GLOBAL STABILIZATION OF PARAMETRIC CHAINED-FORM SYSTEMS BY TIME-VARYING DYNAMIC FEEDBACK

We propose in this paper a constructive time-varying adaptive control scheme for a new class of linearly parametrized non-linear systems. The considered systems are not stabilizable by continuous time-invariant state feedback for any value of the parameters. The novelty of the proposed method is a combined use of currently popular backstepping and time-varying techniques.     

Robust economic model predictive control of nonlinear networked control systems with communication delays

In this work, we consider economic model predictive control of nonlinear networked control systems subject to external disturbances and communication delays in both sensor-to-controller and controller-to-actuator channels. The problem is addressed in the framework of the min-max model predictive control. First, a delay compensation strategy is proposed to minimize the impact of communication delays on the control performance. In the compensation strategy, once the receiver at the controller node receives a new state measurement, the controller generates a control sequence and sends the sequence to the actuator to compensate for delayed control inputs. Subsequently, the presence of disturbance is explicitly considered for robustness and the semi-feedback min-max optimization algorithm is used to design the control law based on the estimate of the current state reconstructed by the estimator. Furthermore, the input-to-state practical stability of the proposed approach is established by constructing a modified Lyapunov function. Simulation results of a numerical example and a chemical process example demonstrate the applicability and effectiveness of our approach.     

Improved performance MRAC of linear and non-linear systems

The problem of improved performance adaptive control (IPAC) of a class of linear and non-linear systems is considered. A method for its solution is presented, the main feature of which lies in augmenting the ‘standard’ model reference adaptive controller by a signal properly designed to compensate for the effect of plant parameter uncertainty on the output error. One of the main performance improvement characteristics of the proposed IPAC is that the zero-state output error can be made arbitrarily small under standard model reference adaptive control (MRAC) assumptions in the case of linear systems, while a similar result holds true for a class of linearizable systems as well. the structure of the proposed controller is such that several existing MRAC results, such as exponential convergence of output and parameter errors in the presence of sufficiently rich reference inputs, remain valid. the proposed controller also achieves improved performance in the presence of a class of bounded disturbances and/or unmodelled dynamics as well as in the case of an adaptation switch-off.     

An autosynthesizing non-linear control system using a rule-based expert system

An advanced non-linear control system is obtained by combining recent developments in non-linear control system synthesis with a rule-based system approach to real-time control. The basic problem to be addressed is the control of a non-linear plant which is sufficiently sensitive to both operating point and input amplitude that the desired control system performance can be obtained only with a non-linear controller that is ‘retuned’ or ‘resynthesized’ whenever the operating point changes significantly. (The term ‘changes significantly’ in this context signifies an operating point change that causes the non-linear control system input/output behaviour to change substantially in an undesirable way.) This is accomplished by a hierarchically organized intelligent control system with a conventional reprogrammable controller under the direction of a rule-based expert system. The function of the expert system is to (i) monitor the behaviour of the non-linear control system to determine when retuning or resynthesis is required; if the behaviour is satisfactory, then continue passive monitoring; else (ii) when retuning or resynthesis is required: set up and execute experiments to derive the model information required to tune or synthesize a new non-linear controller in terms of a given structure and parametrization; execute the retuning/resynthesis procedure; reprogramme the controller (download the parametrization); and recommission the updated non-linear control system and return to normal operation. In essence, the rule-based system provides supervisory control (‘meta-control’) for a conventional reprogrammable non-linear controller that will autotune or autosynthesize as required. Autosynthesis takes place to accommodate variability in plant behaviour due to operating point changes, and the nonlinear controller thus synthesized corrects for amplitude sensitivity. This concept represents one way to combine artificial intelligence with control; it will be discussed and illustrated by example below.     

Non-linear learning control of robot manipulators without requiring acceleration measurement

A new class of non-linear learning control laws is introduced for a robot manipulator to track a given trajectory in performing a series of tasks. The learning control scheme is applicable to robots with both resolute and prismatic joints, requires only position and velocity feedback, and removes the acceleration measurement required by the existing results. It has been shown that under the proposed learning control the tracking errors are always guaranteed to be asymptotically stable with respect to the number of trials. The proposed control is robust in the sense that exact knowledge about the non-linear dynamics is not required except for bounding functions on the magnitudes. In addition, the new learning scheme can be used without assumptions such as repeatability of robot motion, repeatability of tasks and resetting of initial tracking errors.     

Neuro-adaptive output-feedback optimized stochastic control for the active suspension systems with state constraints

This article investigates an adaptive neural network (NN) output-feedback optimal control design problem for active suspension systems (ASSs) with stochastic disturbance. The ASSs under consideration contain the characteristics of spring nonlinear dynamics, unmeasured states, and state constraints. The NNs are developed to approximate the unknown nonlinear functions. Meanwhile, observer-based output feedback control design method is proposed based on the adaptive backstepping technique. Furthermore, the stability of the closed-loop system is demonstrated by constructing the barrier Lyapunov function, thus ensuring that the full-state constraints are not exceeded. In particular, the simulation validations are given for the cases of bump, C-class, and D-class road displacements inputs. Finally, the simulation results verify the effectiveness of the studied control strategy.     

Enhancing optimal controllers via techniques from robust and adaptive control

Optimal control strategies for both non-linear and linear plants and indices are notoriously sensitive to modelling errors and external noise disturbances. In this paper a general framework to enhance robustness of an optimal control law is presented, with emphasis on the non-linear case. The framework allows a blending of off-line non-linear optimal control, on-line linear robust feedback control for regulation about the optimal trajectory and on-line adaptive techniques to enhance performance/robustness. The adaptive-Q techniques are those developed in previous work based on the Youla-Kucera parametrization for the class of all stabilizing two-degree-of-freedom controllers. Some general fundamental stability properties are developed which are new, at least for the non-linear plant and linear robust controller case. Also, performance enhancement results in the presence of unmodelled linear dynamics based on an averaging analysis are reviewed. A convergence analysis based on averaging theory appears possible in principle for any specific non-linear system but is beyond the scope of the present paper. Certain model reference adaptive control algorithms come out as special cases. A non-linear optimal control problem is studied to illustrate the efficacy of the techniques, and the possibility of further performance enhancement based on functional learning is noted.     

使用滑模控制的单相电动机新逆变驱动

针对单相感应电动机,提出了新的逆变驱动策略。与采用传统的逆变驱动控制相比,由于新的逆变驱动策略采用了LC滤波,因此可使单相感应电动机的控制输入是平滑的,控制精度更高。完整的控制系统由四级级联控制环组成,其内环实现感应电动机速度调节和电流控制,而外环实现电动机驱动,由此构成了新的电流模式定向控制,以替代磁场定向控制。逆变器开关驱动由滑模控制实现。由实验结果验证了该方案的有效性。     

Adaptive feedback linearization for position control of a switched reluctance motor: Analysis and simulation

In this paper a non-linear adaptive feedback-linearizing control is designed for a fifth-order model of a three-phase switched reluctance motor (SRM) which includes both electrical and mechanical dynamics. This non-linear adaptive control structure compensates for all the non-linearities between inputs and outputs, allows the use of a linear controller for motion tracking and improves the performance by reducing torque ripple of the SRM. A validated non-linear model of the SRM is used for the system simulation, while the control algorithm contains an adaptive scheme based on the parametrized model. Simulation results are given to demonstrate the effectiveness of the control method.     

三相感应电机电流模式定向控制与新逆变驱动

提出了三相感应电机新的逆变驱动策略与电流模式定向控制(Current Mode Oriented Control,简称CMOC)的方法.在新逆变驱动策略的控制下,三相感应电机具有平滑的控制输入和更高的控制精度.完整的控制系统由内部控制环与外部控制环组成,内环实现感应电机速度调节和电流控制,而外环实现电机驱动.由于新逆变驱动策略具有LC滤波,因此感应电机的控制输入平滑.逆变器开关驱动由滑模控制实现.由于逆变器开关律由滑模控制直接驱动,因此不存在PWM驱动方式下的死因时间问题.实验结果验证了该方案的有效性.     

660MW超超临界汽轮机自启动控制策略及其热应力计算

以华电望亭发电厂3号机组为例,介绍上海汽轮机厂引进的德国西门子公司超超临界汽轮机自启动控制系统策略,详细阐述了数字电液（DEH）控制系统自启动的控制步骤,X准则、Z准则和控制裕度的计算方法以及相应的控制策略,为国内同类型机组的自启动控制设计提供了一个可借鉴的思路。     

静止同步补偿器精确反馈线性化滑模控制研究

静止同步补偿器(Static Synchronous Compensator,STATCOM)的传统滑模控制方法结合了其他的线性控制方法,使得控制参数调节麻烦,不能达到滑模控制的良好效果,针对该问题提出了STATCOM输入状态精确反馈线性化的滑模控制策略,通过计算得到合适的输出函数,将原非线性模型全部转化为Brunovsky标准型,然后对Brunovsky标准型设计了滑模控制的滑模面和控制律并转化到原系统中,采用Lyapunov函数检验了系统的稳定性。最后,仿真验证了该控制器改进了传统滑模控制结合线性控制的缺陷,对系统变化的反应更灵敏。     

基于混合型逻辑数字控制的三相三线制并联型有源电力滤波器的研究

对一种由非线性负载产生的谐波电流的全部或特定频率进行补偿的并联型有源电力滤波器进行了研究，并应用数字信号处理器（DSP）对其进行混合型控制，可以容易地检测和验证对不同电流或直流电压控制算法的正确性．还应用正序检测器来减少传统的p-q算法在不对称情况下的延时．经试验研究分析，证明了这种设计和控制策略的有效性．     

ADAPTIVE INPUT–OUTPUT LINEARIZATION OF A pH NEUTRALIZATION PROCESS

Adaptive non-linear control strategies for a pH neutralization process are developed and evaluated via simulation. A non-adaptive non-linear controller is designed using a modified input–output linearization technique which accounts for the implicit output equation in the reaction invariant model. For simplicity the reaction invariants are assumed to be available for feedback. Because the model exhibits significant time-varying behaviour, the input–output linearizing controller is combined with non-linear parameter estimators which account for unmeasured buffering changes. Simulation results demonstrate that a novel indirect adaptive strategy is most suitable for experimental implementation where the reaction invariants must be estimated and sampling is required. © 1997 by John Wiley & Sons, Ltd.     

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.     

无位置传感器永磁无刷直流电机的起动控制研究

针对无位置传感器无刷直流电机存在的起动问题,根据无刷直流电机的起动换相时刻与直流母线电压直接相关的特点,提出一种插值起动方法。该方法首先通过改变母线电压值,采集相应的起动换相时刻数据样本。在离线情况下,以母线电压为输入,换相时刻作为输出,拟合出两者之间的3次样条插值函数。开环起动时,该方法能够准确计算出任意母线电压下电机的起动换相时刻;闭环起动时,随着PI调节器控制的母线电压有效值的变化,系统通过插值函数在线修正换相时刻,从而保证电机准确换相。起动完毕后,由软件将系统切换到反电势运行状态。仿真和实验表明,提出的插值起动方法,广泛适用于无位置传感器无刷直流电机的开环、闭环以及相应情况下的带负载起动。     

Coordinated Adaptive Control of Multiple Flexible AC Transmission Systems using Multiple-input—Multiple-output Neuro-fuzzy Damping Control Paradigms

This article presents a novel online adaptive non-linear multiple-input–multiple-output neuro-fuzzy control scheme for flexible AC transmission systems. The proposed control system synergistically integrates the time-frequency localization property of wavelets with locally controllable membership functions in the structure of neuro-fuzzy Takagi–Sugeno–Kang control. The proposed control scheme is checked for both multiple-input–single-output and multiple-input–multiple-output topologies. The performance of the proposed control system is validated against different contingencies and operating conditions using non-linear time-domain simulations and different performance indices. The results reveal that the proposed control scheme effectively damps the local and inter-area modes of oscillations.     

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.     

Blind identifiability of quadratic non-linear systems in higher-order statistics domain

Quadratic non-linear systems are widely used in various engineering fields such as signal processing, system filtering, predicting and identification. Some conditions to blindly estimate kernels of any discrete and finite extent quadratic system in the higher-order cumulants domain are introduced in this paper. The input signal is assumed as an unobservable i.i.d. random sequence which is viable for engineering practice. Due to properties of the output third-order cumulant functions, identifiability of the non-linear system holds even if the system's output measurement is corrupted by a Gaussian random disturbance. It provides a useful starting point for implementating the identification of a truncated Volterra non-linear system using conventional techniques or neural network methodologies. © 1998 John Wiley & Sons, Ltd.