FPGA-Based Adaptive Backstepping Sliding-Mode Control for Linear Induction Motor Drive

A field-programmable gate array (FPGA)-based adaptive backstepping sliding-mode controller is proposed to control the mover position of a linear induction motor (LIM) drive to compensate for the uncertainties including the friction force. First, the dynamic model of an indirect field-oriented LIM drive is derived. Next, a backstepping sliding-mode approach is designed to compensate the uncertainties occurring in the motion control system. Moreover, the uncertainties are lumped and the upper bound of the lumped uncertainty is necessary in the design of the backstepping sliding-mode controller. However, the upper bound of the lumped uncertainty is difficult to obtain in advance of practical applications. Therefore, an adaptive law is derived to adapt the value of the lumped uncertainty in real time, and an adaptive backstepping sliding-mode control law is the result. Then, an FPGA chip is adopted to implement the indirect field-oriented mechanism and the developed control algorithms for possible low-cost and high-performance industrial applications. The effectiveness of the proposed control scheme is verified by some experimental results. With the adaptive backstepping sliding-mode controller, the mover position of the FPGA-based LIM drive possesses the advantages of good transient control performance and robustness to uncertainties in the tracking of periodic reference trajectories.     

Total sliding-mode controller for PM synchronous servo motor driveusing recurrent fuzzy neural network

In this paper, the dynamic responses of a recurrent-fuzzy-neural-network (RFNN) sliding-mode-controlled permanent-magnet (PM) synchronous servo motor are described. First, a newly designed total sliding-mode control system, which is insensitive to uncertainties, including parameter variations and external disturbance in the whole control process, is introduced. The total sliding-mode control comprises the baseline model design and the curbing controller design. In the baseline model design, a computed torque controller is designed to cancel the nonlinearity of the nominal plant. In the curbing controller design, an additional controller is designed using a new sliding surface to ensure the sliding motion through the entire state trajectory. Therefore, in the total sliding-mode control system, the controlled system has a total sliding motion without a reaching phase. Then, to overcome the two main problems with sliding-mode control, i.e., the assumption of known uncertainty bounds and the chattering phenomena in the control effort, an RFNN sliding-mode control system is investigated to control the PM synchronous servo motor. In the RFNN sliding-mode control system, an RFNN bound observer is utilized to adjust the uncertainty bounds in real time. To guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RFNN. Simulated and experimental results due to periodic step and sinusoidal commands show that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties     

Decoupled stator-flux-oriented induction motor drive with fuzzyneural network uncertainty observer

A stator-flux-oriented induction motor drive using online rotor time-constant estimation with a robust speed controller is introduced in this paper. The estimation of the rotor time constant is made on the basis of the model reference adaptive system using an energy function. The estimated rotor time-constant is used in the current-decoupled controller, which is designed to decouple the torque and flux in the stator-flux-field-oriented control. Moreover, a robust speed controller, which is comprised of an integral-proportional speed controller and a fuzzy neural network uncertainty observer, is designed to increase the robustness of the speed control loop. The effectiveness of the proposed control scheme is demonstrated by simulation and experimental results     

A hybrid computed torque controller using fuzzy neural network formotor-quick-return servo mechanism

The dynamic response of a hybrid computed torque controlled quick-return mechanism, which is driven by a permanent magnet (PM) synchronous servo motor, is described in this paper. 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 control, a position controller is designed to control the position of a slider of the motor-quick-return servo mechanism. In addition, to relax the requirement of the lumped uncertainty in the design of a computed torque controller, a fuzzy neural network (FNN) uncertainty observer is utilized to adapt the lumped uncertainty online. Moreover, a hybrid control system, which combines the computed torque controller, the FNN uncertainty observer, and a compensated controller, is developed based on Lyapunov stability to control the motor-quick-return servo mechanism. The computed torque controller with FNN uncertainty observer is the main tracking controller, and the compensated controller is designed to compensate the minimum approximation error of the uncertainty observer instead of increasing the rule numbers of the FNN. Finally, simulated and experimental results due to periodic step and sinusoidal commands show that the dynamic behaviors of the proposed hybrid computed torque control system are robust with regard to parametric variations and external disturbances     

Recurrent-fuzzy-neural-network sliding-mode controlled motor-toggleservomechanism

The dynamic responses of a recurrent-fuzzy-neural-network (RFNN) sliding-mode controlled motor-toggle servomechanism are described. The servomechanism is a toggle mechanism actuated by a permanent magnet (PM) synchronous servo motor. First, a total sliding-mode control system, which is insensitive to uncertainties including parameter variations and external disturbance in the whole control process, is introduced. In the baseline model design a computed torque controller is designed to cancel the nonlinearity of the nominal plant. In the curbing controller design an additional controller is designed using a new sliding surface to ensure the sliding motion through the entire state trajectory. Therefore, in the total sliding-mode control system the controlled system has a total sliding motion without a reaching phase. Then, to overcome the two main problems with sliding-mode control, a RFNN sliding-mode control system is investigated to control the motor-toggle servomechanism. In the RFNN sliding-mode control system a RFNN bound observer is utilized to adjust the uncertainty bounds in real time. To guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RFNN. Simulated and experimental results due to periodic sinusoidal command show that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties     

On Sensorless Induction Motor Drives: Sliding-Mode Observer and Output Feedback Controller

In this paper, a sensorless output feedback controller is designed in order to drive the induction motor (IM) without the use of flux and speed sensors. First, a new sliding-mode observer that uses only the measured stator currents is synthesized to estimate the speed, flux, and load torque. Second, a current-based field-oriented sliding-mode control is developed so as to steer the estimated speed and flux magnitude to the desired references. A stability analysis based on the Lyapunov theory is also presented in order to guarantee the closed-loop stability of the proposed observer-control system. Two experimental results for a 1.5-kW IM are presented and analyzed by taking into account the unobservability phenomena of the sensorless IM.      

Real-time IP position controller design with torque feedforwardcontrol for PM synchronous motor

A digital signal processor (DSP)-based permanent magnet (PM) synchronous motor (SM) drive with a proposed recursive least-square (RLS) estimator and real-time integral-proportional (IP) position controller is introduced in this study. First, the rotor inertia constant, the damping constant, and the disturbed load torque of the synchronous motor are estimated by the proposed RLS estimator, which is composed of an RLS estimator and a torque observer. Next, the IP position controller is real-time designed according to the estimated rotor parameters, to match the time-domain command tracking specifications. Then, the observed disturbance torque is fed forward, to increase the robustness of the synchronous motor drive     

Recurrent-neural-network-based adaptive-backstepping control for induction servomotors

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.     

Recurrent-Fuzzy-Neural-Network-Controlled Linear Induction Motor Servo Drive Using Genetic Algorithms

A recurrent fuzzy neural network (RFNN) controller based on real-time genetic algorithms (GAs) is developed for a linear induction motor (LIM) servo drive in this paper. First, the dynamic model of an indirect field-oriented LIM servo drive is derived. Then, an online training RFNN with a backpropagation algorithm is introduced as the tracking controller. Moreover, to guarantee the global convergence of tracking error, a real-time GA is developed to search the optimal learning rates of the RFNN online. The GA-based RFNN control system is proposed to control the mover of the LIM for periodic motion. The theoretical analyses for the proposed GA-based RFNN controller are described in detail. Finally, simulated and experimental results show that the proposed controller provides high-performance dynamic characteristics and is robust with regard to plant parameter variations and external load disturbance     

Sliding-mode control of a nonlinear-input system: application to amagnetically levitated fast-tool servo

Magnetic servo levitation (MSL) is currently being investigated as an alternative to drive fast-tool servo systems that could overcome the range limitations inherent to piezoelectric driven devices while operating over a wide bandwidth. To control such systems, a feedback-linearized controller coupled with a Kalman filter has been previously described. Performance limitations that degrade tracking accuracy suggest the use of a more robust controller design approach, such as sliding-mode control. Current literature on sliding mode deals almost exclusively with systems that are affine on the input, while the magnetic fast-tool servo is nonlinear on it when the control action is current command. This paper discusses a sliding mode-based controller that overcomes the aforementioned problem by defining a modified sliding condition to calculate control action. Experimental results demonstrate the feasibility of achieving long-range fast tracking with magnetically levitated devices by using sliding-mode control     

Adaptive positioning control for a LPMSM drive based on adapted inverse model and robust disturbance observer

The adaptive robust positioning control for a linear permanent magnet synchronous motor drive based on adapted inverse model and robust disturbance observer is studied in this paper. First, a model following two-degrees-of-freedom controller consisting of a command feedforward controller (FFC) and a feedback controller (FBC) is developed. According to the estimated motor drive dynamic model and the given position tracking response, the inner speed controller is first designed. Then, the transfer function of FFC is found based on the inverse model of inner speed closed-loop and the chosen reference model. The practically unrealizable problem possessed by traditional feedforward control is avoided by the proposed FFC. As to the FBC, it is quantitatively designed using reduced plant model to meet the specified load force regulation control specifications. In dealing with the robust control, a disturbance observer based robust control scheme and a parameter identifier are developed. The key parameters in the robust control scheme are designed considering the effect of system dead-time. The identification mechanism is devised to obtain the parameter uncertainties from the observed disturbance signal. Then by online adapting the parameters set in the FFC according to the identified parameters, the nonideal disturbance observer based robust control can be corrected to yield very close model following position tracking control. Meanwhile, the regulation control performance is also further improved by the robust control. In the proposed identification scheme, the effect of a nonideal differentiator in the accuracy of identification results is taken into account, and the compromise between performance, stability, and control effort limit is also considered in the whole proposed control scheme.     

Evaluating generalized predictive control for a brushless DC drive

This paper proposes a new control approach for a brushless DC motor drive using the generalized predictive control (GPC) algorithm. Based on the same least-squares framework as in the controller design, we further develop the method to design an observer. The GPC algorithm uses the receding horizon approach whereby the control signals are determined by minimizing a quadratic cost function. Our study shows that the rise time and settling time of the servo system have an approximate linear relationship with the prediction horizon. Thus, it is used to tune the controller of the drive. Moreover, the control weighting factor can be used to smooth the controller output. The proposed algorithm has been implemented using a digital signal processor (DSP) and tested in real time with a prototype system. The performance and robustness of the algorithms have been evaluated both in simulation and experiment. The results show that the drive performs reasonably well despite load changes and step changes in the position setpoint. Furthermore, it is fairly robust against motor parameters change     

Development of new training algorithms for neuro-wavelet systems on the robust control of induction servo motor drive

A robust wavelet neural network control (RWNNC) system is proposed to control the rotor position of an induction servo motor drive in this paper. In the proposed RWNNC system, a wavelet neural network controller is the main tracking controller that is used to mimic a computed torque control law, and a robust controller is designed to recover the residual approximation for ensuring the stable control performance. Moreover, to relax the requirement for a known bound on lumped uncertainty, which comprises a minimum approximation error, optimal network parameters and higher order terms in a Taylor series expansion of the wavelet functions, an RWNNC system with adaptive bound estimation was investigated for the control of an induction servo motor drive. In this control system, a simple adaptive algorithm was utilized to estimate the bound on lumped uncertainty. In addition, numerical simulation and experimental results due to periodic commands show that the dynamic behaviors of the proposed control systems are robust with regard to parameter variations and external load disturbance.     

Design and Implementation of a Robust Current-Control Scheme for a PMSM Vector Drive With a Simple Adaptive Disturbance Observer

This paper introduces a robust current-control scheme for a permanent-magnet synchronous motor (PMSM) with a simple adaptive disturbance observer. The robust controller is realized by including an adaptive element in the reference-voltage-generation stage using the feedforward control. Due to the time-varying nature and the high-bandwidth property of the uncertainties in a practical PMSM drive system, the adaptive element is simply chosen as the estimated uncertainty function, which adaptively varies with different operating conditions. Subsequently, the frequency modes of the uncertainty function are embedded in the control effort, and a robust current-control performance is yielded. Furthermore, the inclusion of the estimated uncertainty function provides an efficient solution for torque-ripple minimization in PMSM drives. This is because the frequency modes of the disturbances to be eliminated, i.e., the flux harmonics, are included in the stable closed-loop system. To provide a high-bandwidth estimate of the uncertainty function, a simple adaptation law is derived using the nominal current dynamics and the steepest descent method. To guarantee the system's convergence and to properly tune the proposed observer, a stability analysis based on a discrete-time Lyapunov function has been used. Comparative evaluation experiments are presented to demonstrate the effectiveness of the proposed control scheme under different operating conditions.     

Robust control of the DC–DC boost converter based on the uncertainty and disturbance estimator

In this paper, a robust non-linear controller based on the uncertainty and disturbance estimator (UDE) scheme is successfully developed and implemented for the output voltage regulation of the DC–DC boost converter. System uncertainties, external disturbances and unknown non-linear dynamics are lumped as a signal that is accurately estimated using a low-pass filter and their effects are cancelled by the controller. This methodology forms the basis of the UDE-based controller. A simple procedure is also developed that systematically determines the parameters of the controller to meet certain specifications. Using simulation, the effectiveness of the proposed controller is compared against the sliding-mode control (SMC). Experimental tests also show that the proposed controller is robust to system uncertainties, large input and load perturbations.     

A robust speed controller for induction motor drives

A speed controller considering the effects of parameter variations and external disturbance for indirect field-oriented induction motor drives is proposed in this paper. First a microprocessor-based indirect field-oriented induction motor drive is implemented and its dynamic model at nominal case is estimated. Based on the estimated model, an integral plus proportional (IP) controller is quantitatively designed to match the prescribed speed tracking specifications. Then a dead-time compensator and a simple robust controller are designed and augmented to reduce the effects of parameter variations and external disturbances. The desired speed tracking control performance of the drive can be preserved under wide operating range, and good speed load regulating performance can also be obtained. Theoretic basis and implementation of the proposed controller are detailedly described. Some simulated and experimental results are provided to demonstrate the effectiveness of the proposed controller     

Adaptive backstepping control using recurrent neural network forlinear induction motor drive

An adaptive backstepping control system using a recurrent neural network (RNN) is proposed to control the mover position of a linear induction motor (LIM) drive to compensate the uncertainties including the friction force in this paper. First, the dynamic model of an indirect field-oriented LIM drive is derived. Then, a backstepping approach is proposed to compensate the uncertainties including the friction force occurred in the motion control system. With the proposed backstepping control system, the mover position of the LIM drive possesses the advantages of good transient control performance and robustness to uncertainties for the tracking of periodic reference trajectories. Moreover, to further increase the robustness of the LIM drive, an RNN uncertainty observer is proposed to estimate the required lumped uncertainty in the backstepping control system. In addition, an online parameter training methodology, which is derived using the gradient-descent method, is proposed to increase the learning capability of the RNN. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results     

A Globally Stable, Load-Independent Pressure Observer for the Servo Control of Pneumatic Actuators

Pneumatic actuators are governed by nonlinear dynamics. Thus, robust precision motion control of pneumatic systems requires model-based control techniques such as sliding-mode and/or adaptive control. These controllers typically require full-state knowledge of the system, i.e., pressure, position, velocity, and acceleration. For measuring pressure states, pneumatic servo systems require two expensive pressure sensors per axis, and hence, it makes the system economically noncompetitive with most electromagnetic types of actuation. This paper presents the development of a Lyapunov-based pressure observer for pneumatically actuated systems. The pressure observer is energy-based and has the useful feature of not requiring a model for the load of the system, i.e., it is load-independent. This pressure observer is proven to be globally stable with the added feature of having a response bandwidth equal to that of the modeled pressure dynamics. A robust observer-based controller is developed to obtain a low-cost precision pneumatic servo system. Experimental results are presented that demonstrate and validate the effectiveness of the proposed observer.     

Design and Performance Tuning of Sliding-Mode Controller for High-Speed and High-Accuracy Positioning Systems in Disturbance Observer Framework

The tuning method of controllers can be used for effectively determining the overall performance of positioning systems. In particular, this method is highly effective in the case of high-speed and high-accuracy positioning systems. In this paper, a sliding-mode controller that uses one of the well-known approaches of robust control methodology is designed for high-speed positioning systems that require a high-accuracy performance. A performance-tuning method based on a disturbance observer (DOB) structure is also proposed. First, a generalized disturbance attenuation framework named robust internal-loop compensator (RIC) is introduced, and a sliding-mode controller based on a Lyapunov redesign is analyzed in the RIC framework. Then, the DOB properties of the sliding-mode controller are presented, and it is shown that the performance of the closed-loop system with a sliding-mode controller can be tuned up by using the structural characteristics of the DOB. These results make the design of an enhanced sliding-mode controller possible. Finally, the proposed algorithm is experimentally verified and discussed with two positioning systems. Experimental results show the effectiveness and the robustness of the proposed scheme.     

Sliding-mode-controlled slider-crank mechanism with fuzzy neuralnetwork

The dynamic response of a sliding-mode-controlled slider-crank mechanism, which is driven by a permanent-magnet (PM) synchronous servo motor, is studied in this paper. First, a position controller is developed based on the principles of sliding-mode control. Moreover, to relax the requirement of the bound of uncertainties in the design of a sliding-mode controller, a fuzzy neural network (FNN) sliding-mode controller is investigated, in which a FNN is adopted to adjust the control gain in a switching control law on line to satisfy the sliding mode condition. In addition, to guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the FNN. Numerical and experimental results show that the dynamic behaviors of the proposed controller-motor-mechanism system are robust with regard to parametric variations and external disturbances. Furthermore, compared with the sliding-mode controller, smaller control effort results and the chattering phenomenon is much reduced by the proposed FNN sliding-mode controller