Adjustable speed control of ultrasonic motors by adaptive control

The driving principle of the ultrasonic motor (USM) is different from those of the electro-magnetic type motors. Some mathematical models for the USM have been reported; however, these models are very complex to apply for speed control of the USM. Therefore, the speed controllers have been designed using PI controllers or fuzzy controllers and it is necessary to develop a simple and convenient mathematical model for the USM in order to achieve a high-performance speed control. In this paper, a mathematical model for the USM is proposed which is simple and useful for speed control. The speed controller is designed based on the model using adaptive control theory. Adaptive control is attractive for control of the USM because the speed characteristics of the USM vary with drive conditions. The application of this control scheme to speed control for the USM is attempted first. The effectiveness of proposed control is demonstrated by experimental     

Robust nonlinear control of brushless DC motors for direct-driverobotic applications

The control problem associated with brushless DC motors (BLDCMs) for direct-drive robotic applications is considered. In order to guarantee the high-performance operation of BLDCMs in such applications, the effects of reluctance variations and magnetic saturation are accounted for in the model. Such a BLDCM model constitutes a highly coupled and nonlinear dynamic system. Using the transformation theory of nonlinear systems, a feedback control law, which is shown to compensate for the system nonlinearities, is derived. Conditions under which such a control law is possible are presented. The need for the derivation of explicit commutation strategies is eliminated, resulting in reduction of the computations involved. To guarantee the high-performance operation of the system under substantial uncertainties, a robust control law is derived and appended to the overall control structure. The inclusion of the robust controller results in good tracking performance when there are modeling and measurement errors and payload uncertainties. The efficacy of the overall control law is investigated by considering a single-link direct-drive arm actuated by a BLDCM     

Robust adaptive control of a quadrotor helicopter

This work presents a direct approximate-adaptive control, using CMAC nonlinear approximators, for an experimental prototype quadrotor helicopter. The method updates adaptive parameters, the CMAC weights, as to achieve both adaptation to unknown payloads and robustness to disturbances. Previously proposed weight-update methods, such as e-modification, provide robustness by simply limiting weight growth. In order to let the weights grow large enough to compensate unknown payloads, the proposed method relies on a set of alternate weights to guide the training. The alternate weights produce nearly the same output, but with values clustered closer to the average weight so that the output remains relatively smooth. This paper describes the design of a prototype helicopter suitable for testing the control method. In the experiment the new method stops weight drift during a shake test and adapts on-line to a significant added payload, whereas e-modification cannot do both.     

Robust adaptive control: a unified approach

A complete tutorial review of the entire field is presented, beginning with simple instability examples to identify the causes of nonrobust behavior in adaptive control. Some of the mathematical groundwork is presented, and the theory for the design and analysis of adaptive laws is developed. Commonly used adaptive controller structures are discussed, highlighting their particular robustness properties. Particular attention is paid to model reference, pole placement, and linear quadratic controller structures. Designs and analyses of model reference, pole placement, and linear quadratic controllers, based on combining the corresponding controller structures with the various robust adaptive laws, are presented. Suggestions for future research are given     

Torque and current control of high-speed motion control systems with sinusoidal-PMAC motors

Describes a torque- and current-control application to a commercial motion-control system with sinusoidal permanent magnet ac (PMAC) motors. The control approach is based on maximization of torque-per-amp ratio. The proposed torque controller, in the form of torque feedforward plus proportional-integral (PI)-type torque feedback, utilizes the feedback of nominal torque signal only, a signal that can be readily calculated, online. No torque sensor is required. Through proper design of the desired nominal torque with adaptive control, the proposed torque controller can overcome disturbances due to torque estimation error and model uncertainties. A discrete-time approach is developed for inner-current loop control design. The inner-loop control gains, which are hard to obtain through manual tuning in practice, are determined by a dynamic model-based calculation methodology. Experimental evaluation on a commercial motion control system demonstrates the validity of the proposed approach in high-speed motions.     

Nonlinear adaptive control of direct-drive brushless DC motors andapplications to robotic manipulators

Robust adaptive nonlinear control of brushless DC (BLDC) motors is considered. A controller is designed for the plant that is robust to parametric and dynamic uncertainties in the entire electromechanical system. These uncertainties are shown to be bounded by polynomials in the states. In addition, the controller can reject any bounded unmeasurable disturbances entering the system. A model for the motor incorporating magnetic saturation is used to design voltage-level control inputs for the motor. The design methodology is based on our earlier work on adaptive control of nonlinear systems. The overall stability of the system is shown using Lyapunov techniques. The tracking error is shown to be globally uniformly bounded. The design procedure is shown to be also applicable to multilink manipulators actuated by BLDC motors. The performance of the controller is verified through simulations and comparisons with a proportional-integral-derivative-type controller are made     

Robust control of permanent magnet motors: VSS techniques lead tosimple hardware implementations

It is shown how very simple velocity-tracking robust controllers for permanent magnet motors driving nonlinear loads can be designed based on variable structure systems techniques. Very fast dynamics, accurate and robust velocity-tracking are achieved with very simple hardware components without resorting to powerful digital signal processors and related interface hardware. A cascade control structure is used to ensure maximum flexibility. The controller for a DC motor is considered in great detail. Extension to AC synchronous PM motors is also presented. At the different control levels robustness is addressed with specific algorithms and the simplest solution is always selected. The controller architecture for both DC and AC synchronous motor are presented and discussed in the paper. Experimental results related to the control of a DC motor driving a nonlinear load are also shown. They demonstrate feasibility and excellent performances of the proposed approach     

Robust vector control of induction motors using full-order observer in consideration of core loss

This paper proposes a method for designing a robust full-order observer for vector-controlled induction motors taking core loss into account. Although conventional research focuses on parameter identification, global stability of the identification remains questionable. Therefore, robustness against some parameters is required. This paper describes the design of a robust full-order observer which takes core loss into account, using both the gain-scheduled H/sub /spl infin// control and the linear matrix inequality technique. This design always results in a stable controller. The robustness of the proposed method against variations of resistances is evaluated by experiments.     

Robust adaptive sliding-mode control using fuzzy modeling for aninverted-pendulum system

In this paper, a new robust adaptive control architecture is proposed for operation of an inverted-pendulum mechanical system. The architecture employs a fuzzy system to adaptively compensate for the plant nonlinearities and forces the inverted pendulum to track a prescribed reference model. When matching with the model occurs, the pendulum will be stabilized at an upright position and the cart should return to its zero position. The control scheme has a sliding control input to compensate for the modeling errors of the fuzzy system. The gain of the sliding input is automatically adjusted to a necessary level to ensure the stability of the overall system. Global asymptotic stability of the algorithm is established via Lyapunov's stability theorem. Experiments on an inverted-pendulum system are given to show the effectiveness of the proposed control structure     

New sensorless control for brushless DC motors using disturbanceobservers and adaptive velocity estimations

A brushless DC motor has been represented by a nonlinear equation. Therefore, it has been difficult to apply linear control theory to brushless DC motor systems. In this paper, to apply the linear control theory to brushless DC motor systems, the authors propose considering the nonlinear term of the equation as a disturbance and to realize a sensorless control of the brushless DC motor using both the disturbance observer and the adaptive velocity estimation. With proper pole locations of the disturbance observer, stability of the position estimation is guaranteed, and stability of the adaptive velocity estimation is also guaranteed by Popov's hyperstability theory. The experimental results show that the proposed method is very useful     

Robust adaptive tracking control of uncertain electrostatic micro-actuators with H-infinity performance

A novel adaptive robust tracking control scheme is proposed for a class of single-degree-of-freedom (1DOF) electrostatic micro-actuator systems in the presence of parasitics, parameter uncertainties and external disturbances. This method integrates the adaptive dynamic surface control and H-infinity control techniques. Based on this method, both the design procedure and the derived tracking controller itself are simplified, and the controller guarantees that the output tracking error satisfies the H-infinity tracking performance. In addition, the tracking accuracy can be adjusted by an appropriate choice of the design parameters of the controller. Simulation results show that prescribed transient output tracking performance can be achieved, and the closed-loop system exhibits good robustness to system uncertainties.     

Robust adaptive control of a three-axis motion Simulator with state observers

The development of a nonlinear robust adaptive tracking control system for a three-axis motion simulator is presented in this paper. The motion simulator is used to test and calibrate certain spacecraft instruments within a hardware-in-the-loop environment. Permanent magnet synchronous motor (PMSM) drives are used as simulator actuators. The control system is developed based on Lyapunov stability theory for which only rotor position and stator current signals are required. By using mechanical and electrical state observers, the measurement of acceleration and load torque which is usually required when motor dynamics are considered, is avoided. The control system can be made adaptable to constant unknown motor parameters and load inertia and robust to unknown but bounded fast varying disturbances. Simulation and experimental results are presented to verify the stability and efficacy of the proposed control system.     

Robust torque optimization for BLDC spindle motors

A robust design for torque optimization of brushless DC spindle motors applied in a hard disk drive, using the Taguchi method, is described and illustrated in this paper. The optimal design process takes into consideration noises that arise in the manufacturing process, such as manufacturing tolerances for the stator tooth shape and variation of the rotor magnet magnetization distribution due to the magnetization fixture and process. The objective of the optimal design using the combined Taguchi's design of experiment (DOE) and finite-element analysis (FEA) approach is to ensure that the spindle motor torque performance is insensitive to the noise, with moderate computational effort. The optimization is realized by a simulation and analysis tool that integrates Taguchi's DOE with the FEA. In this paper, the design optimization process is described and the results are presented     

Multirate adaptive robust control for discrete-time non-minimum phase systems and application to linear motors

It is well known that a plant becomes non-minimum phase in discrete-time domain when the relative degree of the original continuous-time plant is greater than 2 even if the plant is minimum-phase in continuous-time domain. Thus, it was difficult to apply the conventional adaptive controllers directly to these systems. In this paper, multirate adaptive robust control (MARC) is proposed for these systems. This scheme is developed by the good combination of perfect tracking control (PTC) with multirate feedforward control which has been proposed by the first author and discontinuous projection based adaptive robust control (ARC) which has been proposed by the second author. Although the original PTC can assure perfect tracking only for nominal plant, the proposed MARC can guarantee: 1) perfect tracking for plant with parametric uncertainty and 2) overall stability even if there exist modeling error and disturbance. The proposed scheme is applied to the high-speed position control of a linear motor, and the advantages are demonstrated through experiments.     

Optimizing current control performance in double windingasynchronous motors in large power inverter drives

The operation of AC drives in applications requiring high overload capability imposes hard working conditions on the electronic switching devices. The use of induction motors with two stator windings, fed by two inverter modules, allows large power rating in variable speed drives for high performance applications. This paper shows the structure and the main features of a field-oriented control for a double winding motor, fed by two GTO inverters. The operational results of an 850 kW drive, working as a melt pump in a polyethylene plant, are reported in order to describe the obtained performances     

Self-tuning adaptive neurocontroller for brushless DC motors

This paper describes a self-tuning adaptive neurocontroller for brushless DC motors. Nonlinear and unknown motor dynamics are identified by using a multilayer neural network and the control input for the motor is derived from the identified model. The effect of the load torque on the control system is damped by filtering the control input. Simulation and experimental results show that the self-tuning adaptive neurocontrol has a good tracking performance but needs an adaptive filter and a parallel PI controller in the case of disturbances.     

顽健自适应波束形成算法

针对方向向量偏差会导致最小均方(LMS)算法的性能急剧下降这一问题,提出了一种基于可变对角载入的顽健自适应波束形成算法.采用最陡下降法对信号方向向量进行优化求解,并在每次迭代过程中更新对角载入值,进而求出最优的权重向量,避免了矩阵求逆运算和特征值分解运算,大大降低了计算复杂度.通过建立步长与输入信号的关系得到可变的步长因子,克服了收敛速度和稳态误差之间的矛盾.该算法收敛速度快,抗扰动性强,对信号方向向量偏差具有很强的顽健性,从而改善了阵列输出的信干噪比,使其更接近最优值.理论分析和仿真结果表明与传统自适应波束形成算法相比,所提顽健算法具有更好的性能.     

Robust M-estimate adaptive filtering

An M-estimate adaptive filter for robust adaptive filtering in impulse noise is proposed. Instead of using the conventional least-square cost function, a new cost function based on an M-estimator is used to suppress the effect of impulse noise on the filter weights. The resulting optimal weight vector is governed by an M-estimate normal equation. A recursive least M-estimate (RLM) adaptive algorithm and a robust threshold estimation method are derived for solving this equation. The mean convergence performance of the proposed algorithm is also analysed using the modified Huber (1981) function (a simple but good approximation to the Hampel's three-parts-redescending M-estimate function) and the contaminated Gaussian noise model. Simulation results show that the proposed RLM algorithm has better performance than other recursive least squares (RLS) like algorithms under either a contaminated Gaussian or alpha-stable noise environment. The initial convergence, steady-state error, robustness to system change and computational complexity are also found to be comparable to the conventional RLS algorithm under Gaussian noise alone     

Robust Huber adaptive filter

Classical filtering methods are not optimal when the statistics of the signals violate the underlying assumptions behind the theoretical development. Most of the classical filtering theory like least-squares filtering assumes Gaussianity as its underlying distribution. We present a new adaptive filter that is optimal in the presence of Gaussian noise and robust to outliers. This novel robust adaptive filter minimizes the Huber objective function. An estimator based on the Huber objective function behaves as an L₁ norm estimator for large residual errors and as an L₂ norm estimator for small residual errors. Simulation results show the improved performance of the Huber adaptive filter (configured as a line enhancer) over various nonlinear filters in the presence of impulsive noise and Gaussian noise     

一种不依赖状态估计的自适应输出反馈控制方法

研究了一种不依赖状态估计的自适应输出反馈控制方法。首先引入伪控制信号使系统反馈线性化，然后设计一个线性动态补偿器和在线神经网络，自适应消除不确定性和建模所引起的误差。对一含有末建模动态的非线性系统的仿真结果表明，该控制方法能够消除系统的稳态误差，提高系统动态性能，并且具有较好的鲁棒性。