial drives employ the cascaded PI control with a subordinated current control loop to make sure that the current does not exceed the admissible value and improve dynamic performances. The nonlinear FLBC characteristics permit one to achieve the performances of the cascaded control using only one control loop. This is feasible by a suitable choice of the scaling factors together with the rules of the fuzzy controller. The authors propose a minimum number of rules and the criteria, based on physical considerations, to determine the input and output gains instead of using the trial and error procedure. The designed FLBC is able to control the speed of a DC drive as well as the rotor speed and flux of a vector-controlled induction motor drive. Computer simulations show the effectiveness of the new fuzzy-controller design method. The reduced number of rules and membership functions and the application flexibility together with the possible implementation on low cost μPs lead the authors to think that the proposed tuning criteria will be widely adopted     

Damping of Torsional Vibrations in Two-Mass System Using Adaptive Sliding Neuro-Fuzzy Approach

In this paper, a new robust control system with the adaptive sliding neuro-fuzzy speed controller for the drive system with the flexible joint is proposed. A model reference adaptive control structure (MRAC) is used in this drive system. The torsional vibrations are successfully suppressed in the control structure with only one basic feedback from the motor speed. The damping ability of the proposed system has been confirmed for a wide range of the system parameters and compared with the other control concepts, like the adaptive Pi-type neuro-fuzzy controller and the classical cascade PI structure.     

Online self-tuning ANN-based speed control of a PM DC motor

This paper presents an online self-tuning artificial-neural-network (ANN)-based speed control scheme of a permanent magnet (PM) DC motor. For precise speed control, an online training algorithm with an adaptive learning rate is introduced, rather than using fixed weights and biases of the ANN. The complete system is implemented in real time using a digital signal processor controller board (DS1102) on a laboratory PM DC motor. To validate its efficacy, the performances of the proposed ANN-based scheme are compared with a proportional-integral controller-based PM DC motor drive system under different operating conditions. The comparative results show that the ANN-based speed control scheme is robust, accurate, and insensitive to parameter variations and load disturbances