A combined backstepping and dynamic surface control to adaptive fuzzy state‐feedback control

This paper studies an adaptive fuzzy dynamic surface control for a class of nonlinear systems with fuzzy dead zone, unmodeled dynamics, dynamical disturbances, and unknown control gain functions. The unknown system functions are approximated by the Takagi‐Sugeno–type fuzzy logic systems. There are 3 main features for the presented systematic design scheme. First, by adopting an integrated method, a novel adaptive fuzzy controller is constructed for the nonlinear system with fuzzy dead zone. Second, only 3 online learning parameters need to be tuned, which significantly reduces the computation burden. Third, the possible controller singularity problem in some of the existing adaptive control methods with feedback linearization techniques can be avoided. On the basis of the backstepping technique and dynamic surface control, all the signals of the closed‐loop system are guaranteed to be semiglobally uniformly ultimately bounded. Finally, 2 simulation examples are provided to illustrate the effectiveness of the proposed scheme.     

Position sensorless control for ultrasonic motors based on input voltage information

This paper presents a method of position sensorless control of ultrasonic motors (USM). The position sensorless control method is useful for reducing the cost and size of drive system. The position estimation method is based on the input voltage information of the ultrasonic motor. The characteristic of input voltage versus rotor position is expressed by a quadratic function which is used to estimate a rotor position based on input voltage information. This estimation method, which uses only the drive voltage of the ultrasonic motor, is a very simple algorithm and shows good estimation ability. The parameters of the quadratic function are adjusted by using the recursive least squares method and are obtained on‐line. Since it can correspond to parameter change, the estimated rotor position shows good agreement with the measured rotor position against load torque and motor temperature changes. Sensorless position control is achieved by using the estimated rotor position instead of the measured rotor position. The validity of the proposed method is confirmed by experimental results. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 163(1): 57–64, 2008; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20626     

Prescribed performance adaptive fuzzy control for nonstrict-feedback nonlinear systems with dead zone outputs

This paper presents an adaptive fuzzy control scheme for a class of nonstrict-feedback nonlinear systems with dead zone outputs and prescribed performance. By utilizing the monotonically increasing property of system bounding functions and the Nussbaum function, the design difficulties caused by the nonstrict-feedback structure and dead zone output are overcome. Combining backstepping technique with prescribed performance algorithm, a feasible adaptive fuzzy controller is designed to guarantee the boundedness of all signals of the closed-loop system and the prescribed tracking performance of the system. Finally, simulation results are depicted to illustrate the effectiveness of the proposed control approach.     

Prescribed performance adaptive fuzzy dynamic surface control of nonaffine time‐varying delayed systems with unknown control directions and dead‐zone input

In this paper, an adaptive prescribed performance control method is presented for a class of uncertain strict feedback nonaffine nonlinear systems with the coupling effect of time‐varying delays, dead‐zone input, and unknown control directions. Owing to the universal approximation property, fuzzy logic systems are used to approximate the uncertain terms in the system. Since there is no systematic approach to determine the required upper bounds of errors in control systems, the prior selection of control parameters to have a satisfactory performance is somehow impossible. Therefore, the prescribed performance technique as a solution is applied in this study to bring satisfactory performance indices to the system such as overshoot and steady state performance within a predetermined bound. Dynamic surface control strategy is also introduced to the proposed control scheme to address the “explosion of complexity” behavior existing in conventional backstepping methods. To ease the control design, the mean‐value theorem is utilized to transform the nonaffine system into the affine one. Moreover, with the help of this theorem, the unknown dead‐zone nonlinearity is separated into the linear and nonlinear disturbance‐like bounded term. The proposed method relaxes a prior knowledge of control direction by employing Nussbaum‐type functions, and the effect of time‐varying delays are compensated by constructing the proper Lyapunov‐Krasovskii functions. The proposed controller guarantees that all the closed‐loop signals are semiglobally uniformly ultimately bounded and the error evolves within the decaying prescribed bounds. In the end, in order to demonstrate the superiority of this method, simulation examples are given.     

Control of robots using radial basis function neural networks with dead‐zone

In this paper, we examine the control of robot manipulators utilizing a Radial Basis Function (RBF) neural network. We are able to remove the typical requirement of Persistence of Excitation (PE) for the desired trajectory by introducing an error minimizing dead‐zone in the learning dynamics of the neural network. The dead‐zone freezes the evolution of the RBF weights when the performance error is within a bounded region about the origin. This guarantees that the weights do not go unbounded even if the PE condition is not imposed. Utilizing protection ellipsoids we derive conditions on the feedback gain matrices that guarantee that the origin of the closed loop system is semi‐globally uniformly bounded. Simulations are provided illustrating the techniques. Copyright © 2011 John Wiley & Sons, Ltd.     

Sliding mode disturbance observer‐based adaptive control for uncertain MIMO nonlinear systems with dead‐zone

In this paper, an adaptive integral sliding mode control (ISMC) scheme is developed for a class of uncertain multi‐input and multi‐output nonlinear systems with unknown external disturbance, system uncertainty, and dead‐zone. The research is motivated by the fact that the ISMC scheme against unknown external disturbance and system uncertainty is very important for multi‐input and multi‐output nonlinear systems. The system uncertainty, the unknown external disturbance, and the effect of dead‐zone are integrated as a compounded disturbance, which is well estimated using a sliding mode disturbance observer (SMDO). Then, the adaptive ISMC based on the designed SMDO is presented to guarantee the satisfactory tracking performance in the presence of system uncertainty, external disturbance, and dead‐zone. Finally, the designed adaptive ISMC strategy based on SMDO is applied to the attitude control of the near space vehicle, and simulation results are presented to illustrate the effectiveness of the proposed adaptive ISMC scheme using the SMDO. Copyright © 2016 John Wiley & Sons, Ltd.     

Neural‐hybrid control of systems with sandwiched dead‐zones

The control of systems that have sandwiched nonsmooth nonlinearities, such as a dead‐zone sandwiched between two dynamic blocks, is addressed. An adaptive inverse control scheme using a hybrid controller structure and a neural network based inverse compensator, is proposed for such systems with unknown sandwiched dead‐zone. This neural‐hybrid controller consists of an inner loop discrete‐time feedback structure incorporated with an adaptive inverse using a neural network for the unknown dead‐zone, and an outer‐loop continuous‐time feedback control law for achieving desired output tracking. The dead‐zone compensator consists of two neural networks, one used as an estimator of the sandwiched dead‐zone function and the other for the compensation itself. The compensator neural network has neurons that can approximate jump functions such as a dead‐zone inverse. The weights of the two neural networks are tuned using a modified gradient algorithm. Simulation results are given to illustrate the performance of the proposed neural‐hybrid controller. Copyright © 2002 John Wiley & Sons, Ltd.     

孤岛光伏并联逆变器系统的固定时间模糊反步控制策略

针对孤岛光伏并联逆变器系统存在固有的建模不确定性以及动态性能差的问题,提出一种固定时间模糊反步控制策略。首先,将滤波器参数和输出电流作为未知项,建立含有滤波器参数摄动和输出电流扰动的单台逆变器等效数学模型。其次,将固定时间Lyapunov稳定性原理、模糊逻辑控制理论和反步控制理论相结合,利用固定时间模糊反步控制器逼近系统中的未知项,以改善逆变器的输出电压。然后,基于严格的Lyapunov定理证明所提控制策略下的系统是固定时间稳定的,且稳定时间的上界与系统的初始状态无关。最后,通过两台20 kW样机验证了所提控制策略的有效性。     

Robust output tracking of uncertain nonlinear time‐delay systems with unknown dead‐zone inputs via control schemes with a simple structure

The adaptive robust output tracking control problem is considered for a class of uncertain nonlinear time‐delay systems with completely unknown dead‐zone inputs. A new design method is proposed so that some adaptive robust output tracking control schemes with a rather simple structure can be constructed. It is not necessary to know the nonlinear upper bound functions of uncertain nonlinearities. In fact, the constructed output tracking control schemes are structurally linear in the state and have a self‐tuning control gain function that is updated by an adaptation law. In this paper, the dead‐zone input is nonsymmetric, and its information is assumed to be completely unknown. In addition, a numerical example is given to describe the design procedure of the presented method, and the simulations of this numerical example are implemented to demonstrate the validity of the theoretical results.     

Direct adaptive neural tracking control for a class of stochastic pure‐feedback nonlinear systems with unknown dead‐zone

This paper considers the problem of adaptive neural tracking control for a class of nonlinear stochastic pure‐feedback systems with unknown dead zone. Based on the radial basis function neural networks' online approximation capability, a novel adaptive neural controller is presented via backstepping technique. It is shown that the proposed controller guarantees that all the signals of the closed‐loop system are semi‐globally, uniformly bounded in probability, and the tracking error converges to an arbitrarily small neighborhood around the origin in the sense of mean quartic value. Simulation results further illustrate the effectiveness of the suggested control scheme. Copyright © 2012 John Wiley & Sons, Ltd.     

A new voltage control equipment using fuzzy inference

Power system control equipment needs higher sensitivity and operational reliability. Advanced voltage control equipment is needed for reducing the frequency of tap changes and improving the characteristics (the relationship between the actual voltage and reference voltage) of the voltage to meet today's power system requirements. However, these objectives are in a trade-off relationship. Studies of voltage control derived from a knowledge base suitable for electric power systems can satisfy these objectives using fuzzy inference. Compared with corresponding conventional equipment, the new equipment improved the deviation of 30 min average voltage of 30 percent. This paper describes the design concept of new voltage control equipment using fuzzy inference. In addition, field test results are described along with rules of fuzzy inference, membership functions, and the deviation of 30 min average voltage through detailed simulation.     

Adaptive fuzzy output feedback backstepping control of pure‐feedback nonlinear systems via dynamic surface control technique

In this paper, an adaptive fuzzy backstepping dynamic surface control approach is considered for a class of uncertain pure‐feedback nonlinear systems with immeasurable states. Fuzzy logic systems are first employed to approximate the unknown nonlinear functions, and then an adaptive fuzzy state observer is designed to estimate the immeasurable states. By the combination of the adaptive backstepping design with a dynamic surface control technique, an adaptive fuzzy output feedback backstepping control approach is developed. It is proven that all the signals of the resulting closed‐loop system are semi‐globally uniformly ultimately bounded, and the observer and tracking errors converge to a small neighborhood of the origin by choosing the design parameters appropriately. Simulation examples are provided to show the effectiveness of the proposed approach. Copyright © 2012 John Wiley & Sons, Ltd.     

Precise position control of an ultrasonic motor using the PID controller combined with NN

The input‐output characteristic of an ultrasonic motor (USM) has nonlinear elements and changes with a temperature rise or fluctuation of load‐mass. Therefore, it is difficult to accomplish satisfactory control performance by using conventional PID controllers. In this paper, we propose a PID controller combined with a neural network (NN‐PID controller). In this design method, the controller gains consist of constant gains of the PID controller and variable gains of the NN controller. The weights of the NN are adjusted by the backpropagation method so that the control error can be minimized. This method does not require long learning time of the NN. The effectiveness of the proposed design method is confirmed by experiments using an existing USM. © 2003 Wiley Periodicals, Inc. Electr Eng Jpn, 146(3): 46–54, 2004; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10199     

双连杆柔性臂机器人的模糊补偿滑模控制

提出了宏微双重驱动双连杆柔性臂机器人递阶控制系统结构，下层为根据柔性臂刚性模态设计的实现关节角位置控制的滑模算法和压电陶瓷抑振器，上层为根据关节角误差信号和滑模函数变化趋势建立的模糊推理表用以确定对滑模控制律的修正。借助自动机模型分析了系统的稳定性，将控制律在包含柔性模态的完整柔性臂机器人模型上进行计算机仿真，取得了满意的结果。     

Adaptive backstepping flight control for a mini‐UAV

The paper presents the design of a mini‐unmanned aerial vehicle (UAV) attitude controller using the backstepping method. Starting from the nonlinear dynamic equations of the mini‐UAV, by using the backstepping method, the authors of this paper obtained the expressions of the elevator, rudder, and aileron deflections, which stabilize the UAV at each moment to the desired values of the attitude angles. The attitude controller controls the attitude angles, the angular rates, the angular accelerations, and other variables describing the UAV longitudinal and lateral motions. The designed controller has been implemented in Matlab/Simulink environment, and its effectiveness has been tested with a campaign of numerical simulations using data from the UAV flight tests. Copyright © 2012 John Wiley & Sons, Ltd.     

Refinement of inverter model considering dead‐time for performance improvement in predictive instantaneous current control

Model predictive instantaneous‐current control (MPIC), which was proposed in our earlier works, enables us to achieve better instantaneous current control using mathematical models of an inverter and permanent magnet synchronous motors (PMSMs). However, dead‐time to avoid short breakdown in the inverter is not usually considered in a general inverter model. Such an unmodeled part in the inverter model prevents accurate prediction of current evolution in motor systems based on the model predictive control. Therefore, in this paper, we analyze current response resulting from the dead‐time in the MPIC, and propose a refined inverter model considering the dead‐time so that control performance is improved. The effectiveness of the proposed method is verified through simulation and experiments. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于模糊神经网络的超声电机的位置控制

超声电机的优良特性使其得到越来越广泛的应用,但由于其时变和非线性特性,使电机的速度和位置控制成为研究热点。文中将模糊神经网络用于电机的位置控制中,模糊逻辑部分获得专家知识,神经网络部分调整参数,通过改变超声电机的驱动频率来实现精确的位置控制,并通过实验和仿真来验证这种方法的有效性。     

Robust output tracking of uncertain nonlinear systems with completely unknown dead‐zone input: A self‐tuning design approach

The main purpose of this paper is to propose a direct and simple approach, called a self‐tuning design approach, to dealing with any nonsymmetric dead‐zone input nonlinearity where its information is completely unknown. In order to describe the approach, the output tracking problem is considered for a class of uncertain nonlinear systems with any nonsymmetric dead‐zone input. First, a dead‐zone input is represented as a time‐varying input‐dependent function such that the considered dynamical system with dead‐zone input can be transfered into an uncertain nonlinear dynamical system subject to a linear input with time‐varying input coefficient. Then, by making use of the self‐tuning design approach, a class of adaptive robust output tracking control schemes with a rather simple structure is synthesized. Thus, the proposed direct and simple self‐tuning design approach can be easily understood by the engineering designers, and the resulting simple adaptive robust control schemes can be well implemented in most practical engineering control problems. By combining the proposed self‐tuning design approach with other control methods, one may expect to obtain a number of interesting results for a rather large class of uncertain nonlinear dynamical systems with dead‐zone in the actuators. Finally,the simulations of some numerical examples are provided to demonstrate the validity of the theoretical results. Copyright © 2016 John Wiley & Sons, Ltd.     

感应电机系统的非线性反推自适应控制

针对感应电机的动态特性,在电机参数未知的前提下,研究了磁链与转速的渐近跟踪特性和整个系统的全局稳定性问题.应用反推技术设计了自适应控制器,对所有参数进行估计,实现了电机对给定信号的输出跟踪控制,保证了整个系统的全局稳定性.系统的转子磁链与转速能渐近跟踪给定的参考信号,仿真结果验证了该控制策略的有效性.     

基于模糊推理的节假日负荷预测

由于节假日负荷成分与正常日有较大差异,加之样本较少,节假日短期负荷预测难度较大.而准确预测可以提高系统运行的可靠性和经济性.为了提高节假日负荷预测的精度,针对节假日负荷特点,利用相似日方法获得待预测日负荷归一化曲线,利用模糊推理方法对负荷水平年增长率进行调整.通过对实际负荷进行预报计算,结果表明预报精度较高,建议用于节假日短期负荷预测.