Advanced methods of process computer control for industrial processes

In recent years, the requirements for the performance of multilevel process control, including feedforward and feedback control, monitoring and optimization have increased. Applying process computers and micro computers, the functions of analog equipment and hardwired logic devices cannot only be replaced. Extended or quite new methods can be realized improving the performance of multilevel process control. These advanced methods for process control are characterized by: more sophisticated, better adjusted control algorithms, forecasting of process variables, estimation of not directly measurable variables, computer aided design of algorithms and adaptive or selftuning algorithms. The basis of these advanced methods are mathematical models of the processes and their signals, often gained by the process computer itself during on-line operation.The present paper discusses first how process models in open and closed loop can be obtained by on-line identification methods. Then, based on these models, the computer aided design of control algorithms, adaptive control algorithms and adaptive steady-state on-line optimization will be regarded. Monitoring of not direct measurable variables will be mentioned. For some methods, practical results with real and simulated processes are shown. Interactive process computer software packages are used which can easily be transferred to other process computers.     

Adaptive neural network predictive control for nonlinear pure feedback systems with input delay

This paper presents an adaptive neural control design for nonlinear pure-feedback systems with an input time-delay. Novel state variables and the corresponding transform are introduced, such that the state-feedback control of a pure-feedback system can be viewed as the output-feedback control of a canonical system. An adaptive predictor incorporated with a high-order neural network (HONN) observer is proposed to obtain the future system states predictions, which are used in the control design to circumvent the input delay and nonlinearities. The proposed predictor, observer and controller are all online implemented without iterative predictive calculations, and the closed-loop system stability is guaranteed. The conventional backstepping design and analysis for pure-feedback systems are avoided, which renders the developed scheme simpler in its synthesis and application. Practical guidelines on the control implementation and the parameter design are provided. Simulation on a continuous stirred tank reactor (CSTR) and practical experiments on a three-tank liquid level process control system are included to verify the reliability and effectiveness.     

Adaptive Synchronization Control of the Magnetically Suspended Rotor System

An adaptive controlapproach to the implementation of synchronization control forthe magnetically suspended rotor system is presented. The controlsystem enables a translatory mode for the whirling rotor alongthe bearing axes, thereby eliminating the gyroscopic effectsthat can vastly degrade the rotor stability at high speeds. Thecontrol method features the integration of adaptive control andcross feedback control for the electromagnetic circuit, in whichan adjustment system is trained to tune the adaptation parametersto minimize the differential errors of state variables throughouta cross coupling controller. Under this control process, thecontrol variables are updated in a way to suppress the conicalmodes against the gyroscopic forces. This paper develops twokinds of synchronization control algorithms in conjunction withan optimization design, and the simulation of synchronizationcontrol for a magnetically suspended rotor system is presented.     

基于AFSMC算法的飞机飞行姿态自适应滑模控制系统

传统飞机飞行姿态滑膜控制系统,存在飞机飞行姿态自适应系数稳定性差的问题,在控制过程中会受到多重因素影响,导致飞行姿态可控误差系数增大,需要辅助控制系统修正才能完成飞行姿态的控制操作;针对上述问题,提出基于AFSMC算法的飞机飞行姿态自适应滑模控制系统;系统硬件基于PID自适应滑模控制器,对飞机飞行姿态控制器进行结构设计;软件部分通过引入自适应滑模控制策略,对PID控制器姿态控制变量进行适配;引入AFSMC算法计算姿态控制器当前时间点下的运动控制方程,得到飞行姿态自适应滑模控制的最优量,完成基于AFSMC算法的飞机飞行姿态自适应滑模控制系统设计;实验结果表明,所设计系统能够在不同飞行工况下,对飞机飞行姿态作出准确控制,系统的整体控制精度范围为90%~97.4%,飞机飞行控制稳定性较好,有效提升了系统对飞机飞行姿态的控制准确度。     

网络攻击下一类非线性切换多智能体系统的安全自适应控制

针对一类控制方向未知的非线性切换多智能体系统, 本文研究了在不确定网络攻击下的安全控制问题. 网 络攻击破坏传感器真实数据, 导致系统真实信息无法获取且不能直接用于控制设计. 为此, 通过受攻击状态构建一 组新颖辅助变量来消除网络攻击造成的影响. 此外, 所研究的系统包含更一般的不确定性, 即未知控制方向, 未知常 值参数以及不确定攻击. 这些不确定性在设计过程中相互耦合. 利用Nussbaum型函数并设计自适应律补偿耦合后 的不确定性, 极大降低了系统复杂性. 通过系统性地迭代构造出共同Lyapunov函数, 提出了一套安全自适应控制方 法, 保证了受攻击系统在任意切换下达到渐近输出一致性. 最后, 数值仿真验证了该方法的有效性.     

Output feedback control with disturbance rejection of a class of nonlinear MIMO systems

Output feedback control with disturbance rejection is developed for a class of nonlinear multi-input-multi-output (MIMO) systems. The availability of state variables and the bound of disturbances are not required to be known in advance. In the design of an adaptive observer, a robust adaptive nonlinear state feedback controller using the estimated states is proposed. The control methodology is robust to bounded disturbances that are both constant and time-varying, with effective performance. The adaptive laws are derived based on the Lyapunov synthesis method; therefore closed-loop asymptotic stability is also guaranteed. Moreover, chattering can be reduced by the proposed design approach. Simulation results are included to illustrate the effectiveness of the proposed controller. The text was submitted by the authors in English.     

Robust adaptive fault tolerant control for a process with actuator faults

This paper studies design and implementation of an enhanced multivariable adaptive control scheme for an uncertain nonlinear process exposed to actuator faults. For adaptive fault compensation, a model reference adaptive control (MRAC) strategy is utilized as main controller. A new adaptation algorithm making possible to improve transient performance of adaptive control is integrated to the controller. With the help of further modifications, some restrictive conditions on multivariable adaptive design are relaxed so that the system requires less plant information. The resulting controller has a simpler structure than the other matrix factorization based controllers. At the final stage of design, a robust adaptive control scheme is obtained with consideration of practical implementation problems such as sensor noises, external disturbances and unmodeled​ system dynamics. It is proved that the controller guarantees closed-loop signal boundedness and asymptotic output tracking. Real-time experiment results acquired from quadruple tank benchmark system are presented in order to exhibit the effectiveness of the proposed scheme.     

LMI-Based Adaptive Tracking Control for Parametric Strict-Feedback Systems

This paper presents an effective linear matrix inequality (LMI)-based adaptive scheme to design the output tracking controller for parametric strict-feedback systems. In our previous paper, the concepts of virtual desired variables (VDVs) and, in turn, the so-called generalized kinematics are introduced to benefit the tracking design. All the VDVs can be determined without fail from the generalized kinematics via a newly defined recursive procedure if the systems are in strict-feedback form. The proposed adaptive tracking control is investigated for single-input and multi-input systems with unknown parameters. Different from the existent adaptive fuzzy control, our research is an exact approach, compared with the approach using universal approximation. Relative to the adaptive backstepping control, it makes the design procedure more straightforward. Moreover, we propose a new method called balance technique to deal with the infeasibility of LMIs for the specified structure of common positive definite matrix $P$, and adopt an overparameterization technique to make sure that the VDVs can be well-defined. From the numerical simulations, it is shown that the proposed scheme is very powerful with expected satisfactory performance.      

Design optimization of balloon-expandable coronary stent

To prevent the dogboning effect of stent implantation (i.e. the ends of a stent opening first during expansion), an adaptive optimization method based on the kriging surrogate model is proposed to reduce the absolute value of the dogboning rate. Integrating design of experiment (DOE) methods with the kriging surrogate model can approximate the functional relationship between the dogboning rate and the geometrical design parameters of the stent, replacing the expensive reanalysis of the stent dogboning rate during the optimization process. In this adaptive process, an infilling sampling criterion termed expected improvement (EI) is used to balance local and global search and tends to find the global optimal design. Finite element method is used to analyze stent expansion. As an example, a typical diamond-shaped coronary stent is investigated, where four key geometries are selected to be the design variables. Numerical results demonstrate that the proposed adaptive optimization method can effectively decrease the absolute value of the dogboning rate of stent dilation.     

Feedback Control Architecture and Design Methodology for Service Delay Guarantees in Web Servers

This paper presents the design and implementation of an adaptive Web server architecture to provide relative and absolute connection delay guarantees for different service classes. The first contribution of this paper is an adaptive architecture based on feedback control loops that enforce desired connection delays via dynamic connection scheduling and process reallocation. The second contribution is the use of control theoretic techniques to model and design the feedback loops with desired dynamic performance. In contrast to heuristics-based approaches that rely on laborious hand-tuning and testing iteration, the control theoretic approach enables systematic design of an adaptive Web server with established analytical methods. The adaptive architecture has been implemented by modifying an Apache server. Experimental results demonstrate that the adaptive server provides robust delay guarantees even when workload varies significantly.     

块控非线性系统自适应神经网络控制

针对一类含有非匹配不确定性的块控型多输入多输出非线性系统,提出一种基于反演技术和RBF神经网络的控制系统设计方案.通过引入一种改进型的Lyapunov函数,避免了控制矩阵未知情况下可能出现的奇异问题.在控制系统设计过程中,充分应用鲁棒自适应控制技术,解决了多输入多输出结构不确定性所带来的设计难题,得到了系统所有状态量将全局指数收敛至原点附近一个邻域的结论.最后的仿真结果表明了设计方案的正确性.     

Adaptive stabilization of a class of nonlinear systems with unstable internal dynamics

This note considers global stabilization of a class of uncertain nonlinear output feedback systems with unstable internal dynamics. The coefficients which characterize the internal dynamics are allowed to be functions of system output, and the uncertainty of the system is characterized by an unknown constant parameter vector. The key step in the proposed control design is to estimate the internal state variables and impose control over them. The control design is presented first for systems without unknown parameters, and then for the systems with unknown parameters using adaptive control techniques.     

A time-optimal control algorithm for digital computer control

A time-optimal control algorithm for digital computer control allowing bounds on control variables and state variables is presented. Through linear programming techniques a time-optimal sequence is computed by using a linear discrete model of the process. For real-time applications feedback control is achieved by recalculating the control sequence each sampling period. In addition, an adaptive control strategy based on on-line estimation of the state variables and the parameters of the system is introduced. In order to be able to apply the algorithm in a real-time environment, computational efficiency is emphasized. An application of the algorithm to a sixth-order multivariable system is given.     

Control of an anaerobic digester through normal form of fold bifurcation

Nonlinear dynamics is ubiquitous in engineering systems. As some parameters are varied bifurcations arise in the state variables. Generically, when one parameter changes, Hopf and fold bifurcations are found. Other ones can also be present due to special systems characteristics, such as symmetries. Knowing in advance the significant bifurcation scenario, a novel approach to control can be considered. We compute the normal form corresponding to such a bifurcation and we take this model as the nominal model of the plant. Then we design a nonlinear control which takes advantage of the precise bifurcation scenario. This general method is applied, in this paper, to an anaerobic digester. We will control the process with an adaptive controller.Specifically, we want to compare with the case that the nominal plant is considered as a linear model, such as it is typical in adaptive control techniques. Our proposed method has more benefits in signal control effort, faster convergence rate and low error.This paper shows how the combination of appropriated nonlinear dynamic techniques such as bifurcations and normal forms, and nonlinear control, can give rise to an improvement of the traditional methodology.     

A Bayesian approach to design of adaptive multi-model inferential sensors with application in oil sand industry

In the context of process industries, online monitoring of quality variables is often restricted by inadequacy of measurement techniques or low reliability of measuring devices. Therefore, there has been a growing interest in the development of inferential sensors to provide frequent online estimates of key process variables on the basis of their correlation with real-time process measurements. Representation of multi-modal processes is one of the challenging issues that may arise in the design of inferential sensors. In this paper, Bayesian procedures for the development and implementation of adaptive multi-model inferential sensors are presented. It is shown that the application of a Bayesian scheme allows for accommodating the overlapping operating modes and facilitating the inclusion of prior knowledge. The effectiveness of the proposed procedures are first demonstrated through a simulation case study. The efficacy of the method is further highlighted by a successful industrial application of an adaptive multi-model inferential sensor designed for real-time monitoring of a key quality variable in an oil sands processing unit.     

Adaptive Fuzzy Backstepping Tracking Control for Flexible Robotic Manipulator

In this paper, an adaptive fuzzy state feedback control method is proposed for the single-link robotic manipulator system. The considered system contains unknown nonlinear function and actuator saturation. Fuzzy logic systems (FLSs) and a smooth function are used to approximate the unknown nonlinearities and the actuator saturation, respectively. By combining the command-filter technique with the backstepping design algorithm, a novel adaptive fuzzy tracking backstepping control method is developed. It is proved that the adaptive fuzzy control scheme can guarantee that all the variables in the closed-loop system are bounded, and the system output can track the given reference signal as close as possible. Simulation results are provided to illustrate the effectiveness of the proposed approach.      

High performance nonlinear adaptive control of temperature in cryogenic wind tunnel

A new nonlinear adaptive control method based on Immersion and Invariant (I&I) approach is proposed for the temperature control of a cryogenic wind tunnel. The proposed control method can be applied to wide range temperature operation of the tunnel by incorporating the nonlinear dynamic model of temperature into the controller design. By constructing globally stable nonlinear observer, and rendering an invariant and attractive manifold in the state space of plant and observer, the uncertain gain of known disturbance and unknown wind tunnel wall temperature are considered in the same manner within the adaptive I&I control frame. In the design, nominal heat transfer coefficient is deployed to avoid complicated design process involving nonlinear parameterization. This design leads to an adaptive output feedback stabilization control law for the temperature with guaranteed transient and steady performance, which exhibits reasonable robustness to time delay in the control input channel and other uncertainties. The analysis and simulation show the effectiveness of the proposed control methodology.     

An adaptive region segmentation combining surrogate model applied to correlate design variables and performance parameters in a transonic axial compressor

Surrogate models have been widely applied to correlate design variables and performance parameters in turbomachinery optimization applications. With more design variables and uncertain factors taken into account in an optimization design problem, the mathematical relations between the design variables and the performance parameters might present linear, low-order nonlinear or even high-order nonlinear characteristics, and are usually analytically unknown. Therefore, it is required that surrogate models have high adaptability and prediction accuracy for both the linear and nonlinear characteristics. The paper mainly investigates the effectiveness of an adaptive region segmentation combining surrogate model based on support vector regression and kriging model applied to a transonic axial compressor to approximate the complicated relationships between geometrical variables and objective performance outputs with different sampling methods and sizes. The purpose is to explore the prediction accuracy and computational efficiency of this adaptive surrogate model in real turbomachinery applications. Three different sampling techniques are studied: (1) uniform design; (2) Latin hypercube sampling method; (3) Sobol quasi-random design. For the low dimensional case with five variables, the adaptive region segmentation combining surrogate model performs better (not worse) than the single component surrogate in terms of prediction accuracy and computational efficiency. In the meanwhile, it is also noted that the uniform design applied to the adaptive surrogate model has more advantages over the Latin hypercube sampling method especially for the small sample size cases, both performing better than the Sobol quasi-random design. Moreover, a high dimensional case with 12 variables is also utilized to further validate the prediction advantage of the adaptive region segmentation combining surrogate model over the single component surrogate, and the computational results favor it. Overall, the adaptive region segmentation combining surrogate model has produced acceptable to high prediction accuracy in presenting complex relationships between the geometrical variables and the objective performance outputs and performed robustly for a transonic axial compressor problem.

     

Output regulation of uncertain nonlinear systems with nonlinear exosystems

An adaptive control algorithm is proposed for output regulation of uncertain nonlinear systems in output feedback form under disturbances generated from nonlinear exosystems. A new nonlinear internal model is proposed to generate the desired input term for suppression of the disturbances. The proposed internal model design is based on boundedness of the disturbance, high gain design and saturation. It is capable to tackle disturbances in any specified initial conditions. Some uncertainties in the systems are allowed, provided that they do not affect the desired feedforward control term, and they are tackled by using nonlinear dominant functions and an adaptive control coefficient. The proposed control algorithm ensures the global convergence of the state variables to the invariant manifold, which implies that the measurement or the tracking error approaches to zero asymptotically.