Observer‐based adaptive control for stochastic nonstrict‐feedback systems with unknown backlash‐like hysteresis

This paper studies an observer‐based adaptive fuzzy control problem for stochastic nonlinear systems in nonstrict‐feedback form. The unknown backlash‐like hysteresis is considered in the systems. In the design process, the unknown nonlinearities and unavailable state variables are tackled by introducing the fuzzy logic systems and constructing a fuzzy observer, respectively. By using adaptive backstepping technique with dynamic surface control technique, an adaptive fuzzy control algorithm is developed. For the closed‐loop system, the proposed controller can guarantee all the signals are 4‐moment semiglobally uniformly ultimately bounded. Finally, simulation results further show the effectiveness of the presented control scheme.     

Fuzzy bilinear state feedback control design based on TS fuzzy bilinear model for DC–DC converters

This paper proposes a fuzzy bilinear state feedback controller based on Tagaki–Sugeno (TS) fuzzy bilinear model for DC–DC converters. The DC–DC converters can be approximated to bilinear model via Taylor series expansion. For achieving the output voltage regulation, we formulate the extended system of bilinear DC–DC converter model, one of whose state variables is the error between the output voltage and the reference output voltage. The extended system can be described to TS fuzzy bilinear model. We design a fuzzy bilinear state feedback controller to track the reference output voltage. The closed loop system is globally asymptotically stable via the proposed controller. Lyapunov theory is employed to guarantee the stability of the closed loop system. Numerical examples are given to demonstrate the validity of our proposed control approach with comparative results.     

Output‐feedback model‐reference adaptive calibration for map‐based anti‐jerk control of electromechanical automotive clutches

It is well known that the map‐based control can reduce the computational burden of the automotive on‐board controller. This paper proposes an output‐feedback model‐reference adaptive control algorithm to calibrate the map‐based anti‐jerk controller for electromechanical clutch engagement. The algorithm can be used to adaptively construct a data‐driven fuzzy rule base without resorting to manual tuning, so that it can overcome the problem of conventional knowledge‐based fuzzy logic design, which involves strenuous parameter‐tuning work in the construction of calibration maps. To accurately define the consequent of each fuzzy rule for anti‐jerk control, an output feedback law for computing the reference trajectory of clutch engagement is developed to eliminate the discontinuous slip‐stick transition, whereas an adaptive controller is designed to track the reference trajectory and compensate the nonlinearity. The convergence of the proposed output‐feedback model‐reference adaptive control algorithm is analyzed. Simulation results indicate that the proposed method can successfully reduce the excessive vehicle jerk and frictional energy dissipation during clutch engagement as compared with the conventional knowledge‐based fuzzy logic controller without fine tuning.     

Adaptive fuzzy output‐feedback tracking control for switched stochastic pure‐feedback nonlinear systems

This paper considers the problem of adaptive fuzzy output‐feedback tracking control for a class of switched stochastic nonlinear systems in pure‐feedback form. Unknown nonlinear functions and unmeasurable states are taken into account. Fuzzy logic systems are used to approximate the unknown nonlinear functions, and a fuzzy observer is designed to estimate the immeasurable states. Based on these methods, an adaptive fuzzy output‐feedback control scheme is developed by combining the backstepping recursive design technique and the common Lyapunov function approach. It is shown that all the signals in the closed‐loop system are semiglobally uniformly ultimately bounded in mean square in the sense of probability, and the observer errors and tracking errors can be regulated to a small neighborhood of the origin by choosing appropriate parameters. Finally, a simulation result is provided to show the effectiveness of the proposed control method.     

一种新型的间接自适应模糊控制器

自适应模糊控制为复杂对象的控制提供了有效途径,引起控制领域的广泛关注。针对一类单输入单输出非线性不确定对象,利用Popov超稳定理论提出了一种新型的间接自适应模糊控制器设计方案。该方案首先采用对象模型构成理想的控制器,利用模糊系统的万能逼近特性构造若干模糊系统在线逼近未知的对象模型,然后将闭环系统转换为1个线性定常的前向环节和1个非线性时变的反馈环节组成的等效误差模型,通过Popov超稳定理论推导出稳定的参数自适应律。该方案能确保系统的输出渐近收敛到给定的参考信号,同时放宽了对最小逼近误差的限制,并且具有更广泛和灵活的参数调节形式。仿真结果验证了方案对非线性对象的有效性。     

Adaptive state‐feedback control with sensor failure compensation for asymptotic output tracking

This paper addresses a new adaptive output tracking problem in the presence of uncertain plant dynamics and uncertain sensor failures. A new unified nominal state‐feedback control law is developed to deal with various sensor failures, which is directly constructed by state sensor outputs. Such a new state‐feedback compensation control law is able to ensure the desired plant‐model matching properties under different failure patterns. Based on the nominal compensation control design, a new adaptive compensation control scheme is proposed, which guarantees closed‐loop signal boundedness and asymptotic output tracking. The new adaptive compensation scheme not only expands the sensor failures types that the system could tolerate but also avoids some signal processing procedures that the traditional fault‐tolerant control techniques are forced to encounter. A complete stability analysis and a representative simulation study are conducted to evaluate the effectiveness of the proposed adaptive compensation control scheme.     

Passivity‐based control for stabilization,regulation and tracking purposes of a class of nonlinear systems

In this paper, a new passivity‐based control (PBC) scheme based on state feedback is proposed in order to solve tracking, regulation and stabilization problems for a class of multi‐input multi‐output (MIMO) nonlinear systems expressed in the normal form, with time‐invariant parameters and locally bounded reference weakly minimum phase. For the proposed control scheme two new different state feedbacks, one non‐adaptive for the case when the system parameters are assumed to be known and the other adaptive for the case of unknown parameters, are developed. For the adaptive case it is assumed that the unknown parameters appear linearly in the equations. Analysis of the transient behaviour of the proposed control schemes is presented through the simulation of two examples. Copyright © 2006 John Wiley & Sons, Ltd.     

Adaptive fuzzy FTC design of nonlinear stochastic systems with actuator faults and unmodeled dynamics

This paper investigates an adaptive fuzzy control method for accommodating actuator faults in a class of uncertain stochastic nonlinear systems with both immeasurable states and unmodeled dynamics. The considered faults are modeled as both loss of effectiveness and lock‐in‐place. To deal with the immeasurable states, a novel state observer containing the actuator faults is designed. Combining with the backstepping technique and stochastic small‐gain theorem, an adaptive fuzzy output feedback control method is developed. The presented design scheme can guarantee that the closed‐loop system is input‐to‐state practically stable in probability. Finally, a simulation example is shown to verify the effectiveness of the proposed control method.     

基于状态反馈与重复控制的逆变器控制技术

针对PWM电压型逆变器提出了一种将带积分状态反馈与重复控制相结合的输出电压控制方案。状态反馈所需的变量为电容电压和电容电流。为避免采样、计算延时占用 PWM脉宽,以上反馈变量值均由状态观测器基于实测电容电压和负载电流值预测得到,使控制算法可以提前一拍进行,这样也避免了设置专门的电容电流传感器。由于积分控制的引入,改善了状态反馈控制的稳态精度,克服了早先提出的类似方案在加载时的电压跌落问题。居于控制系统最外层的重复控制器可以保证逆变器即使带非线性负载也能保持高度正弦的输出电压。实验表明该方案可同时实现高动态响应和高稳态波形精度,适用于UPS等需要高性能输出电压控制的场合。     

Slope seeking: a generalization of extremum seeking

This work introduces slope seeking, a new idea for non‐model based adaptive control. It involves driving the output of a plant to a value corresponding to a commanded slope of its reference‐to‐output map. To achieve this objective, we introduce a slope reference input into a sinusoidal perturbation‐based extremum seeking scheme; derive a stability test for single parameter slope seeking, and then develop a systematic design algorithm based on standard linear SISO control methods to satisfy the stability test. We then extend the results to the multivariable case of gradient seeking. Finally, we illustrate near‐optimal compressor operation under slope seeking feedback through a simulation study upon the well‐known Moore–Greitzer model of compressor instability. Copyright © 2003 John Wiley & Sons, Ltd.     

基于变论域模糊逻辑的互联电力系统负荷频率控制

随着风电在电力系统中的渗透率不断提高,其出力不确定性对系统频率稳定造成威胁。针对风电接入系统后的频率波动问题,提出变论域模糊PI负荷频率控制策略。为克服传统模糊控制器由于论域固定导致自适应能力有限的缺点,设计的变论域模糊PI负荷频率控制器通过变论域方法实现输入、输出论域的动态调整。为满足风电接入系统后复杂的论域调整需求,基于模糊推理设计新型变论域伸缩因子。典型两区域互联系统仿真实验表明,在不同形式的扰动下,该新型控制器较PI控制器、模糊PI控制器有更好的控制表现,能更好地处理风电出力不确定性对互联电力系统频率稳定的影响。     

An approach to tune fuzzy controllers based on reinforcement learning for autonomous vehicle control

In this paper, we suggest a new approach for tuning parameters of fuzzy controllers based on reinforcement learning. The architecture of the proposed approach is comprised of a Q estimator network (QEN) and a Takagi-Sugeno-type fuzzy inference system (TSK-FIS). Unlike other fuzzy Q-learning approaches that select an optimal action based on finite discrete actions, the proposed controller obtains the control output directly from TSK-FIS. With the proposed architecture, the learning algorithms for all the parameters of the QEN and the FIS are developed based on the temporal-difference (TD) methods as well as the gradient-descent algorithm. The performance of the proposed design technique is illustrated by simulation studies of a vehicle longitudinal-control system.     

Robust adaptive output‐feedback control for a class of nonlinear systems with time‐varying actuator faults

A robust adaptive output‐feedback control scheme is proposed for a class of nonlinear systems with unknown time‐varying actuator faults. Additional unmodelled terms in the actuator fault model are considered. A new linearly parameterized model is proposed. The boundedness of all the closed‐loop signals is established. The desired control performance of the closed‐loop system is guaranteed by appropriately choosing the design parameters. The properties of the proposed control algorithm are demonstrated by two simulation examples. Copyright © 2010 John Wiley & Sons, Ltd.     

A small‐gain approach for adaptive output‐feedback NN control of switched pure‐feedback nonlinear systems

This paper investigates the problem of adaptive output‐feedback neural network (NN) control for a class of switched pure‐feedback uncertain nonlinear systems. A switched observer is first constructed to estimate the unmeasurable states. Next, with the help of an NN to approximate the unknown nonlinear terms, a switched small‐gain technique‐based adaptive output‐feedback NN control scheme is developed by exploiting the backstepping recursive design scheme, input‐to‐state stability analysis, the common Lyapunov function method, and the average dwell time (ADT) method. In the recursive design, the difficulty of constructing an overall Lyapunov function for the switched closed‐loop system is dealt with by decomposing the switched closed‐loop system into two interconnected switched systems and constructing two Lyapunov functions for two interconnected switched systems, respectively. The proposed controllers for individual subsystems guarantee that all signals in the closed‐loop system are semiglobally, uniformly, and ultimately bounded under a class of switching signals with ADT, and finally, two examples illustrate the effectiveness of theoretical results, which include a switched RLC circuit system.     

Direct adaptive neural control of nonlinear strict‐feedback systems with unmodeled dynamics using small‐gain approach

In this paper, a novel direct adaptive neural control approach is presented for a class of single‐input and single‐output strict‐feedback nonlinear systems with nonlinear uncertainties, unmodeled dynamics, and dynamic disturbances. Radial basis function neural networks are used to approximate the unknown and desired control signals, and a direct adaptive neural controller is constructed by combining the backstepping technique and the property of hyperbolic tangent function. It is shown that the proposed control scheme can guarantee that all signals in the closed‐loop system are semi‐globally uniformly ultimately bounded in mean square. The main advantage of this paper is that a novel adaptive neural control scheme with only one adaptive law is developed for uncertain strict‐feedback nonlinear systems with unmodeled dynamics. Simulation results are provided to illustrate the effectiveness of the proposed scheme. Copyright © 2015 John Wiley & Sons, Ltd.     

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.     

Amplitude-constrained adaptive lqg control of first-order systems

This paper considers the problem of adaptive LQG control design when the system control input is subject to an amplitude constraint. A first-order stochastic system is considered mainly because its output can be satisfactorily described by a Gaussian probability density function (PDF) or can be calculated numerically. In this light, two algorithms are proposed. In the first algorithm an approach given by Toivonen has been adopted where the PDF of the output is approximated by the Gaussian density in order to develop a suboptimal adaptive feedback controller. the approach in the second algorithm is based on the numerically calculated PDF of the system output yielding a near-optimal feedback gain. the proposed algorithms are compared with existing ones via simulations.     

Adaptive fault tolerant control for a class of multi‐input multi‐output nonlinear systems with both sensor and actuator faults

Compared with the fault diagnosis, detection, and isolation literature, very few results are available to discuss control algorithms directly for multi‐input multi‐output nonlinear systems with both sensor and actuator faults in the fault tolerant control literature. In this work, we present a fault tolerant control algorithm to address the system output stabilization problem for a class of multi‐input multi‐output nonlinear systems with both parametric and nonparametric uncertainties, subject to sensor and actuator faults that can be both multiplicative and additive. All elements of the sensor measurements and actuator components can be faulty. Besides, the control input gain function is not fully known. Backstepping method is used in the analysis and control design. We show that under the proposed control scheme, uniformly ultimate boundedness of the system output is guaranteed, while all closed‐loop system signals stay bounded. In the cases where the sensor faults are only multiplicative, exponential convergence of the system state variables into small neighbourhoods around zero is guaranteed. An illustrative example on a robot manipulator model is presented in the end to further demonstrate the effectiveness of the proposed control scheme.     

PFC Design via FRIT Approach for Adaptive Output Feedback Control of Discrete‐Time Systems

This paper deals with a design problem of an adaptive output feedback control for discrete‐time systems with a parallel feedforward compensator (PFC), which is designed for making the augmented controlled system “Almost Strictly Positive Real” (ASPR). A PFC design scheme by a fictitious reference iterative tuning (FRIT) approach with only using an input/output experimental data set will be proposed for discrete‐time systems in order to design an adaptive output feedback control system. Furthermore, the effectiveness of the proposed PFC design method will be confirmed through numerical simulations by designing an adaptive control system with adaptive NN (neural network) for an uncertain discrete‐time system. © 2014 Wiley Periodicals, Inc. Electr Eng Jpn, 187(1): 24–32, 2014; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/eej.22456     

全桥型DC/DC变换器的DMC-PID串级控制策略研究

研究了全桥型DC/DC变换器系统的控制方法。针对现有DC/DC变换器动态响应速度慢、抗干扰能力差的现状,提出了基于状态反馈精确线性化的动态矩阵控制-比例积分微分DMC-PID（dynamic matrix control-propor-tional integral differential）串级控制方法。应用状态空间平均法建立全桥型DC/DC变换器系统的非线性模型,通过状态反馈精确线性化后得到线性化系统。为使系统获得优良的控制性能,选取DMC-PID串级控制方法。该控制方法同时具备PID算法的鲁棒性和DMC算法的快速性和稳定性,能有效地解决电源输入电压大范围波动输出电压不能及时稳定的问题。通过仿真分析和实验验证了该方案能在各种扰动工作条件下实现对变换器输出精确、快速的控制。