Dynamic Linearization Data Model Research Articles

Cooperative MFAILC for Multiple Subway Trains With Actuator Faults and Actuator Saturation

In this paper, a cooperative model free adaptive iterative learning control (CMFAILC) scheme is proposed for multiple subway trains subjected to actuator faults and actuator saturation. The controller design includes: the dynamic linearization technique is adopted to transform the subway train system into an equivalent dynamic linearization data model along the iteration axis, the CMFAILC controller is designed by optimizing the cost function, the projection algorithm is applied to deal with the unknown nonlinear terms, the RBFNN is used to compensate the effects of unknown actuator faults, and the anti-windup compensator is introduced to tackle actuator saturation. Theoretical analysis shows that the speed tracking errors of multiple subway trains are bounded, and the distance of adjacent subway trains is stabilized in a safe range. Finally, the validity of the proposed CMFAILC scheme is verified through multiple subway train simulations.

A novel bipartite consensus tracking scheme for unknown nonlinear multi-agent systems: Theoretical analysis and applications

This article proposes a novel distributed data-driven bipartite consensus tracking scheme for bipartite consensus tracking problems of multi-agent systems with bounded disturbances and coopetition networks. The proposed scheme only uses the input/output data of each agent without requiring the agents’ dynamics. We obtain the equivalent dynamic linearization data model for a controlled plant using the dynamic linearization technique based on the pseudo partial derivative. Considering the cooperative and competitive interactions among agents, the proposed method ensures that agents with adversarial relationships implement bipartite consensus tracking tasks even if only a subset of agents can access the information from the virtual leader. Moreover, the strict proof process of convergence properties reveals that the tracking error coverages to a small range around the origin. We also establish a set of software and hardware platform to demonstrate the effectiveness of the proposed distributed data-driven bipartite consensus tracking method.

Chaotic motion control of a vibro-impact system based on data-driven control method

For the chaotic motion control of a vibro-impact system with clearance, the parameter feedback chaos control strategy based on the data-driven control method is presented in this article. The pseudo-partial-derivative is estimated on-line by using the input/output data of the controlled system so that the compact form dynamic linearization (CFDL) data model of the controlled system can be established. And then, the chaos controller is designed based on the CFDL data model of the controlled system. And the distance between two adjacent points on the Poincaré section is used as the judgment basis to guide the controller to output a small perturbation to adjust the damping coefficient of the controlled system, so the chaotic motion can be controlled to a periodic motion by dynamically and slightly adjusting the damping coefficient of the controlled system. In this method, the design of the controller is independent of the order of the controlled system and the structure of the mathematical model. Only the input/output data of the controlled system can be used to complete the design of the controller. In the simulation experiment, the effectiveness and feasibility of the proposed control method in this article are verified by simulation results.

Symposium Title: The Very Young Brain and the Auditory World

This paper proposes a novel proportional-integral observer (PIO) based high-order data-driven iterative learning control (HODDILC) for nonlinear batch processes with non-repetitive disturbances subject to input constraints. First, an equivalent dynamic linearization data model (DLDM) with an uncertainty term arising from the nonrepetitive disturbances is constructed in the batch direction. Based on the established DLDM along with a gradient estimation algorithm, a data-driven PIO is then designed to estimate the uncertainty term along the batch direction, followed by designing a HODDILC law in terms of the tracking errors over more than one previous batches and control inputs in the previous time instants of the current batch. Using the contraction mapping principle and matrix theory, rigorous convergence analysis is conducted in the iteration domain. A notable advantage of the proposed design is that only input and output measurement data are used rather than a process model. Finally, an illustrative example from the literature is given to demonstrate the effectiveness and merit of the proposed method.

Path Dependence in Disability.

The average prevalence of disability in most African countries is 10%, but for many it exceeds the global disability prevalence rate of 15%. The extent to which this disability capturing functional and activity limitations results in permanent job loss, lowered lifetime income and assets, in part, depends upon the extent to which the onset of limitations becomes permanent. In this paper, we use five rounds of longitudinal data from rural Malawi, a low-income African country with high prevalence of disability, to examine path dependence in activity limitations. We estimate a dynamic linear panel data model where the coefficient on the one-period lagged health outcome captures path dependence in limitations. Our preferred Arellano–Bover estimates show that males experience partial persistence in both the incidence and intensity of severe limitations and no persistence in other limitations. Females, on the other hand, exhibit no persistence in any type of limitations. Our findings have important policy implications for computing the long-term costs associated with onset of activity limitations as these costs can be moderated by the recovery exhibited in these limitations.

Data-driven discrete terminal sliding mode decoupling control method with prescribed performance

This paper proposes a data-driven terminal sliding mode decoupling controller with prescribed performance for a class of discrete-time multi-input multi-output systems in the presence of external disturbances and uncertainties. First, utilizing a discrete-time extended state observer and a compact form dynamic linearization data model, we derive a new data-driven mothod and establish the relationship between the input and output signals of controlled plant. Moreover, the disturbances, uncertainties, and couplings are suppressed owing to the application of the terminal sliding mode technique. Combined with the principle of prescribed performance control, the terminal sliding mode law with prescribed performance is derived. With the proposed data-driven method, the tracking error is lower, and the decoupling ability is improved. Furthermore, the stability of the control system is proven. Finally, a simulation is conducted on a three-tank system to demonstrate the effectiveness of the proposed scheme.

A Two-Step System for Dynamic Panel dealing with Endogeneity Issues and Causal Relationships

The paper addresses a computational method implementing a standard Dynamic Panel Data model with Generalized Method of Moment estimators to deal with endogeneity issues, because of omitted factors and unobserved heterogeneity, and causal relationships in large and long panel databases. The methodology takes the name of Two-step System Dynamic Panel Data that combines a first-step Bayesian procedure for selecting potential candidate predictors in a static linear regression model with a frequentist second-step procedure for estimating the parameters of a dynamic linear panel data model. An empirical example to the effects of obesity and socioeconomic factors on labor market outcomes among Italian regions is performed. Potential prevention policies and strategies to address key behavioral and diseases risk factors affecting labor market outcomes and social environment are also discussed.

PIO Based Data-Driven Iterative Learning Control for Nonlinear Batch Processes with Nonrepetitive Disturbances Subject to Input Constraints

Abstract This paper proposes a novel proportional-integral observer (PIO) based high-order data-driven iterative learning control (HODDILC) for nonlinear batch processes with non-repetitive disturbances subject to input constraints. First, an equivalent dynamic linearization data model (DLDM) with an uncertainty term arising from the nonrepetitive disturbances is constructed in the batch direction. Based on the established DLDM along with a gradient estimation algorithm, a data-driven PIO is then designed to estimate the uncertainty term along the batch direction, followed by designing a HODDILC law in terms of the tracking errors over more than one previous batches and control inputs in the previous time instants of the current batch. Using the contraction mapping principle and matrix theory, rigorous convergence analysis is conducted in the iteration domain. A notable advantage of the proposed design is that only input and output measurement data are used rather than a process model. Finally, an illustrative example from the literature is given to demonstrate the effectiveness and merit of the proposed method.

Model-Free Adaptive Control for Unknown MIMO Nonaffine Nonlinear Discrete-Time Systems With Experimental Validation.

In this article, a model-free adaptive control (MFAC) algorithm based on full form dynamic linearization (FFDL) data model is presented for a class of unknown multi-input multi-output (MIMO) nonaffine nonlinear discrete-time learning systems. A virtual equivalent data model in the input-output sense to the considered plant is established first by using the FFDL technology. Then, using the obtained data model, a data-driven MFAC algorithm is designed merely using the inputs and outputs data of the closed-loop learning system. The theoretical analysis of the monotonic convergence of the tracking error dynamics, the bounded-input bounded-output (BIBO) stability, and the internal stability of the closed-loop learning system is rigorously proved by the contraction mapping principle. The effectiveness of the proposed control algorithm is verified by a simulation and a quad-rotor aircraft experimental system.

Distributed Model-Free Bipartite Consensus Tracking for Unknown Heterogeneous Multi-Agent Systems with Switching Topology.

This paper proposes a distributed model-free adaptive bipartite consensus tracking (DMFABCT) scheme. The proposed scheme is independent of a precise mathematical model, but can achieve both bipartite time-invariant and time-varying trajectory tracking for unknown dynamic discrete-time heterogeneous multi-agent systems (MASs) with switching topology and coopetition networks. The main innovation of this algorithm is to estimate an equivalent dynamic linearization data model by the pseudo partial derivative (PPD) approach, where only the input–output (I/O) data of each agent is required, and the cooperative interactions among agents are investigated. The rigorous proof of the convergent property is given for DMFABCT, which reveals that the trajectories error can be reduced. Finally, three simulations results show that the novel DMFABCT scheme is effective and robust for unknown heterogeneous discrete-time MASs with switching topologies to complete bipartite consensus tracking tasks.

Model‐free adaptive fault‐tolerant control for subway trains with speed and traction/braking force constraints

This study investigates the subway train fault-tolerant control problem for the actuator fault with constraints of speed and traction/braking force. The complex subway train dynamics is first transformed into a compact form dynamic linearization data model with the help of the concept pseudo-partial derivative (PPD) proposed under the framework of model-free adaptive control. By using the approximation of the uncertainty fault with radial basis function neural network (RBFNN), then a model-free adaptive fault-tolerant control (MFAFTC) scheme is designed by only using saturated input/output data of a subway train. The proposed MFAFTC scheme consists of two data driven on-line learning updating algorithms for RBFNN-weights and PPD, and its convergence is strictly proved by rigorous theoretical analysis, and whose correctness and effectiveness are further verified by a numerical simulation.

The effect of training on workers\u2019 perceived job match quality

We study the causal effect of training on job match quality using longitudinal data for a representative sample of the Dutch population. We construct an index of workers’ perceived job match quality from five survey questions on job satisfaction and on how a worker’s education and skills match with the job. Based on a dynamic linear panel data model, which accounts for potential endogeneity in training, we find that training has significantly positive short- and long-term effects on job match quality. This is mainly driven by training for human capital accumulation. Further analysis incorporating job changes shows that training for job change purpose increases the probability to change jobs, but job changes immediately following this type of training do not significantly increase job match quality. On the other hand, those who change jobs 1 year after this training do tend to get a better-matched job.

Enhanced data-driven optimal iterative learning control for nonlinear non-affine discrete-time systems with iterative sliding-mode surface

In this work, an enhanced data-driven optimal iterative learning control (eDDOILC) is proposed for nonlinear nonaffine systems where a new iterative sliding mode surface (ISMS) is designed to replace the traditional tracking error in the controller design and analysis. It is the first time to extend the sliding mode surface to the iteration domain for systems operate repetitively over a finite time interval. By virtual of the new designed ISMS, the control design becomes more flexible where both the time and the iteration dynamics can be taken into account. Before proceeding to the controller design, an iterative dynamic linear data model is built between two consecutive iterations to formulate the linear input-output data relationship of the repetitive nonlinear nonaffine discrete-time system. The linear data model is virtual and does not have any physical meanings, which is very different to the traditional mechanism mathematical model. In the sequel, the eDDOILC is proposed by designing an objective function with respect to the proposed two-dimensional ISMS. Rigorous proof is provided to show the convergence of the proposed eDDOILC method. Furthermore, the results have been extended to a multiple-input multiple-output (MIMO) nonaffine nonlinear discrete-time repetitive system. In general, the proposed eDDOILC is data-driven where no explicit model information is included. It is illustrated that the presented eDDOILC is effective when applied to the nonlinear nonaffine uncertain systems.

Model-Free Adaptive Fault-Tolerant Control for Multiple Point-Mass Subway Trains With Speed and Traction/Braking Force Constraints

Abstract This paper considers the model-free adaptive fault-tolerant control for a subway train based on multiple point-mass model with the actuator fault under the constraints of speed and traction/braking force. The complex subway train model is first transformed into a compact form dynamic linearization (CFDL) data model with pseudo gradient (PG). The actuator fault function is approximated with radial basis function neural network (RBFNN). Finally a fault-tolerant controller only using saturated input/output (I/O) data is designed. The effectiveness of proposed controller is illustrated by a simulation.

Energy Saving Control of Bionic Robotic Fish based on Model-free Adaptive Control

Abstract In this paper, an energy-saving model-free adaptive control (MFAC) is proposed for the control of the bionic robotic fish. First, the original MFAC controllers for the speed based on the full form dynamic linearization data model are presented as an example of the controlled variable of the controlled object. Then by modifying the criterion function for control input optimization, an energy-saving MFAC controller is designed to reduce the energy consumption. The proposed method is a data-driven control method, which means that the control system designing process merely needs input and output (I/O) measurement data of the controlled plant, and does not need any model information. Simulation results demonstrate the effectiveness of the improved MFAC in speed and attitude control of the bionic robotic fish.

On Model-Free Adaptive Control and Its Stability Analysis

In this paper, the main issues of model-based control methods are first reviewed, followed by the motivations and the state of the art of the model-free adaptive control (MFAC). MFAC is a novel data-driven control method for a class of unknown nonaffine nonlinear discrete-time systems since neither explicit physical model nor Lyapunov stability theory or key technical lemma is used in the controller design and theoretical analysis except only for the input/output (I/O) measurement data. The basis of MFAC is the dynamic linearization data modeling method at each operating point of the closed-loop system. The established dynamic linearization data model is a virtual equivalent data relationship in the I/O sense to the original nonlinear plant by means of a novel concept called pseudo-partial derivative (PPD) or pseudo-gradient (PG) vector. Based on this virtual equivalent dynamic linearization data model and the time-varying PPD or PG estimation algorithm designed merely using the I/O measurements of a controlled plant, the MFAC system is constructed. The main contribution of this paper is that the theoretical analysis of the bounded-input bounded-output stability, the monotonic convergence of the tracking error dynamics, and the internal stability of the full form dynamic linearization based MFAC scheme are rigorously presented by the contraction mapping principle; the well known PID control and the traditional adaptive control for linear time-invariant systems are explicitly shown as the special cases of this MFAC. The simulation results verify the effectiveness of the proposed approach.

Data-driven model-free adaptive attitude control of partially constrained combined spacecraft with external disturbances and input saturation

This study presents an improved data-driven Model-Free Adaptive Control (MFAC) strategy for attitude stabilization of a partially constrained combined spacecraft with external disturbances and input saturation. First, a novel dynamic linearization data model for the partially constrained combined spacecraft with external disturbances is established. The generalized disturbances composed of external disturbances and dynamic linearization errors are then reconstructed by a Discrete Extended State Observer (DESO). With the dynamic linearization data model and reconstructed information, a DESO-MFAC strategy for the combined spacecraft is proposed based only on input and output data. Next, the input saturation is overcome by introducing an anti-windup compensator. Finally, numerical simulations are carried out to demonstrate the effectiveness and feasibility of the proposed controller when the dynamic properties of the partially constrained combined spacecraft are completely unknown.

Neural Network-Based Model-Free Adaptive Fault-Tolerant Control for Discrete-Time Nonlinear Systems With Sensor Fault

In this paper, the main focus is to cope with the fault detection and estimation (FDE) and fault-tolerant control (FTC) issues of nonlinear single input single output model-free system (MFS), while only the input/output data are utilized. First, in accordance with the pseudo-partial-derivative approach, the original system is transformed into a compact form dynamic linearization data model, in which only one parameter is employed. Second, an estimator is developed to detect the fault. A key highlight is the design of a time varying residual threshold. Moreover, an online neural network (NN) approximator is utilized to learn the unknown fault dynamics and an FTC strategy is reconstructed based on the optimality criterion. In contrast to the previous methods, the main features of the proposed method are as follows: 1) the fault related problem is solved for MFS; 2) the number of system parameters is largely reduced; and 3) NNs are utilized to establish a novel fault estimation scheme. Finally, a numerical simulation is provided to show the effectiveness of the proposed FDE and FTC strategy.

Dynamics in Physical Functioning Limitations

The extent to which physical functioning limitations result in permanent job loss, lowered lifetime income and assets, in part, depends upon the extent to which onset of these limitations becomes permanent. This paper uses five rounds of data from Malawi to examine path dependence in physical functioning limitations. We do so using a dynamic linear panel data model where the coefficient on the one-period lagged health outcome captures path dependence in functional limitations. Our preferred estimates indicate – (a) partial recovery from onset of functional limitations for males, (b) there is less recovery in severe limitations than moderate limitations for males, (c) perfect recovery from both moderate and severe functional limitation for females.

THE ROBUSTNESS OF ESTIMATORS FOR DYNAMIC PANEL DATA MODELS TO MISSPECIFICATION

Transition from economic theory to a testable form of model invariably involves the use of certain "simplifying assumptions." If, however, these are not valid, misspecified models result. This article considers estimation of the dynamic linear panel data model, which often forms the basis of testable economic hypotheses. The estimators of such a model are frequently similarly based on certain assumptions which appear to be often untenable in practice. Here, the performance of these estimators is analyzed in scenarios where the theoretically required conditions are not met. Specifically, we consider three such instances of serial correlation of the idiosyncratic disturbance terms; correlation of the idiosyncratic disturbance terms and explanatory variables; and, finally, cross-sectional dependence (as a robustness check to these findings, we also consider correlations between observed and unobserved heterogeneity terms). The major findings are that the limited tests readily available tend to have poor power properties and that estimators' performance varies greatly across scenarios. In such a wide array of experiments, it is difficult to pick-out just one "winner." However, a robust estimator across all experiments and parameter settings was a variant of the Wansbeek–Bekker estimator. This is a significant finding, as this estimator is infrequently used in practice. When the experiments are extended to include correlations between observed and unobserved heterogeneity terms, one might also consider, for across-the-board performance, the Blundell and Bond estimator.