一类非仿射非线性系统混合自适应模糊控制

针对一类单输入单输出非仿射非线性系统控制方向未知情况下的控制器设计问题,提出一种混合自适应模糊控制器设计方案。该方案利用泰勒级数将非仿射系统转换为仿射系统,通过模糊逻辑系统逼近该仿射系统中的未知函数,基于此构造模糊控制器,同时利用跟踪误差和模型预测误差设计参数自适应律。利用Lyapunov稳定性定理证明运用所设计的控制器可以保证系统跟踪误差收敛,同时证明了该方案相比已有方案系统跟踪误差具有更快的收敛速度。仿真结果验证了所提控制器设计方案的有效性。     

一类非仿射非线性系统的自适应模糊控制

为了讨论一类非仿射非线性系统自适应模糊控制问题，利用有关隐函数定理和泰勒公式，将系统由非仿射型转变为仿射型，基于滑模控制原理，并运用模糊逻辑系统对未知函数进行在线逼近，提出了一种具有监督器的自适应模糊控制方法。该方法在考虑到外界干扰的情况下，通过监督控制器保证闭环系统所有信号有界，通过引入最优逼近误差的自适应补偿项来消除建模误差的影响。通过Lyapunov方法，证明了跟踪误差收敛到零，仿真结果表明了该方法的有效性。     

一类模型不确定非线性系统的反演预测控制

针对一类模型不确定的单输入单输出仿射非线性系统,设计一种使得闭环系统稳定且滚动时域性能指标在线最小化的预测控制器。运用反演(Backstepping)设计思想获得具有待定参数的控制器表达式,其误差导数中的未知函数采用模糊逻辑系统来逼近,通过直接估计模糊系统最优参数向量的范数上界来设计控制器和自适应律,大大降低了在线计算量。理论证明该方法设计的控制器保证了闭环系统所有信号是半全局有界的,并且跟踪误差收敛于零的某一邻域。仿真算例验证了所提出算法的有效性。     

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。     

非线性时滞系统自适应神经网络Backstepping控制

针对一类未知非线性时滞系统,提出了一种自适应神经网络控制设计方案,将Backstepping、占有方法以及自适应界化技术结合起来构造了一个鲁棒自适应神经网络跟踪控制器,采用神经网络逼近未知时滞函数,放松了对非线性时滞函数的要求。通过构建一个恰当的Lyapunov-Krasoviskii泛函证明了闭环系统所有信号半全局一致最终有界,调节设计参数可以实现任意输出跟踪精确度。实例仿真说明了该方案的可行性。     

基于ELM的机器人自适应跟踪控制

针对刚性臂机器人系统,提出基于极限学习机(ELM)的两种自适应神经控制算法。极限学习机随机选择单隐层前馈神经网络(SLFNs)的隐层节点及其参数,仅调整其网络的输出权值,以极快的学习速度可获得良好的推广性。在自适应控制算法中,ELM逼近系统的未知非线性函数,附加的鲁棒控制项补偿系统的逼近误差。ELM神经控制器的参数自适应调整律及鲁棒控制项由Lyapunov稳定性理论分析得出,所设计的两种控制算法均不依赖于初始条件的约束且放松对参数有界的要求,同时保证闭环系统跟踪误差满足全局稳定而且渐近收敛于零。将所提出的ELM控制器应用于二连杆刚性臂机器人跟踪控制实例中,并与现有的径向基函数(RBF)神经网络自适应控制算法进行比较,仿真结果表明,在同等条件下,ELM控制器具有良好的跟踪控制性能,显示出其有效性和应用潜力。     

基于CARIMA模型的自适应预测函数控制在锅炉燃烧系统中的应用

针对目前火电厂锅炉燃烧控制系统的大滞后、强耦合、变工况等突出问题,提出了一种基于卡尔曼(CARIMA)模型的自适应预测函数控制方法.该方法先利用预测模型得到系统未来时刻输出,然后将设定输出值和预测值间的预测误差变化率作为自适应控制器的输入,控制器利用最小二乘算法推理得到控制输出.当被控对象模型参数未知或渐时变时,该方法通过实时辨识过程模型的参数来实时修正预测函数控制器的参数,这样可以进一步提高预测函数控制方法的控制品质,提高锅炉的燃烧效率.仿真实验表明,自适应预测函数控制是一种计算简单、鲁棒性和适应性较强、控制精度高的控制方法.     

基于自适应模糊滑模控制的机器人轨迹跟踪算法

针对控制参数的不确定性以及存在未知外部扰动情况下移动机器人的轨迹跟踪问题,提出一种基于光滑非线性饱和函数的自适应模糊滑模轨迹跟踪控制算法。通过建立不确定非线性移动机器人运动控制模型,利用自适应模糊逻辑系统构建自适应模糊滑模控制器。为了增强轨迹跟踪控制算法对随机不确定外部扰动适应能力的同时削弱滑模控制算法中的输入抖振现象,利用有界输入有界输出(BIBO)稳定的方法,通过带有自适应调节算法的模糊系统对滑模控制律中非线性函数项进行自适应逼近,并设计了模糊系统中可调参数的自适应控制律,保证了控制系统的稳定与收敛。实验结果表明,所设计的控制器对系统参数不确定性和外界扰动均具有较强的轨迹跟踪性能和鲁棒性。与传统的滑模控制算法相比,该算法不仅能有效减小输入抖振而且轨迹跟踪控制精度提高了18.89%。     

电力机车PMSM自适应模糊滑模控制

为研究牵引工况下电力机车永磁同步电机(PMSM)的转速控制精度,考虑轮轨接触不平顺及车体静载荷在轮对径向产生的未知时变负载转矩,建立了机车PMSM在d-q坐标系下的数学模型。针对该耦合非线性系统中存在的负载转矩,设计非线性转矩观测器对其实际值进行估计,对观测误差采用自适应模糊逻辑系统进行逼近;为考察d轴电压过零跳变对转速控制及转矩观测性能的影响,在d轴电压控制器设计中引入Nussbaum函数,并依据Lyapunov稳定性理论,构造了基于转矩观测器的自适应模糊滑模控制器。理论分析及仿真结果表明,当转矩时变或d轴电压过零时,机车PMSM闭环转速控制系统跟踪误差一致有界,转矩观测误差收敛于0。     

基于多智能体的微电网电压与频率二次控制

基于多智能体一致性方法,提出了考虑状态受限的自适应模糊固定时间二次电压控制器和基于控制障碍函数的二次频率控制器。在多智能体一致性控制中,将每一个分布式电源(DG)视为一个非线性智能体,智能体之间通过稀疏网络进行通信。对反馈线性化后的未知变量进行自适应模糊估计以提高控制器的自适应能力。引入类超螺旋方法改进非奇异快速终端滑模,使其由有限时间收敛拓展成固定时间收敛。考虑系统状态受限问题,采用障碍Lyapunov函数(BLF)和控制障碍函数分别设计电压与频率控制器,使系统状态在预设的约束范围内。最后通过实验验证了所提控制器的有效性。     

Adaptive neural prescribed performance output feedback control of pure feedback nonlinear systems using disturbance observer

In this study, an adaptive output feedback control with prescribed performance is proposed for unknown pure feedback nonlinear systems with external disturbances and unmeasured states. A novel prescribed performance function is developed and incorporated into an output error transformation to achieve tracking control with prescribed performance. To handle the unknown non-affine nonlinearities and avoid the algebraic loop problem, the radial basis function neural network (RBFNN) is adopted to approximate the unknown non-affine nonlinearities with the help of Butterworth low-pass filter. Based on the output of the RBFNN, the coupled design between sate observer and disturbance observer is presented to estimate the unmeasured states and compounded disturbances. Then, the adaptive output feedback control scheme is proposed for unknown pure feedback nonlinear systems, where a first-order filter is introduced to tackle with the issue of “explosion of complexity” in the traditional back-stepping approach. The boundedness and convergence of the closed-loop system are proved rigorously by utilizing the Lyapunov stability theorem. Finally, simulation studies are worked out to demonstrate the effectiveness of the proposed scheme.     

Finite-time adaptive fuzzy command filtering control for stochastic nonlinear systems with input quantization

This article focuses on the finite-time adaptive fuzzy control problem based on command filtering for stochastic nonlinear systems subject to input quantization. Fuzzy logic systems are employed to estimate unknown nonlinearities. In the control design, the hysteretic quantized input is decomposed into two bounded nonlinear functions, which solves the chattering problem. Meanwhile, an adaptive fuzzy controller is presented by the combination of command filter technique and backstepping control, which eliminates the computational complexity existing in traditional backstepping design. Under the proposed adaptive mechanism, all the closed-loop signals remain bounded while the desired system performance can be realized within finite time. The main significance of this work is that (1) the filtering error can be solved on the basis of the designed compensating signals; (2) the requirement of adaptive parameters is decreased to only one, which simplifies the controller design process and may improve the control performance. Two simulation examples are used to validity of the developed scheme.     

Adaptive multi-dimensional Taylor network funnel control of a class of nonlinear systems with asymmetric input saturation

In view of the result and performance of control are affected by the existence of input constraints and requirements, adaptive multi-dimensional Taylor network (MTN) funnel control problem is studied for a class of nonlinear systems with asymmetric input saturation in this paper. Firstly, the effect of asymmetric input saturation can overcome by introducing the Gaussian error function, namely, the asymmetric saturation model is represented as a simple linear model with a bounded disturbance. Secondly, MTNs are employed to approximate the unknown functions in the controller design. Then, an adaptive MTN tracking controller is developed by blends the idea of funnel control into backstepping, which can guarantee that the tracking error always meets the given prescribed performance regarding the transient and steady state responses as well as the output of system tracks the give continuous reference signal. Finally, the effectiveness of the proposed control is demonstrated using two examples.     

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.     

Command filter-based adaptive neural network controller design for uncertain nonsmooth nonlinear systems with output constraint

This paper investigates the command filter-based adaptive neural network tracking control problem for uncertain nonsmooth nonlinear systems. First, an integral barrier Lyapunov function is introduced to deal with the symmetric output constraint and make the output comply with prescribed restrictions. Second, by the Filippov's differential inclusion theory and approximation theorem, the considered nonsmooth nonlinear system is converted to an equivalent smooth nonlinear system. Third, the Levant's differentiator is used to deal with the “explosion of complexity” problem. An error compensation mechanism is established to attenuate the effect of the filtering error on control performance. Then, an adaptive neural network controller is set up by resorting to the backstepping technique. It is strictly mathematically proved that the tracking error can converge to an arbitrarily small neighborhood of the origin and all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. Finally, a numerical example and an application example of the robotic manipulator system are provided to demonstrate the availability of the proposed control strategy.     

Decentralized prescribed performance adaptive tracking control for Markovian jump uncertain nonlinear systems with input saturation

A decentralized prescribed performance adaptive tracking control problem is investigated for Markovian jump uncertain nonlinear interconnected large‐scale systems. The considered interconnected large‐scale systems contain unknown nonlinear uncertainties, unknown control gains, actuator saturation, and Markovian jump signals, and the Markovian jump subsystems are in the form of triangular structure. First, by defining a novel state transformation with the performance function, the prescribed performance control problem is transformed to stabilization problem. Then, introducing an intermediate control signal into the control design, employing neural network to approximate the unknown composite nonlinear function, and based on the framework of the backstepping control design and adaptive estimation method, a corresponding decentralized prescribed performance adaptive tracking controller is designed. It is proved that all the signals in the closed‐loop system are bounded, and the prescribed tracking performances are guaranteed. A numerical example is provided to illustrate the effectiveness of the proposed control strategy. Copyright © 2016 John Wiley & Sons, Ltd.     

Adaptive prescribed performance tracking control for uncertain strict-feedback Multiple Inputs and Multiple Outputs nonlinear systems based on generalized fuzzy hyperbolic model

This article proposes an adaptive prescribed performance tracking control methodology for a class of strict-feedback Multiple Inputs and Multiple Outputs nonlinear systems. A combination of backstepping technique and the generalized fuzzy hyperbolic model was used in recursive design of adaptive controller. A novel performance constraint function guarantees the tracking control performance. Lyapunov stability analysis proves that the designed controller can ensure the predefined transient and all signals within the closed-loop systems are semiglobally uniformly ultimately bounded. In the end, simulation results illustrate the validity of the proposed approach.     

Saturated observer-based adaptive neural network leader-following control of N tractors with n-trailers with a guaranteed performance

This article addresses the leader-following neural network adaptive observer-based control of N tractors connected to n trailers with the prescribed performance specifications. To propose the controller, a change of coordinates and a nonlinear error transformation are used to transform the constrained error dynamics to a new second-order Euler-Lagrange unconstrained error dynamics which inherits all structural properties of ith vehicle dynamic model. By combining a projection-type neural network and an adaptive robust technique, a novel leader-following saturated output-feedback controller is proposed to force that ith vehicle tracks a virtual leader trajectory with the prescribed transient and steady-state characteristics while reducing the actuator saturation risk and compensating all unknown dynamic model parameters, external disturbances, unmolded dynamics, and NN approximation errors. A saturated velocity observer is heuristically proposed to obviate the requirement for the velocity measurements of ith vehicle without any unwanted peaking. A Lyapunov-based stability analysis is utilized to prove that all the tracking and state observation errors are semi-globally uniformly ultimately bounded (SGUUB) and they converge to small bounds including the origin with a prescribed performance. At the end, computer simulations will be shown to validate the efficacy of the proposed controller in practice.     

Neural networks-based adaptive finite-time prescribed performance fault-tolerant control of switched nonlinear systems

In this article, the adaptive finite-time fault-tolerant control problem is considered for a class of switched nonlinear systems in nonstrict-feedback form with actuator fault. The problem of finite-time fault-tolerant control is solved by introducing a finite-time performance function. Meanwhile, the completely unknown nonlinear functions exist in the switched system are identified by the neural networks. Based on the common Lyapunov function method with adaptive backstepping technique, the finite-time fault-tolerant controller is designed. The proposed control strategy can guarantee that the tracking error converges to a prescribed zone at a finite-time and all system variables remain semiglobally practical finite-time stable. Numerical examples are offered to verify the feasibility of the theoretical result.