Projection based MIMO control performance monitoring: I—covariance monitoring in state space

In this paper we propose a new control performance monitoring method based on subspace projections. We begin with a state space model of a generally non-square process and derive the minimum variance control (MVC) law and minimum achievable variance in a state feedback form. We derive a multivariate time delay (MTD) matrix for use with our extended state space formulation, which implicitly is equivalent to the interactor matrix. We show how the minimum variance output space can be considered an optimal subspace of the general closed-loop output space and propose a simple control performance calculation which uses orthogonal projection of filtered output data onto past closed-loop data. Finally, we propose a control performance monitoring technique based on the output covariance and diagnose the cause of suboptimal control performance using generalized eigenvector analysis. The proposed methods are demonstrated on a few simulated examples and an industrial wood waste burning power boiler.     

Harmonic suppressed bandpass filter using composite right/left handed transmission line

A wideband composite right/left handed transmission line (CRLH TL) in conjunction with its corresponding equivalent circuit model is studied based on a cascaded complementary single split ring resonator (CCSSRR).The characterization is performed by theory analysis,circuit simulation,and full-wave electromagnetic (EM) simulation.The negative refractive index (NRI) and backward wave propagation performance of the CRLH TL are demonstrated.For application,a bandpass filter (BPF) with enhanced out-of-band selectivity and harmonic suppression operating at the wireless local area network (WLAN) band is designed,fabricated,and measured by combining the CRLH TL with a complementary electric inductive-capacitive resonator (CELC).Three CELC cells with wideband stopband performance in the conductor strip and ground plane,respectively,are utilized in terms of single negative permeability.The design concept has been verified by the measurement data.     

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.     

Decentralized control using quasi-block diagonal dominance of transfer function matrices

The purpose of this paper is to present a generalization of the Nyquist array method to blockwise decompositions, which is based upon a new version of block diagonal dominance. An additional flexibility of the proposed method is in partitioning of the system matrices into disjoint as well as overlapping submatrices, which increases considerably the class of control systems which can be designed via block diagonal dominance. Within this framework, the controllers for individual subsystems can be designed independently of each other, so that their union represents a decentralized controller for the overall system.     

Statistical MIMO controller performance monitoring. Part I: Data-driven covariance benchmark

In this paper, a data-based covariance benchmark is proposed for control performance monitoring. Within the covariance monitoring scheme, generalized eigenvalue analysis is used to extract the directions with the degraded or improved control performance against the benchmark. It is shown that the generalized eigenvalues and the covariance-based performance index are invariant to scaling of the data. A statistical inference method is further developed for the generalized eigenvalues and the corresponding confidence intervals are derived from asymptotic statistics. This procedure can be used to determine the directions or subspaces with significantly worse or better performance versus the benchmark. The covariance-based performance indices within the isolated worse and better performance subspaces are then derived to assess the performance degradation and improvement. Two simulated examples, a multiloop control and a multivariable MPC system, are provided to illustrate the utility of the proposed approach. Then an industrial wood waste burning power boiler unit is used to demonstrate the effectiveness of the method.     

Distributed machining control and monitoring using smart sensors/actuators

The study of smart sensors and actuators led, during the past few years, to the development of facilities which improve traditional sensors and actuators in a necessary way to automate production systems. In another context, many studies have been carried out aimed at defining a decisional structure for production activity control and the increasing need of reactivity leads to the autonomization of decisional levels close to the operational system. We study in this paper the natural convergence between these two approaches and we propose an integration architecture, dealing with machine tool and machining control, that enables the exploitation of distributed smart sensors and actuators in the decisional system.     

Robust stability of MIMO nonlinear uncertain systems using operator-based right coprime factorisation

In this paper, a class of multi-input–multi-output (MIMO) nonlinear systems with uncertainties is considered by using operator-based right coprime factorisation. First, based on proposed quotient operators, coupling effect of the MIMO nonlinear systems is discussed. Second, based on the operator-based right coprime factorisation approach, sufficient conditions for guaranteeing robust stability of the MIMO nonlinear systems with uncertainties are proposed by using a new unimodular operator. Finally, a simulation example is shown to illustrate the proposed design scheme for the MIMO nonlinear systems with uncertainties.     

Closed-loop performance monitoring using loop tuning

In this article it is shown how the tuning of a loop is used to commission a closed-loop performance monitoring method. The extended prediction horizon index is used to monitor disturbance rejection properties expressed with the impulse response. The impulse response is obtained from the tuning of the loop. All parameters of the monitoring method are set by using the impulse response. The presented method works equally well when disturbances are stochastic or deterministic. It is shown how typical performance problems are detected by the method. Simple rules of thumb to commission the method for lambda-tuned loops are given. Finally an industrial evaluation is presented for 18 lambda-tuned loops from a pulp and paper mill.     

无线多跳网络运用MIMO技术性能研究

运用图论和矩阵表示网络拓扑,探讨了无线多跳网络分别采用MIMO（多输入多输出）技术和SISO（单输入单输出）技术时网络在鲁棒性、平均路径长度、簇的数目及最大簇中节点数等方面的性能。仿真结果显示发送功率相同的条件下采用MIMO技术时网络在以上方面的性能优于采用SISO技术时的性能。     

Synchronisation of high-order MIMO nonlinear systems using distributed neuro-adaptive control

This paper addresses synchronisation problem of high-order multi-input/multi-output (MIMO) multi-agent systems. Each agent has unknown nonlinear dynamics and is subject to uncertain external disturbances. The agents must follow a reference trajectory. An adaptive distributed controller based on relative information of neighbours of each agent is designed to solve the problem for any undirected connected communication topology. A radial basis function neural network is used to represent the controller's unknown structure. Lyapunov stability analysis is employed to guarantee stability of the overall system. By the theoretical analysis, the closed-loop control system is shown to be uniformly ultimately bounded. Finally, simulations are provided to show effectiveness of the proposed control method against uncertainty and disturbances.     

A MIMO R2R control using data-driven gain scheduling

This work presents a partial least square (PLS) based double exponentially weighted moving average controller (d-EWMA) of multiple-input and multiple-output (MIMO) processes subjected to constant or moderate gain shifts disturbance. This study is mainly focused on a new gain monitoring method and a PLS based model updating. It was demonstrated that under the circumstance the variance of the first order difference of the latent control action would be more effective in detecting the gain shifts. The performance improvements are illustrated using a simulation problem from literature and applications to thickness control in a diffusion furnace of a local semiconductor facility.     

Output tracking control of MIMO fuzzy nonlinear systems using variable structure control approach

The output tracking control problem for nonlinear systems in the presence of both parameter perturbations and external disturbances is studied. Our approach is based on the Takagi-Sugeno (T-S) fuzzy modeling method and a variable structure control (VSC) technique. Therefore, the systems considered are not and need not be in the triangular and parametric strict-feedback form, which are prevalent among adaptive model following control for nonlinear systems, or in the normal form, which pervades almost all existing results in neuro-fuzzy model following control approach. We first study the problem of stabilization of T-S fuzzy systems by using a VSC technique. A method for the design of a switching surface based on linear matrix inequalities is developed and a stabilizing controller based on a reaching law concept in the presence of both parameter perturbations and external disturbances is proposed. Then, the method is extended to design controllers for output tracking of T-S fuzzy nonlinear systems in two cases, i.e. systems which possess the so-called strong passive subsystems and strong stable zero dynamics, respectively. Finally, illustrative examples are presented to demonstrate the whole design procedure from the original nonlinear systems to their fuzzification and finally to the realization of the desired controllers. Simulation results show that the goal of output tracking can be achieved by the proposed controllers.     

A cubic dual band near-field focused array antenna using composite right/left handed transmission lines

This paper proposes a new method for design of a dual-frequency near field-focused (NFF) antenna. A 4 × 4 array of dual-band patches and a dual-band feed network are designed to provide a phase distribution at 2.4 and 5.8 GHz. Rectangular patches with U-shaped slot be used as the double-band patches. The composite right/left handed transmission lines (CRLH TL) are used in design of a dual-band phase distribution network for the patches. Finally, a three-dimensional cubic structure is proposed to isolate the feed network and thereby improve the antenna performance. A prototype of a cubic dual band 4 × 4 near field focused array antenna is fabricated and tested. The measurement results have good agreement with simulation results.     

Robust MIMO water level control in interconnected twin-tanks using second order sliding mode control

A novel second order sliding mode control algorithm is presented for a class of MIMO nonlinear systems in input–output (I–O) form. The algorithm produces a dynamic control and does not require the derivative of the sliding variable, thus eliminating the requirement to design observers or peak detectors. The algorithm has been applied for robust control of liquid level in interconnected twin-tanks. The controller is implemented on a laboratory rig and the results presented validate the proposed theory. The implementation results show robustness to parameter variations such as tank area, the admittance coefficients of various pipes, leakage in the tanks and uncertainty in the pump dynamics. In order to contextualise the results obtained with second-order sliding mode control, the results obtained using a classical PI controller are also presented.     

Adaptive dynamic surface control for MIMO nonlinear time-varying systems with prescribed tracking performance

In this paper, an adaptive dynamic surface control scheme is proposed for a class of multi-input multi-output (MIMO) nonlinear time-varying systems. By fusing a bound estimation approach, a smooth function and a time-varying matrix factorisation, the obstacle caused by unknown time-varying parameters is circumvented. The proposed scheme is free of the problem of explosion of complexity and needs only one updated parameter at each design step. Moreover, all tracking errors can converge to predefined arbitrarily small residual sets with a prescribed convergence rate and maximum overshoot. Such features result in a simple adaptive controller which can be easily implemented in applications with less computational burden and satisfactory tracking performance. Simulation results are presented to illustrate the effectiveness of the proposed scheme.     

Model predictive control monitoring using multivariate statistics

Control loop monitoring has become an important research field over the past decade. Research has primarily targeted single-input single-output (SISO) feedback control systems with limited progress being made on the monitoring of multi-input multi-output (MIMO) control systems and large scale model predictive control (MPC) systems in particular. The size and complexity of MPC systems means that identifying and diagnosing problems with their operation can be challenging. This paper presents an MPC condition monitoring tool based on multivariate statistical process control (MSPC) techniques. The proposed tool uses intuitive charts to enable casual users of MPC technology to detect abnormal controller operation and to identify possible causes for this behaviour. Through its application to data collected from a large scale MPC system, the proposed technique is shown to be able to identify and diagnose poor control performance resulting from various issues including inappropriate interaction by process operators.     

Adaptive hybrid control for linear piezoelectric ceramic motor drive using diagonal recurrent CMAC network

This work presents an adaptive hybrid control system using a diagonal recurrent cerebellar-model-articulation-computer (DRCMAC) network to control a linear piezoelectric ceramic motor (LPCM) driven by a two-inductance two-capacitance (LLCC) resonant inverter. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time varying, an adaptive hybrid control system is therefore designed based on a hypothetical dynamic model to achieve high-precision position control. The architecture of DRCMAC network is a modified model of a cerebellar-model-articulation-computer (CMAC) network to attain a small number of receptive-fields. The novel idea of this study is that it employs the concept of diagonal recurrent neural network (DRNN) in order to capture the system dynamics and convert the static CMAC into a dynamic one. This adaptive hybrid control system is composed of two parts. One is a DRCMAC network controller that is used to mimic a conventional computed torque control law due to unknown system dynamics, and the other is a compensated controller with bound estimation algorithm that is utilized to recover the residual approximation error for guaranteeing the stable characteristic. The effectiveness of the proposed driving circuit and control system is verified with hardware experiments under the occurrence of uncertainties. In addition, the advantages of the proposed control scheme are indicated in comparison with a traditional integral-proportional (IP) position control system.     

基于支持向量机的脑电信号中左右手判别

在脑-机接口的研究中分类识别技术占有重要地位。介绍了一种方法,用于对单次信号的分类。这种方法主要思想是将共空域子空间分解和支持向量机相结合,以便提取信号特征。该方法被用于“BCI Competition 2003”第IV数据包上,分类正确率达89%。     

Fine control of filter performance based on composite right/left‐handed metamaterial technology

Composite right/left‐handed (CRLH) metamaterial unit cell whose ground‐plane is defected with a rectangular dielectric slot is used to develop a single and multi‐pole pole bandpass filter. The unit cell comprises of serially connected interdigital capacitors whose junction is connected to a short‐circuited inductive stub. By defecting the ground‐plane of the unit cell's structure with a dielectric slot, which is located immediately below the unit cell, enables substantial tuning of the filter's center frequency in the order of 26.5%. This is achieved with minimal effect on the unit cell's insertion and return‐loss performance including its selectivity. The filters were fabricated on conventional dielectric substrate and their performance measured to verify the design methodology. The proposed technique eases the trade‐off constraints and makes possible the realization of challenging filter specifications constituted from CRLH unit cells using distributed transmission‐lines. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:39–45, 2014.