Small-gain technique-based adaptive fuzzy command filtered control for uncertain nonlinear systems with unmodeled dynamics and disturbances

This article studies the adaptive tracking control problem for a class of uncertain nonlinear systems with unmodeled dynamics and disturbances. First, a fuzzy state observer is established to estimate unmeasurable states. To overcome the problem of calculating explosion caused by the repeated differentiation of the virtual control signals, the command filter with a compensation mechanism is applied to the controller design procedure. Meanwhile, with the help of the fuzzy logic systems and the backstepping technique, an adaptive fuzzy control scheme is proposed, which guarantees that all signals in the closed-loop systems are bounded, and the tracking error can converge to a small region around the origin. Furthermore, the stability of the systems is proven to be input-to-state practically stable based on the small-gain theorem. Finally, a simulation example verifies the effectiveness of the proposed control approach.     

An adaptive dynamic surface control of output constrained stochastic nonlinear systems with unknown control directions

This article is concerned about an adaptive dynamic surface control (DSC) of output constrained stochastic nonlinear systems with unknown control directions and unmodeled dynamics. Nonlinear mapping-based backstepping control design is presented for stochastic nonlinear systems with output constraint. The explosion of complexity exists in tradition backstepping method is avoided by using the DSC technique. The radial basis function neural networks are employed to deal with unknown nonlinear functions. Nussbaum gain technique is employed to handle the unknown control directions. And a dynamic signal is employed to dominate the unmodeled dynamics. The adaptive controller is designed can ensure that the tracking error converges on a small region of the origin. And all signals of the closed-loop systems are semiglobal uniformly ultimately bounded. Finally, the results of the simulation cases are provided to show the effectivity of the designed controller scheme.     

基于神经网络补偿的机器人力／位置控制

考虑摩擦及外界干扰的情况下,针对具有不确定性的机器人系统,提出了一种新的基于神经网络补偿的智能力／位置控制方案。该控制器通过神经网络补偿机器人模型的未建模动力学部分等非参数不确定性带来的影响,NN的输出信号加在参考输入上而不是在关节力矩或控制输入上,同时在力控制回路采用灰色预测模糊调节,以提高系统的动态性能和稳态精度。仿真研究表明所设计的控制器具有良好的动态性能和较强的鲁棒性。     

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.     

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.     

Adaptive output feedback dynamic surface control of stochastic nonlinear systems with state and input unmodeled dynamics

In this paper, an adaptive neural output feedback control scheme is investigated for a class of stochastic nonlinear systems with unmeasured states and four kinds of uncertainties including uncertain nonlinear function, dynamic disturbance, input unmodeled dynamics, and stochastic inverse dynamics. The unmeasured states are estimated by K‐filters, and stochastic inverse dynamics is dealt with by constructing a changing supply function. The considered input unmodeled dynamic subsystem possesses nonlinear feature, and a dynamic normalization signal is introduced to counteract the unstable effect produced by the input unmodeled dynamics. Combining dynamic surface control technique with stochastic input‐to‐state stability, small‐gain condition, and Chebyshev's inequality, the designed robust adaptive controller can guarantee that all the signals in the closed‐loop system are bounded in probability, and the error signals are semi‐globally uniformly ultimately bounded in mean square or the sense of four‐moment. Simulation results are provided to verify the effectiveness of the proposed approach. Copyright © 2015 John Wiley & Sons, Ltd.     

Adaptive backstepping trajectory tracking controller for a miniature helicopter

An adaptive backstepping controller is proposed to achieve the asymptotically stable trajectory tracking for a miniature helicopter. The helicopter model is firstly decomposed into a cascaded structure between the position and attitude loops with unmodeled dynamics. The backstepping technique is applied to exploit this cascaded structure and to streamline the controller design procedure. Hyperbolic tangent functions, instead of traditional sign functions, are adopted to complete adaptive algorithms for adjusting the upper bounds of the unmodeled dynamics. Further, the controller introduces an auxiliary dynamic system to ensure the thrust constraint and to obviate singularity during the command attitude extraction. The stability analysis demonstrates that, the asymptotical stability of the auxiliary dynamic system warrants the asymptotically stable tracking of the miniature helicopter system. Simulations verify the theoretical results and analyze the tracking performance. Copyright © 2016 John Wiley & Sons, Ltd.     

Adaptive output feedback control of uncertain nonlinear systems with input delay and output constraint

This paper proposes an adaptive neural‐network control design for a class of output‐feedback nonlinear systems with input delay and unmodeled dynamics under the condition of an output constraint. A coordinate transformation with an input integral term and a Nussbaum function are combined to solve the problem of the input possessing both time delay and unknown control gain. By utilizing a barrier Lyapunov function and designing tuning functions, the adjustment of multiparameters is handled with a single adaptive law. The uncertainty of the system is approximated by dynamic signal and radial basis function neural networks (RBFNNs). Based on Lyapunov stability theory, an adaptive tracking control scheme is developed to guarantee all the signals of the closed‐loop systems are semiglobally uniformly ultimately bounded, and the output constraint is not violated.     

Adaptive control of pure-feedback nonlinear systems with full-state time-varying constraints and unmodeled dynamics

This paper focuses on the problem of adaptive control for a class of pure-feedback nonlinear systems with full-state time-varying constraints and unmodeled dynamics. By introducing a one-to-one nonlinear mapping, the constrained pure-feedback nonlinear system with state and input unmodeled dynamics is transformed into unconstrained pure-feedback system. The controller design based on the transformed novel system is proposed by using a modified dynamic surface control method. Dynamic signal and normalization signal are designed to handle dynamical uncertain terms and input unmodeled dynamics, respectively. By adding nonnegative normalization signal into the whole Lyapunov function and using the introducing compact set in the stability analysis, all signals in the whole system are proved to be semiglobally uniformly ultimately bounded, and all states can obey the time-varying constraint conditions. A numerical example is provided to demonstrate the effectiveness of the proposed approach.     

Robust analysis and design of load frequency controller for power systems

Robust load frequency control for power systems is discussed. A detailed robustness analysis of the existing control laws shows that parameter variation is not a critical issue but more attention should be paid to the unmodeled dynamics in robust load frequency controller design. A new robust load frequency control method is then proposed considering the unmodeled dynamics of power systems. Finally, a new configuration is proposed to overcome the effects of generation rate constraints (GRC). Simulation results show that the design method and the anti-GRC configuration are effective.     

Decentralized adaptive output‐feedback control for stochastic interconnected systems with stochastic unmodeled dynamic interactions

In this paper, we consider the problem of decentralized adaptive output‐feedback regulation for stochastic nonlinear interconnected systems with unknown virtual control coefficients, stochastic unmodeled dynamic interactions. The main contributions of the paper are as follows: (1) This paper presents the first result on decentralized output‐feedback control for stochastic nonlinear systems with unknown virtual control coefficients; (2) For stochastic interconnected systems with stochastic integral input‐to‐state stable unmodeled dynamics, and more general nonlinear uncertain interconnections which depend upon the outputs of subsystems and the stochastic unmodeled dynamics, a decentralized output‐feedback controller is designed to drive the outputs and states to the origin almost surely. Copyright © 2011 John Wiley & Sons, Ltd.     

A robust adaptive control with unmodeled dynamic for HVDC transmission systems

Utilizing the feature of quick response of HVDC to improve the performance of AC/DC system has become the emphasis to be researched. This paper introduces firstly the principle of the robust adaptive control of nonlinear systems with unmodeled dynamics, then developed the robust adaptive additional control of HVDC with unmodeled dynamics of generator in order to improve stability of power system. The additional control of HVDC with unmodeled dynamics only uses the local signals and its design is simple, furthermore it can obviously improve the stability of power system in different operational conditions. Experimental results using the presented concepts obtained on single machine infinite bus model are also included. These results prove the efficiency of the control scheme. The design process of controller provided a new idea to design controller by use of simplified model. __________ Translated from Journal of Sichuan University (Engineering Science Edition), 2005, 37(5): 154–158 (in Chinese)     

一种补偿动态摩擦的自适应模糊控制方法

机械系统的摩擦力精确数学模型很难建立，一直是人们所关注的问题。文中针对小型DC电机采用自适应模糊系统在线逼近摩擦模型并将辨识结果作为PD算法的补偿项。在控制方法上，文中采用了基于自适应模糊补偿的PD控制器．在系统证明上，从李雅普诺夫函数中导出了自适应参数并且分析了闭环系统跟踪误差的有界性。在算法实现上，利用Mauab对文中的方法及证明的有效性进行了验证。文中的方法也可应用到其它机械系统的摩擦补偿建模与控制上，如机器人系统。     

STATCOM无功电流的鲁棒自适应控制

从器件级、装置级和系统级3个层次讨论电压源型STATCOM的数学建模和控制器设计;提出了一种鲁棒型直接自适应控制算法,以解决STATCOM模型参数未知情况下无功电流的追踪控制问题。在对象系统严格正实性得以验证的基础上,系统地选择了控制器设计参数,并考虑了输入输出噪声对控制系统的影响,从而使控制器具有较好的鲁棒性。数字仿真结果表明,得到的控制器在追踪控制能力和鲁棒特性等方面比PI控制优越。     

Adaptive fuzzy finite-time consensus tracking for multiple Euler-Lagrange systems with unknown control directions

In this paper, the problem of adaptive fuzzy finite-time consensus tracking control for multiple Euler-Lagrange systems (ELSs) with uncertain dynamics and unknown control directions (UCDs) is investigated. The computational complexity problem in conventional backstepping is avoided by using finite-time command filter (FTCF), and the error in the filtering process is eliminated through error compensation signals. The fuzzy logic system combined with the adaptive control technique is applied to approximate and estimate the unknown nonlinear dynamics of ELS. The Nussbaum function-based continuous and nonsmooth input control torque is established to eliminate the influence of UCDs, and the proposed control scheme can guarantee the consensus tracking errors converge to the desired neighborhood of the origin within a finite time. Numerical simulation is used to test the effectiveness of the given algorithm.     

A design method for robust model matching control of nonminimum‐phase discrete‐time systems

This paper presents a design method for robust model matching control of nonminimum‐phase discrete‐time systems. This scheme can robustly control the nominal model in the presence of unmodeled dynamics and can achieve the desired model matching simultaneously. Furthermore, the sufficient condition for stabilizing the nominal model in the presence of the unmodeled dynamics is derived and the existence of bounds for all signals is proved. Finally, computer simulation results are presented to illustrate the effectiveness of the proposed method. © 1999 Scripta Technica, Electr Eng Jpn, 128(2): 36–44, 1999     

Robust adaptive output feedback control of nonlinearly parameterized systems

The ideas of adaptive nonlinear damping and changing supply functions were used to counteract the effects of parameter and nonlinear uncertainties, unmodeled dynamics and unknown bounded disturbances. The high-gain observer was used to estimate the state of the system. A robust adaptive output feedback control scheme was proposed for nonlinearly parameterized systems represented by input-output models. The scheme does not need to estimate the unknown parameters nor add a dynamical signal to dominate the effects of unmodeled dynamics. It is proven that the proposed control scheme guarantees that all the variables in the closed-loop system are bounded and the mean-square tracking error can be made arbitrarily small by choosing some design parameters appropriately. Simulation results have illustrated the effectiveness of the proposed robust adaptive control scheme. Translated from Journal of Sichuan University (Engineering Science Edition), 2006, 38(4): 136–140 [译自: 四川大学学报 (工程科学版)]     

A modular adaptive control design with ISS analysis for nonminimum phase hypersonic vehicle models

Air‐breathing hypersonic vehicles typically exhibit a nonminimum phase behavior when altitude is controlled via lift generation. This phenomenon prohibits the use of classical inversion‐based control techniques. Dynamic models of these vehicles are also subject to parametric uncertainties and unmodeled dynamics related to flexible effects of the fuselage. In this paper, we present a modular adaptive control method that achieves asymptotic setpoint tracking in both airspeed and altitude using thrust and elevator deflection as the only control inputs for a generic longitudinal model of a hypersonic cruise. The nonminimum phase problem is overcome through output redefinition, with altitude controlled by pitching moment. The internal dynamics, flight path angle, and altitude are then stabilized by saturating the interconnections and exploiting local stability properties. A new technique for the use of saturation functions in error coordinate is presented. The adaptive controller for altitude uses a pitch rate observer combined with projection. This control augmentation decouples the parameter estimation errors from internal dynamics, allowing for the use of small‐gain arguments. Simulation results from a vehicle model with flexible effects and parametric uncertainty are included to demonstrate control effectiveness.     

3-RRRT并联机器人鲁棒自适应模糊滑模控制

针对3-RRRT型搬运并联机器人提出了一种直接自适应模糊滑模控制方法.根据机器人的系统动力学模型特点,基于Lyapunov函数的综合设计方法和滑模控制理论,利用模糊逻辑系统与自适应技术相结合,提出了一种控制律,然后进行了系统控制仿真实验.结果表明,采用这种直接自适应模糊滑模控制方法的3-RRRT型并联机器人在周期干扰状况下达到了较高的轨迹控制精度,其闭环系统具有较强的自适应性和鲁棒稳定性.