Probe test yield optimization based on canonical correlation analysis between process control monitoring variables and probe bin variables

Process control monitoring (PCM) data provide information that is used to track abnormal processes and estimate various probe bin yields. However, multi-dimensional information has not yet been fully utilized from both PCM data and probe bins. In this paper, we proposed a canonical correlation analysis in order to investigate the relationship between multiple PCM variables and various probe bin variables. Polynomial regression was also employed as a methodology for maximizing the performance yield based on the results of the canonical correlation analysis. Two conclusions were drawn from the results of this research. First, the PCM variables that affected the probe bins were contact resistance, sheet resistance, and Isat_P4H as well as threshold voltage (Vt) during the process tuning step. Second, the typical values of Vtl_P4H and Isat_P4H should be changed in order to maximize the performance yield. The proposed method can be used for yield improvement and as a problem-solving approach for optimizing the IC process.     

Wavelet based approximations in the optimal control of parabolic problems

We consider optimal control of a bilinear parabolic equation. The determination of such control requires to minimize a given energy performance measure. The performance measure of the system is taken as a combination of its modified total energy and the penalty term describing the approach used in the control process. Using an appropriate transformation modal expansion, the optimal control of a distributed parameter system (DPS) is simplified into the optimal control of a bilinear time-varying lumped parameter system (LPS). A computational efficient formulation to evaluate the optimal trajectory and control of the system is determined. It is based on the parametrization of the state and control variables by using finite wavelets. Numerical examples are provided to demonstrate the applicability and the efficiency of the proposed method and the results are quite satisfactory.     

On-line PI control of stable processes

A relay based on-line automatic tuning method for PI controllers for stable processes is presented. In the proposed method, prior to controller re-tuning a relay in tandem with the controller and plant induces limit cycle oscillations. Based on the limit cycle measurements, a first order plus dead time (FOPDT) model of the process dynamics is obtained. Simple tuning rules based on ISTE performance criterion and the first order model are developed. The controller settings may be re-tuned non-iteratively to achieve enhanced performance without disrupting closed loop control. A number of simulation examples are given to illustrate the potential advantages of the proposed on-line tuning method.     

A new control method for MIMO first order time delay non-square systems

This paper proposes a new method using internal model control (IMC) to design Smith delay compensation decoupling controller for multivariable non-square systems with transfer function elements consisting of first order + time delay. This proposed method is applied to a shell control problem in multiple-input-multiple-output (MIMO) first order plus dead time non-square systems in which the number of input variables exceeds the number of output variables, with input and output variables being 3 and 2 respectively. This method does not only dynamically compensate for shortcoming caused by static decoupling but also overcomes the impact of model error on system performance caused by model approximation and uncertainty. In other words, the design method proposed in this paper is capable of significantly improving dynamic quality and robustness of the control system as can be seen from the simulation results. Moreover, this new method is simple and easy to implement. Integral of squared error (ISE) performance criterion is employed to quantitatively evaluate the design method.     

Self-organizing neural control system design for dynamic processes

This paper discusses the achievable nominal performance of a well-parametrized neural feedback control system, and proposes an efficient training method for parametrizing such a controller. A self-organizing neural control (SONC) system is presented in which a layered feedforward neural network is adopted as the controller structure in order to apply directly existing back-propagated learning techniques. A self-organizing methodology is introduced to provide the training set for adjusting parameters of the neural controller. One important feature of the proposed adaptive mechanism is that, though it should lack extensive knowledge of the process dynamics at the outset of controller design, it will still be able to achieve its desired results by employing the subjective experience of control specialists as its training aids. Tuning variables of the SONC system are reviewed through exploring their effects on five typical transfer functions. The applicability of the SONC system is also demonstrated on a continuous stirred tank reactor. Simulation results show that a well-parametrized neural controller can improve nominal performance for a wide variety of different processes, and the proposed self-organizing mechanism can direct a controller to achieve the desired final parametrization.     

LQ最优控制系统中加权阵的确定

本文研究了LQ最优调节器的逆问题.在控制变量加权矩阵R给定的条件下,通过引入 一组自由变量,给出了满足闭环系统特征值要求的状态加权矩阵Q的一种参数化表示结果.基 于这种结果,研究了LQ逆问题的矩阵变换解法和一类系统的LQ逆问题的解法.此外,文中 还给出了不求解代数矩阵Riccati方程确定系统的最优状态反馈系数矩阵K的方法.     

Bayesian estimation of unrestricted and order-restricted association models for a two-way contingency table

In two-way contingency tables analysis, a popular class of models for describing the structure of the association between the two categorical variables are the so-called “association” models. Such models assign scores to the classification variables which can be either fixed and prespecified or unknown parameters to be estimated. Under the row-column (RC) association model, both row and column scores are unknown parameters without any restriction concerning their ordinality. It is natural to impose order restrictions on the scores when the classification variables are ordinal. The Bayesian approach for the RC (unrestricted and restricted) model is adopted. MCMC methods are facilitated in order the parameters to be estimated. Furthermore, an alternative parametrization of the association models is proposed. This new parametrization simplifies computation in the MCMC procedure and leads to a natural parameter space for the order constrained model. The proposed methodology is illustrated via a popular dataset.     

Iterative motion feedforward tuning: A data-driven approach based on instrumental variable identification

Feedforward control can significantly enhance the performance of motion systems through compensation of known disturbances. This paper aims to develop a new procedure to tune a feedforward controller based on measured data obtained in finite time tasks. Hereto, a suitable feedforward parametrization is introduced that provides good extrapolation properties for a class of reference signals. Next, connections with closed-loop system identification are established. In particular, instrumental variables, which have been proven very useful in closed-loop system identification, are selected to tune the feedforward controller. These instrumental variables closely resemble traditional engineering tuning practice. In contrast to pre-existing approaches, the feedforward controller can be updated after each task, irrespective of noise acting on the system. Experimental results confirm the practical relevance of the proposed method.     

Adaptive fuzzy identification and predictive control for industrial processes

This paper proposes a method for adaptive identification and control for industrial applications. The learning of a T–S fuzzy model is performed from input/output data to approximate unknown nonlinear processes by a hierarchical genetic algorithm (HGA). The HGA approach is composed by five hierarchical levels where the following parameters of the T–S fuzzy system are learned: input variables and their respective time delays, antecedent fuzzy sets, consequent parameters, and fuzzy rules. In order to reduce the computational cost and increase the algorithm’s performance an initialization method is applied on HGA. To deal with nonlinear plants and time-varying processes, the T–S fuzzy model is adapted online to maintain the quality of the identification/control. The identification methodology is proposed for two application problems: (1) the design of data-driven soft sensors, and (2) the learning of a model for the Generalized predictive control (GPC) algorithm. The integration of the proposed adaptive identification method with the GPC results in an effective adaptive predictive fuzzy control methodology. To validate and demonstrate the performance and effectiveness of the proposed methodologies, they are applied on identification of a model for the estimation of the flour concentration in the effluent of a real-world wastewater treatment system; and on control of a simulated continuous stirred tank reactor (CSTR) and on a real experimental setup composed of two coupled DC motors. The results are presented, showing that the developed evolving T–S fuzzy model can identify the nonlinear systems satisfactorily and it can be used successfully as a prediction model of the process for the GPC controller.     

Robust stabilization for a class of nonlinear networked control systems

The problem of robust stabilization for a class of uncertain networked control systems （NCSs） with nonlinearities satisfying a given sector condition is investigated in this paper. By introducing a new model of NCSs with parameter uncertainty, network-induced delay, nonlinearity and data packet dropout in the transmission, a strict linear matrix inequality （LMI） criterion is proposed for robust stabilization of the uncertain nonlinear NCSs based on the Lyapunov stability theory. The maximum allowable transfer interval （MATI） can be derived by solving the feasibility problem of the corresponding LMI. Some numerical examples are provided to demonstrate the applicability of the proposed algorithm.     

Detection of model-plant mismatch in closed-loop control system

The performance of model-based control systems depends a lot on the process model quality, hence the process model-plant mismatch is an important factor degrading the control performance. In this paper, a new methodology based on a process model evaluation index is proposed for detecting process model mismatch in closed-loop control systems. The proposed index is the ratio between the variance of the disturbance innovation and that of the model quality variable. The disturbance innovations are estimated from the routine operation data by an orthogonal projection method. The model quality variable can be obtained using the closed-loop data and the disturbance model estimated by adaptive Least absolute shrinkage and selection operator (Lasso) method. When the order of the disturbance model is less than 2 or the process time delay is large enough, no external perturbations are required. Besides, the proposed index is independent of the controller tuning and insensitive to the changes in disturbance model, which indicates that the proposed method can isolate the process model-plant mismatch from other factors affecting the overall control performance. Three systems with proportional integral (PI) controller, linear quadratic (LQ) controller and unconstrained model predictive control (MPC) respectively are presented as examples to verify the effectiveness of the proposed technique. Besides, Tennessee Eastman process shows the proposed method is able to detect process model mismatch of nonlinear systems.     

Precise control of linear systems subject to actuator saturation using tracking differentiator and reduced order composite nonlinear feedback control

Aiming at the actuator saturation problem and the system performance requirement, this article proposes a new control scheme, i.e. a newly developed tracking differentiator-composite nonlinear feedback (TDCNF) control law, which is the combination of a tracking differentiator (TD) and a reduced order composite nonlinear feedback (CNF) control law. The TD, used here, mainly helps to provide a smooth reference signal and largely avoid actuator saturation. The reduced order CNF control law, on the one hand, estimates those unmeasurable state variables for measurement feedback control and, on the other hand, ensures satisfactory system performance. The stability of the newly developed TDCNF is proved in detail. Finally, to verify the effectiveness of the newly developed TDCNF control law, two illustrative examples are demonstrated and therein a novel design of the proposed control law is given. Simulation results show that the proposed control law can achieve good tracking performance and effectively avoid the actuator saturation.     

Rational Parametrization of Surfaces

The parametrization problem asks for a parametrization of an implicitly given surface, in terms of rational functions in two variables. We give an algorithm that decides if such a parametric representation exists, based on Castelnuovo's rationality criterion. If the answer is yes, then we compute such a parametric representation, using the concept of adjoint functions.     

A new control method of nonlinear systems based on impulseresponses of universal learning networks

A new control method of nonlinear dynamic systems is proposed based on the impulse responses of universal learning networks (ULNs), ULNs form a superset of neural networks. They consist of a number of interconnected nodes where the nodes may have any continuously differentiable nonlinear functions in them and each pair of nodes can be connected by multiple branches with arbitrary time delays. A generalized learning algorithm is derived for the ULNs, in which both the first order derivatives (gradients) and the higher order derivatives are incorporated. One of the distinguished features of the proposed control method is that the impulse response of the systems is considered as an extended part of the criterion function and it can be calculated by using the higher order derivatives of ULNs. By using the impulse response as the criterion function, nonlinear dynamics with not only quick response but also quick damping and small steady state error can be more easily obtained than the conventional nonlinear control systems with quadratic form criterion functions of state and control variables.     

Delay-dependent H-infinity control for continuous time-delay systems via state feedback

The delay-dependent H-infinity analysis and H-infinity control problems for continuous time-delay systems are studied. By introducing an equality with some free weighting matrices, an improved criterion of delay-dependent stability with H-infinity performance for such systems is presented, and a criterion of existence and some design methods of delay-dependent H-infinity controller for such systems are proposed in term of a set of matrix inequalities, which is solved efficiently by an iterative algorithm. Further, the corresponding results for the delay-dependent robust H-infinity analysis and robust H-infinity control problems for continuous time-delay uncertain systems are given. Finally, two numerical examples are given to illustrate the efficiency of the proposed method by comparing with the other existing results.     

Optimum design of linear multivariate sampled-data control systems†

This paper deals with the optimum design of linear multivariate sampled-data control systems,

Section 2 is concerned with the design of such control systems which are optimum on the basis of deadbeat performance. This design procedure is general and can be applied similarly to any deterministic inputs such as step, ramp, etc.

Section 3 is concerned with such control systems with respect to an integral form performance criterion. It is desired that the performance index takes the form of an integral, especially when the state variables of a control system are continuous with respect to time. For this reason, the minimum integral control is considered here.

The performance limits for such systems optimized for a deterministic input are considered in §4, and some interesting theorems are proved by using the w-transform.     

Efficient large payloads ternary matrix embedding

Matrix embedding (ME) can be exploited to improve the embedding efficiency for steganography by making fewer changes to the cover data. In ME, the sender and recipient agree on a parity check matrix (PCM) in advance. The PCM will be used by the sender to embed secret message into the cover data and later by the decoder to extract the embedded message. The embedding performance of ME is greatly influenced by the PCM thus the choice of PCM is crucial for ME. On the other hand, since larger sized PCM usually leads to higher embedding efficiency, how to keep the balance between the computational complexity and the embedding efficiency is also an important problem of ME. Based on these considerations, an efficient ternary ME method for large payloads data embedding is proposed in this paper. We utilize a specific matrix construction for PCM to improve embedding efficiency and a sub-optimal search strategy to reduce the computational complexity. The experimental results show that the proposed method achieves good embedding efficiency at low time cost and it outperforms some state-of-the-art works. For example, for a cover image with 512 × 512 pixels at an embedding rate of 1 bit per pixel, the proposed method can be implemented within 0.5 second in a personal computer with a rather high embedding efficiency as 3.89.     

Experimental Assessment of the Period Calibration Method: A Case Study

In this paper we present an experimental evaluation of the period calibration method (PCM) which was developed in Gerber et al. (1994, 1995) as a systematic design methodology for real-time systems. The objective of this experimental study is to assess design alternatives integrated into the method and their performance implication on resultant systems built via the PCM. Such design alternatives include scheduling jitter, sensor-to-output latency, intertask communication schemes, and system utilization. For this study, we have chosen a computerized numerical control (CNC) machine as our target real-time system, and built a realistic controller and a plant simulator. We show the detailed development process of the CNC controller and report its performance. The performance results were extracted from a controlled series of more than hundred test controllers obtained by varying four test variables. This study unveils several weaknesses of the PCM: (1) the communication scheme built into PCM incurs a large latency though average sensor-to-output latency is one of the most dominating factors in determining control quality; (2) scheduling jitter is taken seriously in PCM though its effect appears only when average sensor-to-output latency is sufficiently small; (3) loop processing periods are not properly optimized for control quality though they are another dominating factor of performance; and (4) transient overloads are not considered at all in PCM, even though they can seriously damage the performance of a system. Based on these results, we propose a new communication scheme and a transient overload handling technique for the improved period calibration method.     

Projection based MIMO control performance monitoring: II––measured disturbances and setpoint changes

In this paper the performance monitoring method based on subspace projections from Part I [J. Proc. Cont. 13 (2003) 739] is extended to include measured disturbances and setpoint changes. It was shown in [J. Proc. Cont. 13 (2003) 739] that the minimum variance output space is an optimal subspace of the general closed-loop output space and that orthogonal projections of filtered output data onto past closed-loop output data can be used to assess the performance of feedback controllers. This paper demonstrates that the same framework is directly applicable to systems with measured disturbances by augmenting the data matrix with those measured disturbances. Furthermore, it provides a means of separating suboptimal control performance between that arising from unmeasured disturbances and that due to measured disturbances. The effect of setpoint changes on control performance can be calculated as special feedforward variables. The controller is generally time-varying to include the case of model predictive control. A simulation example and an industrial boiler process are used to demonstrate the effectiveness of the proposed method.     

Delay-range-dependent robust stabilization for uncertain T-S fuzzy control systems with interval time-varying delays

This paper is concerned with robust stabilization for a class of T-S fuzzy control systems with interval time-varying delays. An approach is proposed to significantly improve the system performance while reducing the number of scalar decision variables in linear matrix inequalities. The main points of the approach are: (i) two coupling integral inequalities are proposed to deal with some integral items in the derivation of the stability criteria; (ii) an appropriate Lyapunov-Krasovskii functional is constructed by including both the lower and upper bounds of the interval time-varying delays; and (iii) neither model transformation nor free weighting matrices are employed in the theoretical result derivation. As a result, some improved sufficient stability criteria are derived, and the maximum allowable delay bound and controller gains can be obtained simultaneously by solving an optimization problem. Numerical examples are given to demonstrate the effectiveness of the proposed approach.