An indirect adaptive control for fully feedback linearizable discrete-time non-linear systems

An indirect adaptive control for a discrete-time non-linear system that is fully input–output linearizable is developed. The unknown parameters of the system are identified by using a multi-output RLS algorithm. Based on the certainty equivalence principle, the estimated parameters are then utilized in the controller design. Stability of the adaptively controlled closed-loop system is shown by using the Lyapunov method. Numerical simulations are included to illustrated the performance of the proposed strategy. © 1997 John Wiley & Sons, Ltd.     

链式静止同步补偿器自适应反步控制器

针对链式静止同步补偿器(STATCOM)系统非线性特性,提出一种链式STATCOM自适应反步控制器。该控制器采用反步法的递归思想获得链式STATCOM的虚拟控制输入,并根据确定性等价原理设计系统的中间控制率,利用该中间控制率完成电压控制器设计。通过中间控制率和实际控制率的偏差量构造系统的李雅普诺夫方程,利用李雅普诺夫的渐近稳定性原理,获得能够保证链式STATCOM系统稳定的控制输入和系统参数的自适应率。实验结果进一步验证了所设计自适应反步控制器的正确性和有效性。     

Adaptive internal model control: the discrete‐time case

This paper considers the design and analysis of a discrete‐time H₂ optimal robust adaptive controller based on the internal model control structure. The certainty equivalence principle of adaptive control is used to combine a discrete‐time robust adaptive law with a discrete‐time H₂ internal model controller to obtain a discrete‐time adaptive H₂ internal model control scheme with provable guarantees of stability and robustness. The approach used parallels the earlier results obtained for the continuous‐time case. Nevertheless, there are some differences which, together with the widespread use of digital computers for controls applications, justifies a separate exposition. Copyright © 2001 John Wiley & Sons, Ltd.     

Inverse optimal adaptive control for non‐linear uncertain systems with exogenous disturbances

A Lyapunov‐based inverse optimal adaptive control‐system design problem for non‐linear uncertain systems with exogenous ℒ︁₂ disturbances is considered. Specifically, an inverse optimal adaptive non‐linear control framework is developed to explicitly characterize globally stabilizing disturbance rejection adaptive controllers that minimize a nonlinear‐nonquadratic performance functional for non‐linear cascade and block cascade systems with parametric uncertainty. It is shown that the adaptive Lyapunov function guaranteeing closed‐loop stability is a solution to the Hamilton–Jacobi–Isaacs equation for the controlled system and thus guarantees both optimality and robust stability. Additionally, the adaptive Lyapunov function is dissipative with respect to a weighted input–output energy supply rate guaranteeing closed‐loop disturbance rejection. For special integrand structures of the performance functionals considered, the proposed adaptive controllers additionally guarantee robustness to multiplicative input uncertainty. In the case of linear‐quadratic control it is shown that the operations of parameter estimation and controller design are coupled illustrating the breakdown of the certainty equivalence principle for the optimal adaptive control problem. Finally, the proposed framework is used to design adaptive controllers for jet engine compression systems with uncertain system dynamics. Copyright © 2000 John Wiley & Sons, Ltd.     

Non‐certainty equivalent adaptive control for lower triangular systems based on dynamic scaling and filter

A new adaptive controller is designed on the basis of dynamic scaling and filter for lower triangular systems. Compared with the available adaptive results in the literature, the proposed adaptive approach does not necessarily need to satisfy the certainty equivalence principle and allows for prescribed dynamics to be assigned to the parameter estimation error. The proposed adaptive state feedback controller that ensures all signals of closed‐loop systems are globally bounded while keeping the output tracking error to the origin simultaneously. It is interesting to note that, viewed from a Lyapunov perspective, the proposed method provides a procedure to add cross terms between the parameter estimates and the system states in every design step. Finally, two comparatively simulation examples are given, highlighting the advantages of the proposed methodology. Copyright © 2013 John Wiley & Sons, Ltd.     

基于T-S模糊模型的鲁棒自适应滑模控制

针对一类具有时滞的非线性连续时间系统，基于T—S模糊模型，考虑系统的外界干扰和建模误差不存在的理想情况，并利用滑模控制理论和LMI方法，设计了滑模控制器；考虑系统的外界干扰和建模误差存在的情况，采用神经网络逼近建模误差，引入自适应控制鲁棒项，设计了自适应滑模控制器，根据Lyaponuv稳定性理论，证明了系统状态渐近稳定。仿真示例表明了所提方法的有效性。     

静态无功补偿器鲁棒控制的一种新自适应逆推方法

针对一般的参数反馈型非线性系统提出了一种扩展自适应逆推方法。该方法不仅保留系统的非线性特性和对未知参数的实时在线估计,而且突破了经典的确定性等价性原理。将该方法应用到含有未知参数带有静态无功补偿器 (SVC)的单机无穷大系统。将这种新自适应机制引入电力系统,得到了带有SVC单机无穷大系统的自适应控制律。仿真结果表明,该方法在提高系统稳定性和参数估计方面优于传统的逆推方法,为工程应用提供了一种有效的选择。另外, 该文中的算法可以应用到其他控制系统。     

A novel adaptive control approach for nonlinearly parameterized systems

Nonlinearly parameterized systems are commonly encountered in control of practical systems. However, the conventional adaptive estimation and control strategies, based on the essential assumption of linear parameterization, are incapable of dealing with this class of systems. This incapability in turn becomes a bottleneck for prevalent applications of adaptive control. In literature, there have been some attempts to break through this bottleneck by investigating the characteristics of nonlinearities. However, it is still open for an implementable strategy that is powerful for nonlinearly parameterized systems as the certainty equivalence principle for linearly parameterized systems. This paper aims to contribute an attempt to this open problem by proposing a novel adaptive control approach. On the one hand, the controller is conceptually simple, and it does not explicitly rely on the expression of system nonlinearities. On the other hand, the controller is able to achieve system stability and parameter convergence. Copyright © 2013 John Wiley & Sons, Ltd.     

Robust self‐tuning PI decoupling control of uncertain multivariable systems

In this paper, for a class of multivariable systems with strong couplings, a robust self‐tuning PI decoupling controller is developed by combining a self‐tuning PI controller with a feedforward decoupling compensator and a filter. To determine the gains and other parameters of the PI decoupling controller, we first introduced a reduced order model. The parameters of the reduced order model are identified by using a normalized projection algorithm with dead zone. The gains of the PI controller together with other parameters are tuned online according to the certainty equivalent principle. By resorting to time‐varying operation, we presented the bounded‐input bounded‐output stability conditions and convergence conditions of the closed‐loop system. Simulation results on a synthetic system and a twin‐tank level system show the effectiveness of the proposed method. Copyright © 2011 John Wiley & Sons, Ltd.     

三相VIENNA整流器双闭环控制策略及其参数研究

通过对VIENNA整流器基本原理分析,建立了d-q坐标系下的数学模型。由于系统具有强耦合、非线性的特点,利用前馈解耦控制策略实现对d、q轴的解耦控制。一方面,基于频域法对电流内环进行分析,借助MATLAB单输入-单输出SISO(single input-single output)设计工具,合理配置电流内环控制器零点位置与环路增益,实现对网侧电流快速性与稳定性的控制;另一方面,电压外环易受到负载电流影响,通过对外环控制器零点和环路增益优化配置,提高系统鲁棒性。此外,重点分析了在负载电流干扰的情况下影响电压外环控制器稳定性的成因。最后,利用MATLAB/Simulink软件搭建仿真平台,验证了电流内环和电压外环控制器参数设计的正确性与合理性,使VIENNNA整流器双闭环控制系统具有良好的稳定性能和动态性能,并在负载波动的情况下,保持良好的控制效果。     

Robust adaptive control of robotic manipulators without the regressor matrix

A new adaptive controller is presented here for rigid-body robotic manipulators. It is stable and robust with respect to a class of external disturbances. The robustness of the adaptive controller is established without the ‘slow-varying’ assumption and the computationally demanding regressor matrix. The control law consists of a non-adaptive PD control part and an adaptive control part. It uses two adaptive matrices to compensate two uniformly bounded coefficient matrices derived from the original dynamics. A α σ|q?|-modified adaptive law is designed to adjust the adaptive matrices. A Lyapunov-type stability analysis indicates that the closed-loop system is uniformly ultimately bounded. The tracking error and compensation error will eventually converge into a closed region, which can be made arbitrarily small by adjusting the controller parameters. Simulation results are included to demonstrate the performance of the proposed controller.     

不确定性电力系统鲁棒自适应励磁控制

为降低电力系统不确定参数和外部干扰在建模和控制设计中对系统品质的影响,利用Backstepping方法,递归构造闭环系统耗散不等式的能量存储函数,进而得到具有干扰抑制的反馈控制.推证了存储函数又是Lyapunov函数,保证闭环系统渐进稳定.设计过程用估计值代替不确定参数,故系统对不确定参数具有适应能力.仿真结果表明,该...     

具有非线性输入的鲁棒自适应模糊滑模控制

针对一类具有死区非线性输入的SISO非线性系统,基于滑模控制原理,提出了一种稳定自适应模糊控制器设计方案.该方案通过使用积分型Lyapunov函数避免了反馈线性化方法中可能出现的控制器奇异性问题,运用两阶段法构造两个Lyapunov函数,确定出用于建模的有界闭区域,再证明跟踪误差收敛到零.通过理论分析,证明了闭环控制系统全局一致终结有界;仿真结果表明了该方法的有效性.     

永磁同步电动机的有限时间跟踪控制

针对永磁同步电动机绕组相电流和转速强耦合特性，基于永磁同步电动机精确的数学模型，依据中继切换控制机制和有限时间收敛的终端滑动模态控制机制，研究了永磁同步电动机的有限时间跟踪问题，给出了其终端滑模控制器的设计方案。在所设计的控制作用下，闭环系统将在有限时间内达到平衡状态，保证了闭环系统所有信号的有界性和平衡点的全局稳定性，系统在有限时间内精确地跟踪给定的参考信号。对永磁同步电动机模型进行了数值仿真，结果表明，在所设计的终端滑模控制器作用下，系统的跟踪误差在有限时间内达到零，验证了所提算法的正确和有效性。     

Adaptive neural finite-time output-feedback tracking control for nonlinear stochastic nonstrict-feedback systems with input saturation

In this paper, an observer-based adaptive neural output-feedback control scheme is developed for a class of nonlinear stochastic nonstrict-feedback systems with input saturation in finite-time interval. The mean value theorem and the property of the smooth function are applied to cope with the difficulties caused by the existence of input saturation. According to the universal approximation capability of the radial basis function neural network, it will be utilized to compensate the unknown nonlinear functions. Based on the state observer, the finite-time Lyapunov stability theorem, we propose an adaptive neural output-feedback control scheme for nonlinear stochastic systems in nonstrict-feedback form. The developed controller guarantees that the system output signal can track the given reference signal trajectory, and all closed-loop signals are semi-globally finite-time stability in probability. The observer errors and the tracking error can converge to a small neighborhood of the origin. Finally, simulation results demonstrate the effectiveness of the developed control scheme.     

Neuroadaptive Output Tracking of Fully Autonomous Road Vehicles With an Observer

Automated vehicle control systems are a key technology for intelligent vehicle highway systems (IVHSs). This paper presents an automated vehicle control algorithm for combined longitudinal and lateral motion control of highway vehicles, with special emphasis on front-wheel-steered four-wheel road vehicles. The controller is synthesized using an online neural-estimator-based control law that works in combination with a lateral velocity observer. The online adaptive neural-estimator-based design approach enables the controller to counteract for inherent model discrepancies, strong nonlinearities, and coupling effects. The neurocontrol approach can guarantee the uniform ultimate bounds (UUBs) of the tracking and observer errors and the bounds of the neural weights. The key design features are 1) inherent coupling effects will be taken into account as a result of combining of the two control issues, viz., lateral and longitudinal control; 2) rather ad hoc numerical approximations of lateral velocity will be avoided via a combined controller–observer design; and 3) closed-loop stability issues of the overall system will be established. The algorithm is validated via a formative mathematical analysis based on a Lyapunov approach and numerical simulations in the presence of parametric uncertainties, as well as severe and adverse driving conditions.      

多变量时滞系统的解耦模糊内模控制

针对时滞多变量耦合系统，研究了带有多时滞的多变量稳定过程的解耦内模控制，根据开环系统的时滞给出了一种时滞系统多变量解耦方法，由于理想的多变量时滞系统内模控制器设计复杂，且根据内模控制理论设计的控制器一般是高阶的，因此采用模糊控制方法设计模糊内模控制器。其宗旨是首先根据过程对象的模型，设计多变量时滞解耦的预补偿器，对其进行对角优势化，利用补偿后的主对角元素作为内模控制的预估模型，设计了多通道模糊内模控制器。仿真结果表明，该方法具有很好的解耦能力，鲁棒性强；方法简单，易于实现。具有工程应用价值。     

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)     

Observer-based single-network incremental adaptive dynamic programming for fault-tolerant control of nonlinear systems with actuator faults

A model-free incremental adaptive fault-tolerant control (FTC) scheme is proposed for a class of nonlinear systems with actuator faults. To deal with actuator faults and guarantee the approximate optimal performance of the nominal nonlinear system without any prior knowledge of system dynamics, a single-network incremental adaptive dynamic programming (SIADP) algorithm based on incremental neural network observer is developed to design an active fault-tolerant control (AFTC) policy. An approximate linear time-varying system is obtained by incremental nonlinear technique, in which the relevant matrix parameters are identified by recursive least square estimation. Then, a SIADP algorithm-based fault-tolerant controller is developed. Based on the redundancy characteristic and function of actuators, a grouping scheme of actuators is introduced. An incremental neural network observer is designed to approximate the actuator faults. The novel SIADP scheme is constructed with a simplified single critic neural network to shorten the learning time and decrease the computational burden in the control process, in which the norm of the weight estimations of critic neural network is updated. Moreover, based on the Lyapunov theorem, the uniformly ultimately bounded stability of the closed-loop incremental system is proved. Finally, simulations are given to verify the effectiveness of the proposed FTC scheme.     

Strong robustness in multi‐phase adaptive control: the basic scheme

The general structure of adaptive control systems based on strong robustness is introduced. This approach splits into two phases. In the first phase, emphasis is put on identification until enough information is obtained in order to design a controller that stabilizes the actual system, and even under adaptation. This is achieved if the input sequence is computed in such a way that the uncertainty on the parameters of the system to be controlled becomes sufficiently small. Then, in the second phase, effort is shifted to control via a traditional certainty equivalence type of strategy. Copyright © 2004 John Wiley & Sons, Ltd.