Identification for control of multivariable systems: Controller validation and experiment design via LMIs

This paper presents a new controller validation method for linear multivariable time-invariant models. Classical prediction error system identification methods deliver uncertainty regions which are nonstandard in the robust control literature. Our controller validation criterion computes an upper bound for the worst case performance, measured in terms of the -norm of a weighted closed loop transfer matrix, achieved by a given controller over all plants in such uncertainty sets. This upper bound on the worst case performance is computed via an LMI-based optimization problem and is deduced via the separation of graph framework. Our main technical contribution is to derive, within that framework, a very general parametrization for the set of multipliers corresponding to the nonstandard uncertainty regions resulting from PE identification of MIMO systems. The proposed approach also allows for iterative experiment design. The results of this paper are asymptotic in the data length and it is assumed that the model structure is flexible enough to capture the true system.     

Controller tuning with Bayesian optimization and its acceleration: Concept and experimental validation

This work discusses a data-driven approach to controller parameter tuning based on Bayesian optimization. In particular, we propose to design the prior mean function based on a model of the plant. By encoding the information on the model, the optimization needs a much fewer iterations than standard approaches. The effectiveness of the proposed method is demonstrated with a practical experiment.     

Identification and experimental validation of a scalable elevator vertical dynamic model

A reliable model of an elevator’s vertical motion is of tremendous value in many aspects of elevator design, installation and service. The challenges in developing and validating a dynamic model for an elevator arise from the large size of the dynamic system involved, its position-dependent or time-varying nature and from the limited number of variables available for measurement. In this paper, a physics-based dynamic model of an elevator’s vertical motion, scalable to varying rises, is first derived. Then, extensive experimental data is obtained from two elevator systems with rises over 100 and 250 m. The corresponding parameters of the two elevator systems are identified via modal analysis and a numerical mode-matching procedure so that the model-predicted transfer functions may best match the experimentally estimated ones. The scalability of the model is subsequently examined to extend the validity of the model to untested elevator systems. Finally, the experimentally validated model is successfully used in predicting the performance indices of high-rise elevators.     

Distributed model predictive control of an experimental four-tank system

A distributed model predictive control (DMPC) framework is proposed. The physical plant structure and the plant mathematical model are used to partition the system into self-sufficient estimation and control nodes. Local measurements at the nodes are used to estimate the relevant plant states. This information is then used in the model predictive control calculations. Communication among relevant nodes during estimation and control calculations provides improvement over the performance of completely decentralized controllers. The DMPC framework is demonstrated for the level control of an experimental four-tank system. The performance of the DMPC system for disturbance rejection is compared with other control configurations. The results indicate that the proposed framework provides significant improvement over completely decentralized MPC controllers, and approaches the performance of a fully centralized design.     

Optimizing model predictive control of an industrial distillation column

The main scope of this work is the implementation of an MPC that integrates the control and the economic optimization of the system. The two problems are solved simultaneously through the modification of the control cost function that includes an additional term related to the economic objective. The optimizing MPC is based on a quadratic program (QP) as the conventional MPC and can be solved with the available QP solvers. The method was implemented in an industrial distillation system, and the results show that the approach is efficient and can be used, in several practical cases.     

Information quality model and optimization for 802.15.4-based wireless sensor networks

High information quality is a paramount requirement for wireless sensor network (WSN) monitoring applications. However, it is challenging to achieve a cost effective information quality solution due to unpredictable environment noise and events, unreliable wireless channel and network bandwidth, and sensor resource and energy constraints. Specifically, the dynamic and unreliable nature of WSNs make it difficult to pre-determine optimum sensor rates and predict packet loss. To address this problem, we present an information quality metric which characterizes information quality based on the sampling frequency of sensor nodes and the packet loss rate during network transmission. Our fundamental quality metric is based on signal-to-noise ratio and is therefore application independent. Based on our metric, a quality-aware scheduling system (QSS) is developed, which exploits cross-layer control of sensor nodes to effectively schedule data sensing and forwarding. Particularly, we develop and evaluate several QSS scheduling mechanisms: passive, reactive and perceptive. These mechanisms can adapt to environment noise, bandwidth variation and wireless channel collisions by dynamically controlling sensor rates and phase. Our experimental results indicate that our QSS is a novel and effective approach to improve information quality for WSNs.     

Power system model validation for power quality assessment applications using genetic algorithm

This paper presents an intelligent system for power quality assessment application. This system is used for power system model validation. A genetic algorithm (GA) based system for validating the power system model in capacitor switching studies has been developed. The problem formulation and the proposed solution are illustrated. The feasibility of the developed system for practical applications is demonstrated by evaluation studies.     

Experimental evaluation of MPC-based anti-surge and process control for electric driven centrifugal gas compressors

The present work concerns model predictive control (MPC) of centrifugal gas compressors and describes the development of an MPC application for the tasks of anti-surge and process control. More specifically, the MPC formulation focuses on the question of how the transient manipulation of driver torque can be used to improve the performance of anti-surge and process control. For the purpose of testing and validating the proposed control algorithm, an experimental compressor test rig is presented, which is designed to mimic a typical centrifugal compressor application in the oil and gas industry. Modeling and parameter identification of the experimental setup is followed by the realization of the MPC solution on an embedded system to comply with the stringent real-time requirements for anti-surge control. Testing is performed with experiments using suction and discharge side disturbances, which are created by rapid valve closures. For comparison the same tests are repeated with conventional control approaches. The test results indicate improvements in maintaining the distance to surge by up to 11%, while at the same time reducing the process control settling time by up to 50%.     

Hybrid control of a pneumatic artificial muscle (PAM) robot arm using an inverse NARX fuzzy model

We investigated the possibility of applying a hybrid feed-forward inverse nonlinear autoregressive with exogenous input (NARX) fuzzy model-PID controller to a nonlinear pneumatic artificial muscle (PAM) robot arm to improve its joint angle position output performance. The proposed hybrid inverse NARX fuzzy-PID controller is implemented to control a PAM robot arm that is subjected to nonlinear systematic features and load variations in real time. First the inverse NARX fuzzy model is modeled and identified by a modified genetic algorithm (MGA) based on input/output training data gathered experimentally from the PAM system. Second the performance of the optimized inverse NARX fuzzy model is experimentally demonstrated in a novel hybrid inverse NARX fuzzy-PID position controller of the PAM robot arm. The results of these experiments demonstrate the feasibility and benefits of the proposed control approach compared to traditional PID control strategies. Consequently, the good performance of the MGA-based inverse NARX fuzzy model in the proposed hybrid inverse NARX fuzzy-PID position control of the PAM robot arm is demonstrated. These results are also applied to model and to control other highly nonlinear systems.     

Biomarkers of neurological status in HIV infection: a 3-year study

Purpose : To evaluate circulating cytokines and chemokines as correlates of the degree of brain injury in individuals with advanced human immunodeficiency virus (HIV) infection. Experimental design : Study participants included ten well‐characterized subjects in advanced stage HIV infection. High‐throughput multiplexed analysis was used to quantify markers of interest at baseline and 3 years later in the clinical course. Objective measurements of the brain were derived in vivo with quantitative magnetic resonance segmentation algorithms and with diffusion tensor imaging. Results : Of the markers examined, monocyte chemoattractant protein‐1 (MCP‐1 or CCL‐2) was the most prominent correlate of brain injury. Elevated MCP‐1 levels correlated with brain white matter alterations at the initial assessment. The relationship to injury was more extensive 3 years later; elevated MCP‐1 was significantly correlated with measures of brain microstructural alterations and of abject atrophy. Conclusions and clinical relevance : The findings build on our prior observations that elevated MCP‐1 levels may be a useful predictive marker for HIV‐associated neurocognitive disorder. As a potent chemoattractant, MCP‐1 may mediate injury through participation in self‐reinforcing cycles of chronic immune activation and cytokine/chemokine‐mediated neurotoxicity.     

Hopfield Neural Networks for Parametric Identification of Dynamical Systems

In this work, a novel method, based upon Hopfield neural networks, is proposed for parameter estimation, in the context of system identification. The equation of the neural estimator stems from the applicability of Hopfield networks to optimization problems, but the weights and the biases of the resulting network are time-varying, since the target function also varies with time. Hence the stability of the method cannot be taken for granted. In order to compare the novel technique and the classical gradient method, simulations have been carried out for a linearly parameterized system, and results show that the Hopfield network is more efficient than the gradient estimator, obtaining lower error and less oscillations. Thus the neural method is validated as an on-line estimator of the time-varying parameters appearing in the model of a nonlinear physical system.     

Identification and robust control for systems with ellipsoidal parametric uncertainty by convex optimization

In this paper, sufficient conditions for robust output feedback controller design for systems with ellipsoidal parametric uncertainty are given in terms of solutions to a set of linear matrix inequalities (LMIs) Performance specifications are in terms of combined pole placement with sensitivity function shaping in the H₂ or H_∞ norm. Furthermore, an optimal input design technique for parameter estimation that is integrated into the robust control design is employed in this paper. This means that performance specifications on the closed‐loop transfer functions are translated into the requirements on the input signal spectrum. The simulation results show the effectiveness of the proposed method. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

基于自适应云粒子群算法的Wiener模型辨识

针对非线性系统Wiener模型的系统辨识问题,提出一种基于自适应云模型的粒子群优化(ACMPSO)算法的辨识方法。ACMPSO算法利用云模型实现优秀粒子的遗传和进化操作,根据进化状况动态调整云模型的参数,自适应地控制云模型算法的寻优范围和精度,有较强的全局搜索和局部求精能力。仿真实验证明该算法寻优精度高于其他主要PSO算法;将该算法应用于Wiener模型的系统辨识,通过实验证明了该辨识方法优于当前其他方法。     

On-line closed-loop model validation and detection of abrupt parameter changes

This paper is concerned with on-line closed-loop model validation and detection of abrupt changes of model parameters. A detection algorithm based on the two-model divergence algorithm is developed for the validation of dynamic models under closed-loop conditions. The performance of the detection algorithm is evaluated through frequency domain analysis. Theoretical results are verified via simulation examples.     

Structure-preserving model reduction for spatially interconnected systems with experimental validation on an actuated beam

A technique for model reduction of exponentially stable spatially interconnected systems is presented, where the order of the reduced model is determined by the number of truncated small generalised singular values of the structured solutions to a pair of Lyapunov inequalities. For parameter-invariant spatially interconnected systems, the technique is based on solving a pair of Lyapunov inequalities in continuous-time and -space domain with a rank constraint. Using log-det and cone complementarity methods, an improved error bound can be obtained. The approach is extended to spatially parameter-varying systems, and a balanced truncation approach using parameter-dependent Gramians is proposed to reduce the conservatism caused by the use of constant Gramians. This is done by considering two important operators, which can be used to represent multidimensional systems (temporal- and spatial-linear parameter varying interconnected systems). The results are illustrated with their application to an experimentally identified spatially interconnected model of an actuated beam; the experimentally obtained response to an excitation signal is compared with the response predicted by a reduced model.     

Design and experimental validation of a second-order sliding-mode motion controller for robot manipulators

This article presents an original motion control strategy for robot manipulators based on the coupling of the inverse dynamics method with the so-called second-order sliding mode control approach. Using this method, in principle, all the coupling non-linearities in the dynamical model of the manipulator are compensated, transforming the multi-input non-linear system into a linear and decoupled one. Actually, since the inverse dynamics relies on an identified model, some residual uncertain terms remain and perturb the linear and decoupled system. This motivates the use of a robust control design approach to complete the control scheme. In this article the sliding mode control methodology is adopted. Sliding mode control has many appreciable features, such as design simplicity and robustness versus a wide class of uncertainties and disturbances. Yet conventional sliding mode control seems inappropriate to be applied in robotics since it can generate the so-called chattering effect, which can be destructive for the controlled robot. In this article, this problem is suitably circumvented by designing a second-order sliding mode controller capable of generating a continuous control law making the proposed sliding mode controller actually applicable to industrial robots. To build the inverse dynamics part of the proposed controller, a suitable dynamical model of the system has been formulated, and its parameters have been accurately identified relying on a practical MIMO identification procedure recently devised. The proposed inverse dynamics-based second-order sliding mode controller has been experimentally tested on a COMAU SMART3-S2 industrial manipulator, demonstrating the tracking properties and the good performances of the controlled system.     

Identification for control: Optimal input intended to identify a minimum variance controller

It is well known that the quality of the parameters identified during an identification experiment depends on the applied excitation signal. Prediction error identification using full order parametric models delivers an ellipsoidal region in which the true parameters lie with some prescribed probability level. This ellipsoidal region is determined by the covariance matrix of the parameters. Input design strategies aim at the minimization of some measure of this covariance matrix. We show that it is possible to optimize the input in an identification experiment with respect to a performance cost function of a closed-loop system involving explicitly the dependence of the designed controller on the identified model. In the present contribution we focus on finding the optimal input for the estimation of the parameters of a minimum variance controller, without the intermediate step of first minimizing some measure of the model parameter accuracy. We do this in conjunction with using covariance formulas which are not asymptotic in the model order, which is rather new in the domain of optimal input design. The identification procedure is performed in closed-loop. Besides optimizing the input power spectrum for the identification experiment, we also address the question of optimality of the controller. It is a wide belief that the minimum variance controller should be the optimal choice, since we perform an experiment for designing a minimum variance controller. However, we show that this may not always be the case, but rather depends on the model structure.     

Development of omni-directional correlation functions for nonlinear model validation

In the present study a set of first order correlation functions are proposed to examine the quality of a wide class of identified nonlinear models. The first order correlation functions, defined as omni-directional correlation functions, are integrated into two concise tests to provide more effective auto and cross model error correlation diagnosis than the other approaches proposed from higher order correlation functions. The mechanisms of the novel validity tests are proved in theory and demonstrated with numerical analyses. Two simulated case studies, in the situation of incorrectly detected model structure and estimated parameters, are presented to illustrate the diagnostic power of the new methodology.     

An experimental study of pilots' control characteristics for flight of an STOL aircraft in backside of drag curve at approach and landing

Abstract

In general, most vehicles can be modelled by a multi-variable system which has interactive variables. It can be clearly shown that there is an interactive response in an aircraft's velocity and altitude obtained by stick control and/or throttle control. In particular, if the flight conditions fall to backside of drag curve in the flight of an STOL aircraft at approach and landing then the ratio of drag variation to velocity change has a negative value (ΔD/Δu<0) and the system of motion presents a non-minimum phase. Therefore, the interaction between velocity and altitude response becomes so complicated that it affects to pilot's control actions and it may be difficult to control the STOL aircraft at approach and landing.

In this paper, experimental results of a pilot's ability to control the STOL aircraft are presented for a multi-variable manual control system using a fixed ground base simulator and the pilot's control ability is discussed for the flight of an STOL aircraft at backside of drag curve at approach and landing.