Active disturbance rejection control for power plant with a single loop

The cascade proportional‐integral‐derivative (PID) control method is widely used in power plant control field. In this paper, an active disturbance rejection control (ADRC) for a power plant with a single loop is introduced for eliminating the shortcomings of the cascade PID control method. The proposed controller based on the ADRC method consists of a tracking differentiator (TD), an extended state observer (ESO) and a nonlinear combination of errors. In this approach, the processes with higher orders, uncertainties and unmodeled dynamics are viewed as lower‐ordered systems with general disturbances, and the general disturbances are estimated by ESO, and then actively compensated. Besides, only one measured output is needed in the proposed method instead of two or three in the cascade PID control method. Some simulation studies compared with the regular cascade PID control method show the proposed method has better performance beyond the regular cascade PID control method. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

基于快速输出采样反馈的网络控制系统滑模控制

针对网络控制系统的网络诱导时延问题,提出了一种新的滑模控制方法。通过快速采样被控对象输出,建立了具有线性时不变被控对象的网络控制系统的离散模型。在此基础上,用极点配置法给出了滑模切换面参数,设计了输出反馈离散滑模控制器。仿真结果验证了该控制方法的有效性。     

原料预热温度的模糊PID-神经元控制

针对具有不确定性、大纯滞后的催化裂化反应再生装置原料预热温度控制,提出了一种模糊PID-神经元控制方法.从介绍催化裂化反应再生装置原料预热被控对象的建模、神经元非模型控制和公式化的模糊控制方法人手,建立了模糊PID-神经元控制系统,设计了模糊神经元混合控制器,并使用神经元来在线调整模糊PID控制器的模糊规则.仿真实验结果表明所提出的模糊PID-神经元控制方法具有强鲁棒性,能有效控制具有大纯滞后和不确定性的对象.     

Fuzzy gain-scheduling control of a gas-liquid separation plant implemented on a PLC

In the paper an approach to the non-linear control of a gas-liquid separation plant is presented. To solve the problem the fuzzy gain-scheduling method was used and implemented on a programmable logic controller (PLC). Velocity-based linearization approach was used for control system design and implementation for the first time in process control application. Emphasis was placed on the appropriate adaptation of the method and some important implementation issues. The algorithm was carefully designed and tested by using ordinary simulation, hardware-in-the-loop simulation, and experiments on the real plant. The results show that the proposed approach substantially improves the closed-loop performance of the plant.     

Predictive functional control based on fuzzy model forheat-exchanger pilot plant

In this paper, a new method of predictive control is presented. In this approach, a well-known method of predictive functional control is combined with fuzzy model of the process. The prediction is based on fuzzy model given in the form of Takagi-Sugeno type. The proposed fuzzy predictive control has been evaluated by implementation on heat-exchanger plant, which exhibits a strong nonlinear behavior. It has been shown that in the case of nonlinear processes, the approach using fuzzy predictive control gives very promising results. The proposed approach is potentially interesting in the case of batch reactors, heat-exchangers, furnaces, and all the processes that are difficult to model     

Multirate digital control system design

Methods for multirate digital control system design are discussed. A simple method for sampling rate selection based on control bandwidths is proposed. Methods for generating a discrete-time state model of a sampled-data plant and a discrete-time equivalent to an analog cost function for a sampled-data plant are described. The successive loop closures and linear quadratic Gaussian synthesis methods are reviewed, and a constrained optimization synthesis method is introduced. The proposed sampling rate selection, discretization, and synthesis methods are applied to two example design problems. Multirate and single-rate compensators synthesized by the different methods are compared based on closed-loop responses with compensators having the same real-time computation load     

A practical approach to disturbance decoupling control

In this paper, a unique dynamic disturbance decoupling control (DDC) strategy, based on the active disturbance rejection control (ADRC) framework, is proposed for square multivariable systems. With the proposed method, it is shown that a largely unknown square multivariable system is readily decoupled by actively estimating and rejecting the effects of both the internal plant dynamics and external disturbances. By requiring as little information on plant model as possible, the intention is to make the new method practical. The stability analysis shows that both the estimation error and the closed-loop tracking error are bounded and the error upper bounds monotonously decrease with the bandwidths. Simulation results obtained on two chemical process problems show good performance in the presence of significant unknown disturbances and unmodeled dynamics.     

一类非自衡对象的最优控制

对模型不确定性和控制能量约束并存时一类非自衡对象的最优控制问题作了探讨．首先基于一类随机模型误差的描述,定义了一个平均意义上的包含跟踪误差和控制能量在内的性能指标;然后通过谱分解极小化该性能指标,导出一个最优的控制律,从而为模型不确定性和控制能量约束并存条件下一类非自衡对象的最优控制问题提供了一种实用的控制方法．仿真结果证明了该方法的有效性．     

An improved state-space model structure and a corresponding predictive functional control design with improved control performance

Conventional state-space model predictive control requires a state estimator/observer to access the state information for feedback controller design. Its drawbacks are the numerical convergence stability of the observer and closed-loop control performance deterioration with activated plant input/output constraints. The recent direct use of measured input and output variables to formulate a non-minimal state-space (NMSS) model overcomes these problems, but the subsequent controller is too sensitive to model mismatch. In this article, an improved structure of NMSS model that incorporates the output-tracking error is first formulated and then a subsequent predictive functional control design is proposed. The proposed controller is tested on both model match and model mismatch cases for comparison with previous controllers. Results show that control performance is improved. In addition, a linear programming method for constraints dealing and a closed form of transfer function representation of the control system are provided for further insight into the proposed method.     

非线性动态系统神经模糊建模与内模/PID双重控制系统设计

非线性动态系统的内模控制要求建立精确的对象正模型和逆模型,这对于大多数实际对象是难以做到.提出了基于一类神经模糊模型的非线性动态系统建模方法,并在此基础上研究了基于神经模糊模型的非线性系统的内模控制设计.基于输入输出数据辨识的对象正模型和逆模型存在着模型失配问题,导致神经模糊内模控制范围变窄和控制鲁棒性降低,为了改善系统的性能,提出了神经模糊内模控制与PID控制结合的双重控制策略.对CSTR的反应物浓度控制研究表明,双重控制策略能有效地拓宽系统可控范围,改善系统性能.仿真结果证明该控制策略简单而有效.     

Intelligent fault-tolerant system of vibration control for flexible structures

This paper describes an intelligent fault-tolerant control method for vibration control of flexible structures. We consider a case where the fault phenomena of the control system for flexible structures can be treated as a change of system parameters. Therefore, the adaptive control method can be applied to a vibration control system for flexible structures with a fault. In this paper, a neural network (NN) adaptive control system is used to compensate for the change in the parameters of a plant with a fault. When the characteristics of the plant and of a nominal model have been agreed by a NN adaptive control system, the control method designed for the nominal model, such as decoupling feedback control or linearizing feedback control, can be used even if the change in the system parameters has been caused by a fault. To confirm the effectiveness of the proposed fault-tolerant control method, the simulational results from a 5-link robotic arm are shown at the end of the paper. This work was presented, in part, at the Fourth International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–22, 1999     

线性自抗扰控制的适用性及整定

线性自抗扰控制将被控对象看成串级积分系统,把其他信息都当成不确定性.这种处理方法简单,但是对什么样的系统有效,目前还没有理论给出确定的答案.本文证明任何带有积分行为的严格正则传递函数都可以由线性自抗扰控制的反馈控制器等价实现,从而表明线性自抗扰控制具有广泛的适用性,即只要其他线性控制方法能够控制的系统,线性自抗扰控制同样可以适用.为简化线性自抗扰控制器参数整定,本文针对工业过程中广泛存在的PID控制器,提出将PID参数转化为二阶自抗扰控制参数的方法.该方法转化的线性自抗扰参数以带宽形式表示,从而保留了传统线性自抗扰简单易调的特性,为线性自抗扰控制在工业过程的应用准备了基础.     

Robust synergetic control design under inputs and states constraints

In this paper, a novel robust-constrained control methodology for discrete-time linear parameter-varying (DT-LPV) systems is proposed based on a synergetic control theory (SCT) approach. It is shown that in DT-LPV systems without uncertainty, and for any unmeasured bounded additive disturbance, the proposed controller accomplishes the goal of stabilising the system by asymptotically driving the error of the controlled variable to a bounded set containing the origin and then maintaining it there. Moreover, given an uncertain DT-LPV system jointly subject to unmeasured and constrained additive disturbances, and constraints in states, input commands and reference signals (set points), then invariant set theory is used to find an appropriate polyhedral robust invariant region in which the proposed control framework is guaranteed to robustly stabilise the closed-loop system. Furthermore, this is achieved even for the case of varying non-zero control set points in such uncertain DT-LPV systems. The controller is characterised to have a simple structure leading to an easy implementation, and a non-complex design process. The effectiveness of the proposed method and the implications of the controller design on feasibility and closed-loop performance are demonstrated through application examples on the temperature control on a continuous-stirred tank reactor plant, on the control of a real-coupled DC motor plant, and on an open-loop unstable system example.     

带扰动模型失配史密斯预估器的自适应控制

在扰动情况下,针对传统史密斯预估器的模型失配问题,提出了一种基于模型参考自适应控制的史密斯预估器。首先,在控制对象输入端添加前馈增益矩阵和反馈补偿矩阵,和控制对象相互结合,通过调整矩阵参数,使控制对象和预估模型匹配,消除系统的模型失配误差。其次,在前向控制器输出端引入扰动补偿矩阵,调整扰动补偿矩阵的相关参数,对系统的扰动进行补偿。最后,选取合适的李雅普诺夫函数,求取自适应率。利用MATLAB中的SIMULINK模块进行仿真,仿真结果验证了方法的有效性。     

基于模糊自整定PID的主汽温串级控制系统的设计

火电厂主汽温具有大惯性、大迟延和时变特性等特性,采用常规的PID串级控制难以获得满意的控制效果.为此,提出一种基于模糊自整定PID控制器的串级控制系统,该系统将模糊控制、PID控制和串级控制三者有机地相结合,提高了主汽温控制系统的控制品质.仿真结果表明：系统超调量、调节时间和抗干扰能力明显优于常规的PID串级控制.     

A neural network assisted computed torque method for manipulator tracking control problems

Based on the structure of the computed torque method, this paper develops a neural control method for manipulator trajectory tracking problems. Compared with conventional approaches, the proposed method has the following novel features. First, by using a neural network as the plant model, the proposed method can handle structural as well as unstructural uncertainties. Secondly, with a simple compensation scheme, the proposed approach can effectively avoid tracking performance degradation caused by unmodelled dynamics and/or neural network learning errors. Computer simulation results are given to demonstrate the feasibility of the proposed control method.     

Real-time torque control using discrete-time recurrent high-order neural networks

This paper presents a discrete-time direct current (DC) motor torque tracking controller, based on a recurrent high-order neural network to identify the plant model. In order to train the neural identifier, the extended Kalman filter (EKF) based training algorithm is used. The neural identifier is in series-parallel configuration that constitutes a well approximation method of the real plant by the neural identifier. Using the neural identifier structure that is in the nonlinear controllable form, the block control (BC) combined with sliding modes (SM) control techniques in discrete-time are applied. The BC technique is used to design a nonlinear sliding manifold such that the resulting sliding mode dynamics are described by a desired linear system. For the SM control technique, the equivalent control law is used in order to the plant output tracks a reference signal. For reducing the effect of unknown terms, it is proposed a specific desired dynamics for the sliding variables. The control problem is solved by the indirect approach, where an appropriate neural network (NN) identification model is selected; the NN parameters (synaptic weights) are adjusted according to a specific adaptive law (EKF), such that the response of the NN identifier approximates the response of the real plant for the same input. Then, based on the designed NN identifier a stabilizing or reference tracking controller is proposed (BC combined with SM). The proposed neural identifier and control applicability are illustrated by torque trajectory tracking for a DC motor with separate winding excitation via real-time implementation.     

Synthesis of optimal control using neural network with mixed structure

This paper proposes firstly to use a neural network with a mixed structure for learning the system dynamics of a nonlinear plant, which consists of multilayer and recurrent structure. Since a neural network with a mixed structure can learn time series, it can learn the dynamics of a plant without knowing the plant order. Secondly, a novel method of synthesizing the optimal control is developed using the proposed neural network. Procedures are as follows: (1) Let a neural network with a mixed structure learn the unknown dynamics of a nonlinear plant with arbitrary order, (2) after the learning is completed, the network is expanded into an equivalent feedforward multilayer network, (3) it is shown that the gradient of a criterion functional to be optimized can be easily obtained from this multilayer network, and then (4) the optimal control is generated by applying any of the existing non-linear programming algorithm based on this gradient information. The proposed method is successfully applied to the optimal control synthesis problem of a nonlinear coupled vibratory plant with a linear quadratic criterion functional.     

Automatic controller tuning via unfalsified control

In this paper, we present a new algorithm for the automatic adaptation of linear single-loop controllers. The algorithm is based on the idea of unfalsified control, where a set of controllers is defined and using measured data, the controller from the set is chosen online which provides the best performance. This scheme is extended here by an adaptation of the set of controllers after a change of the operating point of the plant. The original concept of unfalsified control relies on the computation of the performance of controllers that are not in the loop using so-called fictitious signals. However, the proposed cost function leads to erroneous results, in particular, it is not possible to detect closed-loop instability that may result from inserting a controller. Therefore a different cost function and a new method for online evaluation of controllers that are not in the loop without an explicit plant model are proposed. With this new cost function, the set of controllers can be adapted as well which is performed using an evolutionary algorithm. The method is demonstrated for a well-known example, the nonlinear non-minimum phase CSTR model with van der Vusse reaction scheme and PID controllers.