Input-output Measurements Research Articles

Discrete variable structure model reference adaptive control using only input-output measurements

Designing an adequate controller for a plant with an arbitrary relative degree is still an active area of research. In this paper, a discrete variable structure model reference adaptive control using only input-output measurements (DVS-MRAC-IO) for not strictly positive real systems with a relative degree of two is proposed. In order to show the effectiveness of the proposed controller, a detailed stability analysis is studied using Lyapunov theory. Further, a straightforward generalization of DVS-MRAC-IO for systems with arbitrary relative degree is presented. Numerical results are used to show the effectiveness of the proposed methods.

No theory: an explanation of the lack of consistency in cross-country health care comparisons using non-parametric estimators.

Since 2000 several papers have examined the efficiency of healthcare delivery systems worldwide. These papers have extended the literature using drastically different input and output combinations from one another, with little theoretical or empirical support backing these specifications. Issues arise that many of these inputs and outputs are available for a subset of OECD countries each year. Using a common estimator and the different specifications proposed leads to the result that efficiency rankings across papers can diverge quite significantly, with several countries being highly efficient in one specification and highly inefficient in another. Broad input-output measures that are collected annually provide consistent efficiency rankings across specifications, compared to specifications that utilize specific measures collected infrequently. This paper also finds that broad output measures that are not quality-adjusted, such as life expectancy, seem to be a suitable alternative for infrequently collected quality-adjusted output measures, such as disability adjusted life years.

Development of ANFIS model and machinability study on dry turning of cryo-treated PH stainless steel with various inserts

ABSTRACTThis present investigation deals about the machinability comparison of cryogenically treated 15-5 PH stainless steel with various cutting tools such as uncoated tungsten carbide, cryogenic-treated tungsten carbide and wiper geometry inserts. Cryo-treated PH stainless steel is considered as the work material in this investigation and experimental trials were performed under dry turning condition. The machinability aspects considered for evaluation are cutting force (Fz), surface roughness (Ra) and tool wear. The outcomes of experimentation reveal that the tungsten carbide inserts which are cryogenically treated provide improved performance in machining while comparing with conventional and wiper geometry inserts at all machining conditions. The measured cutting force and the observed flank wear were less for the cryo-treated inserts. However, wiper tool produces a better surface finish during machining. An artificial intelligence decision-making tool named Adaptive Neuro Fuzzy Inference System has been evolved to determine the relation among the considered input machining variables and output measures, namely cutting force and surface roughness of the machined surface. An analysis has been performed to compare the results obtained from developed models and experimental results.

Nonlinear identification of a spark ignition engine torque based on ANFIS with NARX method

Spark ignition (SI) engines have a nonlinear dynamic system with inherent uncertainties and unpredictable disturbances. The identification of a nonlinear system is vital in many fields of engineering. In this study, SI engine torque is identified from an input–output measurement. This study aims to propose a dynamic nonlinear model that uses an adaptive neuro-fuzzy inference system and a nonlinear auto-regressive with exogenous input structure to identify the dynamic nonlinear behavior of an SI engine. Considerable good performance is achieved using the adaptive neuro-fuzzy inference system nonlinear auto-regressive with exogenous input method. For model validation, the proposed method is compared with the more conventional identification approach called the Hammerstein method. The results show that the two methods are in excellent agreement. The Hammerstein model was chosen because its identification result of the SI system was studied previously by the author. Validation results prove that the ability of the proposed model can capture the highly nonlinear behavior of the SI system.

Local learning‐based model‐free adaptive predictive control for adjustment of oxygen concentration in syngas manufacturing industry

During the united gas improvement (UGI) gasification process in the syngas industry, the oxygen-enriched technique plays an important role, since the obtained oxygen-enriched air with a high oxygen concentration can enhance the production efficiency of the syngas. However, satisfactory control performance for the oxygen concentration of the oxygen-enriched air is hard to achieve because an accurate dynamical model of the oxygen concentration control process by the first principles is fairly difficult to obtain due to strong non-linearity and unknown disturbances in practice. A novel data-driven control method called compact-form-dynamic-linearisation-based model-free adaptive predictive control approach combined with the local learning (LL-CFDL-MFAPC) is proposed to address the control problem. In LL-CFDL-MFAPC, the online and offline input-output measurement data of the plant are fully and simultaneously utilised during the control process, and the design of the controller is model free by means of compact-form-dynamic-linearisation technique. Moreover, the controller has strong robustness because the prediction mechanism participates in control design and only the input/output measurement data are used. The stability and convergence of LL-CFDL-MFAPC are guaranteed by theoretical analysis under several reasonable assumptions, and simulation experiments using real data collected from a practical UGI gasifier verify that the oxygen concentration control problem can be effectively addressed by the proposed method.

OKID via Output Residuals: A Converter from Stochastic to Deterministic System Identification

This paper presents a new approach for the identification of linear state-space models from input–output measurements in the presence of noise. In contrast to the original OKID/ERA algorithm, which works through the observer Markov parameters, the new approach uses the estimated observer output residuals to convert a stochastic identification problem into a virtually deterministic one. A system state-space model and an associated Kalman-filter gain can be then identified. Because the converted problem is virtually deterministic, any deterministic system-identification method can be used. The proposed stochastic-to-deterministic conversion enables these deterministic algorithms to produce the same performance level as the existing stochastic system-identification algorithms.

Application of Kalman Filtering Methods to Online Real-Time Structural Identification: A Comparison Study

System identification refers to the process of building or improving mathematical models of dynamical systems from the observed experimental input–output data. In the area of civil engineering, the estimation of the integrity of a structure under dynamic loadings and during service condition has become a challenge for the engineering community. Therefore, there has been a great deal of attention paid to online and real-time structural identification, especially when input–output measurement data are contaminated by high-level noise. Among real-time identification methods, one of the most successful and widely used algorithms for estimation of system states and parameters is the Kalman filter and its various nonlinear extensions such as extended Kalman filter (EKF), Iterated EKF (IEKF), the recently developed unscented Kalman filter (UKF) and Iterated UKF (IUKF). In this paper, an investigation has been carried out on the aforementioned techniques for their effectiveness and efficiencies through a highly nonlinear single degree of freedom (SDOF) structure as well as a two-storey linear structure. Although IEKF is an improved version of EKF, results show that IUKF generally produces better results in terms of structural parameters and state estimation than UKF and IEKF. Also IUKF is more robust to noise levels compared to the other approaches.

A new adaptive control scheme based on the interacting multiple model (IMM) estimation

In this paper, an Interacting multiple model (IMM) adaptive estimation approach is incorporated to design an optimal adaptive control law for stabilizing an Unmanned vehicle. Due to variations of the forward velocity of the Unmanned vehicle, its aerodynamic derivatives are constantly changing. In order to stabilize the unmanned vehicle and achieve the control objectives for in-flight conditions, one seeks for an adaptive control strategy that can adjust itself to varying flight conditions. In this context, a bank of linear models is used to describe the vehicle dynamics in different operating modes. Each operating mode represents a particular dynamic with a different forward velocity. These models are then used within an IMM filter containing a bank of Kalman filters (KF) in a parallel operating mechanism. To regulate and stabilize the vehicle, a Linear quadratic regulator (LQR) law is designed and implemented for each mode. The IMM structure determines the particular mode based on the stored models and in-flight input-output measurements. The LQR controller also provides a set of controllers; each corresponds to a particular flight mode and minimizes the tracking error. Finally, the ultimate control law is obtained as a weighted summation of all individual controllers whereas weights are obtained using mode probabilities of each operating mode.

Lazy-Learning-Based Data-Driven Model-Free Adaptive Predictive Control for a Class of Discrete-Time Nonlinear Systems.

In this paper, a novel data-driven model-free adaptive predictive control method based on lazy learning technique is proposed for a class of discrete-time single-input and single-output nonlinear systems. The feature of the proposed approach is that the controller is designed only using the input-output (I/O) measurement data of the system by means of a novel dynamic linearization technique with a new concept termed pseudogradient (PG). Moreover, the predictive function is implemented in the controller using a lazy-learning (LL)-based PG predictive algorithm, such that the controller not only shows good robustness but also can realize the effect of model-free adaptive prediction for the sudden change of the desired signal. Further, since the LL technique has the characteristic of database queries, both the online and offline I/O measurement data are fully and simultaneously utilized to real-time adjust the controller parameters during the control process. Moreover, the stability of the proposed method is guaranteed by rigorous mathematical analysis. Meanwhile, the numerical simulations and the laboratory experiments implemented on a practical three-tank water level control system both verify the effectiveness of the proposed approach.

Data-driven output-feedback fault-tolerant L2 control of unknown dynamic systems

This paper studies the data-driven output-feedback fault-tolerant L2-control problem for unknown dynamic systems. In a framework of active fault-tolerant control (FTC), three issues are addressed, including fault detection, controller reconfiguration for optimal guaranteed cost control, and tracking control. According to the data-driven form of observer-based residual generators, the system state is expressed in the form of the measured input–output data. On this basis, a model-free approach to L2 control of unknown linear time-invariant (LTI) discrete-time plants is given. To achieve tracking control, a design method for a pre-filter is also presented. With the aid of the aforementioned results and the input–output data-based time-varying value function approximation structure, a data-driven FTC scheme ensuring L2-gain properties is developed. To illustrate the effectiveness of the proposed methodology, two simulation examples are employed.

Data-driven output-feedback fault-tolerant control for unknown dynamic systems with faults changing system dynamics

This paper studies the data-driven output-feedback fault-tolerant control (FTC) problem for unknown dynamic systems with faults changing system dynamics. In a framework of active FTC, two basic issues are addressed: the fault detection employing only the measured input–output information; the controller reconfiguration to achieve optimal output-feedback control in the presence of multiple faults. To detect faults and write the system state via the input–output data, an approach to data-driven design of a residual generator with a full-rank transformation matrix is presented. An output-feedback approximate dynamic programming method is developed to solve the optimal control problem under the condition that the unknown linear time-invariant discrete-time plant has multiple outputs. According to the above results and the proposed input–output data-based value function approximation structure of time-varying plants, a model-free output-feedback FTC scheme considering optimal performance is given. Finally, two numerical examples and a practical example of a DC motor control system are used to demonstrate the effectiveness of the proposed methods.

Automated generation of feedforward control using feedback linearization of local model networks

An effective but yet simple approach is introduced to automatically attain a dynamic feedforward control law for non-linear dynamic systems represented by discrete-time local model networks (LMN). In this context, feedback linearization is applied to the generic model structure of LMN and the resulting input transformation is used as model inverse. This general and automated approach for model inversion is applicable even when the overall model complexity may be high. Thus, by representing a non-linear dynamic system by an LMN and applying the proposed feedforward control law generation, a dynamic feedforward control for such a non-linear system can be found automatically with the knowledge of measured input–output data only. However, when feedback linearization is considered, the stability of the internal dynamics plays a key role. This paper analyses the occurring internal dynamics for LMN, which directly result from the chosen model structure in identification, and discusses the effects on the transformed system. Finally, the effectiveness of the proposed data-driven feedforward control is demonstrated by a simulation example as well as by an actual application to the pre-distortion of a microelectromechanical systems (MEMS) loudspeaker with electrostatic actuation.

Multi-objective optimization of an on-road bicycle frame by uniform design and compromise programming

With the rapid changes in manufacturing technology of bicycle, the safety and performance of a bicycle are important and remarkable research subjects. In this study, an innovative and integrated optimization procedure for multi-objective optimization of an on-road bicycle frame is presented. The multiple objectives are to reduce the bicycle frame’s permanent deformations and to decrease the bicycle frame’s mass. First, uniform design of experiments is applied to create a set of sampling points in the design space of control factors. Second, three-dimensional solid models of bicycle frames are constructed and permanent deformations of bicycle frames under dropping-mass and dropping-frame impact test simulations are measured by ANSYS and ANSYS/LS-DYNA. Third, Kriging interpolation is used to transform the discrete relations between input control factors and output measures to continuous surrogate models. Fourth, compromise programming and mixture uniform design of experiments are used to integrate the multiple-objective functions into one compromise objective function. Finally, generalized reduced gradient algorithm is employed to solve the optimization problem. After executing the innovative optimization procedure, an optimized on-road bicycle frame is obtained. Comparing with the original design, the frame’s permanent deformations and mass are reduced. Therefore, both consolidation and lightweight of on-road bicycle frame are achieved.

Adaptive modelling of non-linear errors-in-variables systems

This paper discusses the adaptive modelling algorithm of the non-linear Errors-in-Variables EIV system. The non-linear EIV model is designed as a Wiener model with noisy input and noisy output measurements. The model consists of a moving average model linear subsystem and a piece-wise linear function as the non-linear subsystem. In order to ensure the online real-time updating of system parameters, the recursive form is designed for the algorithm to keep its adaptive properties and improve the parameters' identification efficiency. The derivation process of the algorithm ensures the recursive calculation results converge to their corresponding true values. The validity and convergence of the algorithm is confirmed by a simulation experiment with the help of Matlab.

Photovoltaic Power Forecasting Model Based on Nonlinear System Identification

Solar photovoltaic (PV) energy sources are rapidly gaining potential growth and popularity compared with conventional fossil fuel sources. As the merging of the PV systems with existing power sources increases, reliable and accurate PV system identification is essential to address the highly nonlinear change in the PV system dynamic and operational characteristics. This paper deals with the identification of a PV system characteristic in the real-life environment in Kuwait. The studied PV system is located on the top of the Ministry of Electricity and Water and the Ministry of Public Works buildings. The identification methodology is discussed. A Hammerstein–Wiener model is identified and selected due to its suitability to capture the PV system dynamics. Measured input–output data are collected from the PV system to be used for the identification process. The data are divided into estimation and validation sets. Results and discussions are provided to demonstrate the accuracy of the selected model structure.

Frequency Response Matrix Estimation From Partially Missing Data—for Periodic Inputs

Multivariate nonparametric frequency response estimation is important in engineering. It allows one to get a quick insight into the dynamics of the system from input–output measurements. Sometimes, measurements can be missing due to faulty sensors or communication links. In this paper, we develop a method that estimates the frequency response matrix together with the missing samples from partially known data. In addition, the method can estimate the level of nonlinear (NL) contribution and the additive noise level of weakly NL systems when excited by a periodic excitation. Several special cases can be handled if the reference input is known, like samples missing at the inputs, noisy inputs, and identification in feedback.

Parameter identification of BIPT system using chaotic-enhanced fruit fly optimization algorithm

Bidirectional inductive power transfer (BIPT) system facilitates contactless power transfer between two sides and across an air-gap, through weak magnetic coupling. Typically, this system is nonlinear high order system which includes nonlinear switch components and resonant networks, developing of accurate model is a challenging task. In this paper, a novel technique for parameter identification of a BIPT system is presented by using chaotic-enhanced fruit fly optimization algorithm (CFOA). The fruit fly optimization algorithm (FOA) is a new meta-heuristic technique based on the swarm behavior of the fruit fly. This paper proposes a novel CFOA, which employs chaotic sequence to enhance the global optimization capacity of original FOA. The parameter identification of the BIPT system is formalized as a multi-dimensional optimization problem, and an objective function is established minimizing the errors between the estimated and measured values. All the 11 parameters of this system (Lpi, LT, Lsi, Lso, CT, Cs, M, Rpi, RT, Rsi and Rso) can be identified simultaneously using measured input–output data. Simulations show that the proposed parameter identification technique is robust to measurements noise and variation of operation condition and thus it is suitable for practical application.

Uncertainty Management in Computational Simulations of Medical Devices1

Uncertainty Management in Computational Simulations of Medical Devices1

Optimal bilinear observers for bilinear state-space models by interaction matrices

This paper formulates optimal bilinear observers for bilinear state-space models. Observers in bilinear form, as opposed to other nonlinear forms, are required to develop an extension of observer/Kalman filter identification for simultaneous identification of a bilinear state-space model and an associated bilinear observer from noisy input–output measurements. The paper establishes the relationship between the bilinear observer gains and the interaction matrices which are used to convert the original bilinear state-space model to a form that simplifies the identification of such a model. Techniques to find the interaction matrices are developed. In the absence of noises, these matrices produce the gains of the fastest converging observer. In the presence of noises, they minimise the state estimation error in the same manner as a standard steady-state Kalman filter. Numerical examples illustrate both the theoretical and computational aspects of the proposed algorithms.

On the estimation problem of a class of continuous bioreactors with unknown input

The estimation problem of substrate–biomass reactors with isotonic or nonisotonic growth is addressed. The unmeasured reactor and time-varying feed substrate concentrations must be estimated from the dilution rate input and biomass measurements. First, the solvability conditions (nonlinear-global observability or detectability) are characterized, finding that: (i) the conditions depend on particular motions and growth mechanisms, and (ii) with isotonic (or nonisotonic) growth, the unknown input–state pair is observable in the classical (or nonstandard) single-valued (S) [or bivalued (B)] sense. Then, a robustly convergent S (or B)-observer is designed accordingly. The S-observer yields the estimate of the only unmeasured input–state trajectory pair associated with the measured input–output signal. The B-observer yields the estimates of the two possible unmeasured input–state trajectory pairs associated with the measured input–output signal, each one satisfying the reactor mass balances. The developments and findings are illustrated with representative (Monod and Haldane) examples through analytic assessment and numerical simulation.