Input-output Measurements Research Articles

Inverse Q-Learning Using Input-Output Data.

This article addresses the problem of learning the objective function of linear discrete-time systems that use static output-feedback (OPFB) control by designing inverse reinforcement learning (RL) algorithms. Most of the existing inverse RL methods require the availability of states and state-feedback control from the expert or demonstrated system. In contrast, this article considers inverse RL in a more general case where the demonstrated system uses static OPFB control with only input-output measurements available. We first develop a model-based inverse RL algorithm to reconstruct an input-output objective function of a demonstrated discrete-time system using its system dynamics and the OPFB gain. This objective function infers the demonstrations and OPFB gain of the demonstrated system. Then, an input-output Q -function is built for the inverse RL problem upon the state reconstruction technique. Given demonstrated inputs and outputs, a data-driven inverse Q -learning algorithm reconstructs the objective function without the knowledge of the demonstrated system dynamics or the OPFB gain. This algorithm yields unbiased solutions even though exploration noises exist. Convergence properties and the nonunique solution nature of the proposed algorithms are studied. Numerical simulation examples verify the effectiveness of the proposed methods.

Identification of Deformable Linear Object Dynamics from Input-output Measurements in 3D Space

Controlling deformable linear objects requires reliable models that capture their complex and high-dimensional dynamics. This paper aims to obtain accurate state-space models of such objects from input-output measurements only, tailored for the use in a model predictive control setting. The proposed identification approach is initialized by a physics-inspired baseline model which approximates a deformable linear object by a serial chain of rigid bodies connected via elastic joints. The nonlinear residual dynamics of the baseline model are then modeled by an artificial neural network, where a key element is the so-called guided residual search that first infers the latent residual and state trajectories in the time domain, in this way facilitating supervised learning of the neural network. The efficacy of the modeling approach is demonstrated on experimental data of the 3D end-point motion of a flexible aluminum rod, excited by a 7-axis Franka Panda robot arm. Compared to a physics-inspired baseline model, the proposed method achieves a 14% prediction performance increase, and compared to a fully neural network-based approach with a similar number of parameters, it achieves a 58% improvement, while also reporting a training time that is more than 5 times faster.

Automated Data–Driven Model Extraction and Validation of Inverter Dynamics with Grid Support Function

This research focuses on the evolving dynamics of the power grid, where traditional synchronous generators are being replaced by non-synchronous power electronic converter (PEC)-interfaced renewable energy sources. The non-linear dynamics must be accurately modeled to ensure the stability of future converter-dominated power systems (CDPS). However, obtaining comprehensive dynamic models becomes more complex and computationally intensive as the system grows. This study proposes a scalable and automated data-driven partitioned modeling framework for CDPS dynamics. The method constructs reduced-ordered dynamic linear transfer function models using input-output measurements from a PEC switching model. Validation experiments were conducted on single-house and multi-house scenarios, demonstrating high accuracy (over 97%) and significant computational speed improvements (6.5 times faster) compared to comprehensive models. This framework and modeling approach offer valuable insights for efficient analysis of power system dynamics, aiding in planning, operation, and dispatch.

Model-free predictive current control of Syn-RM based on time delay estimation approach

Abstract This paper investigates an optimal model-free control design for a synchronous reluctance motor (Syn-RM) with unknown nonlinear dynamic functions, parameter variations, and disturbances. The idea is to combine a predictive control with a time-delay estimation technique (TDE) in order to successfully deal with the system’s uncertainties and make the Syn-RM control scheme easy to implement in real-time. This model-free control strategy comprises two cascade control loops namely outer and inner loops. The outer loop is designed for the mechanical part of Syn-RM to ensure the convergence of the speed dynamics by using a proportional-integral controller while the inner loop is developed to control the uncertain dynamics of currents via an optimal robust controller. In the proposed current loop, the predictive control is enhanced by the inclusion of ultra-local model theory where dynamic functions and disturbances are estimated by instantaneous input-output measurements of the Syn-RM using the TDE approach. Moreover, a particle swarm optimization (PSO) algorithm is also proposed to find the optimal design parameters to improve the dynamic performances of the closed-loop control system. Numerical validation tests of the proposed TDE-based model-free predictive current control (TDE-MFPCC) method are performed in the simulation environment of the Syn-RM system, and the results show the robustness and the effectiveness of the proposed TDE-MFPCC compared to the conventional model-based PCC.

The Structural Convergence of New Members of the European Union: An Input-Output Perspective

This paper aims to approach the topic of structural convergence for new member states from the perspective of input-output analysis. Using a set of input-output measures, based on the OECD RStan database, and a number of unit-root tests, both for individual time series and panel data, this paper tests whether there is convergence relative to the production structure of the German economy. Although the individual tests are somewhat mixed, the panel unit-root tests indicate the presence of convergence. The results highlight the importance of assessing structural convergence based on panel tests. They also highlight that as nominal and real convergence are gradually achieved, structural convergence should also become more prioritized.

Aircraft parameter estimation using a stacked long short-term memory network and Levenberg-Marquardt method

To effectively estimate the unknown aerodynamic parameters from the aircraft’s flight data, this paper proposes a novel aerodynamic parameter estimation method incorporating a stacked Long Short-Term Memory (LSTM) network model and the Levenberg-Marquardt (LM) method. The stacked LSTM network model was designed to realize the aircraft dynamics modeling by utilizing a frame of nonlinear functional mapping based entirely on the measured input–output data of the aircraft system without requiring explicit postulation of the dynamics. The LM method combines the already-trained LSTM network model to optimize the unknown aerodynamic parameters. The proposed method is applied by using the real flight data, generated by ATTAS aircraft and a bio-inspired morphing Unmanned Aerial Vehicle (UAV). The investigation reveals that for the two different flight data, the designed stacked LSTM network structure can maintain the efficacy of the network prediction capability only by appropriately adjusting the dropout rates of its hidden layers without changing other network parameters (i.e., the initial weights, initial biases, number of hidden cells, time-steps, learning rate, and number of training iterations). Besides, the proposed method’s effectiveness and potential are demonstrated by comparing the estimated results of the ATTAS aircraft or the bio-inspired morphing UAV with the corresponding reference values or wind-tunnel results.

Green and Low-Carbon Efficiency Assessment of Urban Agglomeration Logistics Industry: Evidence from China’s Beijing-Tianjin-Hebei Metropolitan Area (2008–2020)

With the advent of the post-industrial era, the rapid development of e-commerce has propelled the logistics industry to become the lifeline of the national economy, supporting the orderly flow of resource elements between cities. However, the concerning issues of excessive energy consumption and low logistics efficiency in the transportation process have come to the forefront. The introduction of China’s dual-carbon policy goals indicates that enhancing regional logistics’ green and low-carbon efficiency is key to solving the global logistic sustainability problem. Nowadays, the logistics sector’s efficiency in producing green and low-carbon emissions has been quantified using an input-output measurement index. Using data from 2008 to 2020 from the dynamic panel of the logistics sector in the urban agglomerations of Beijing, Tianjin, and Hebei, the three-stage SBM-DEA and Malmquist index quantitative evaluation models are selected to estimate the logistic green and low-carbon development efficiency comprehensively. The analysis discovered that green and low-carbon logistics in the Beijing-Tianjin-Hebei metropolitan agglomeration are relatively efficient overall, and the urban siphon effect of Beijing and Tianjin is noticeable. Once the impact of environmental variables and random errors is eliminated, it becomes evident that these factors tend to inflate the overall technical efficiency. Technical efficiency levels are the primary factor leading to regional logistics inefficiencies. Additionally, it is essential to note that scale efficiency positively affects urban development, leading to a rebound effect, summarizing the existing problems combined with the visualization map, and putting forward corresponding policy suggestions, which is of great practical significance.

Impact of Uncertainty Quantification on Small Signal Stability of Power System

The statistical uncertainty quantification of the injected uncertainties into a power system has been done to study the response of the power system to such injected randomness. The dominant electromechanical modes have been analysed and the frequency spectrum of the trajectories obtained for various state variables has been used to measure the impact of injected randomness on the system. The idea of Statistical Distance between the point of input perturbations and output measurement data has been explored. The simulation results indicate that there is a direct relationship between the input uncertainty and output measurement variable and is greatly affected by the magnitude and location of random injection. The above framework has been validated using Kundur's two area system and IEEE 14 Bus system.

Bias-Correction Errors-in-Variables Hammerstein Model Identification

In this paper, a Bias-Correction Least-Squares (BCLS) algorithm is proposed for identifying block-oriented Errors-In-Variables nonlinear Hammerstein (EIV-Hammerstein) systems. Due to both the input and output of the EIV-Hammerstein system are observed with additive white noises, the estimation bias of traditional LS algorithm is introduced. The estimation bias is derived from a consistency point of view, which is a function about noises variances and monomial of noiseless system input-output measurements. A bias-estimation scheme based only on the available noisy measurements is then proposed for consistent identification of the monomial of noiseless system input-output measurements in a recursive form. In particular, a specific algorithm based on minimizing the output prediction error is given to find out the unknown noise variances for practical applications. Such that the noise effect can be eliminated and the consistent estimated parameters are obtained. The effectiveness of the proposed method is demonstrated by a simulation example and an experimental prototype of wireless power transfer system.

Transmission-based noise spectroscopy for quadratic qubit-resonator interactions

We develop a theory describing the transient transmission through noisy qubit-resonator systems with quadratic interactions as are found in superconducting and nanomechanical resonators coupled to solid-state qubits. After generalizing the quantum Langevin equations to arbitrary qubit-resonator couplings, we show that only the cases of linear and quadratic couplings allow for an analytical treatment within standard input-output theory. Focussing on quadratic couplings and allowing for arbitrary initial qubit coherences, it is shown that noise characteristics can be extracted from input-output measurements by recording both the averaged fluctuations in the transmission probability and the averaged phase. Our results represent an extension to the field of transmission-based noise spectroscopy with immediate practical applications.

Enhanced P-Type Control: Indirect Adaptive Learning From Set-Point Updates

In this article, an indirect adaptive iterative learning control (iAILC) scheme is proposed for both linear and nonlinear systems to enhance the P-type controller by learning from set points. An adaptive mechanism is included in the iAILC method to regulate the learning gain using input–output measurements in real time. An iAILC method is first designed for linear systems to improve control performance by fully utilizing model information if such a linear model is known exactly. Then, an iterative dynamic linearization (IDL)-based iAILC is proposed for a nonlinear nonaffine system, whose model is completely unknown. The IDL technique is employed to deal with the strong nonlinearity and nonaffine structure of the systems such that a linear data model can be attained consequently for the algorithm design and performance analysis. The convergence of the developed iAILC schemes is proved rigorously, where contraction mapping, two-dimensional (2-D) Roesser’s system theory, and mathematical induction are employed as the basic analysis tools. Simulation studies are provided to verify the developed theoretical results.

Data-Driven Adaptive Iterative Learning Control of a Compliant Rehabilitation Robot for Repetitive Ankle Training

This letter investigates the repetitive range of motion (ROM) training control for a compliant ankle rehabilitation robot (CARR). The CARR utilizes four pneumatic muscle (PM) actuators to manipulate the ankle with three rational degree-of-freedoms (DoFs) and soft human-robot interaction, but the strong-nonlinearity of the PM actuator makes precise tracking difficult. To improve the training effectiveness, a data-driven adaptive iterative learning controller (DDAILC) is proposed based on compact form dynamic linearization (CFDL) with estimated pseudo-partial derivative (PPD). Instead of using a PM dynamic model, the estimated PPD is updated merely by online input-output (I/O) measures. Sufficient conditions are established to guarantee the convergence of tracking errors and the boundedness of control input. Experimental studies are conducted on ten human participants with two therapist-resembled trajectories. Compared with other data-driven methods, the proposed DDAILC demonstrates significant improvement on tracking performance.

Data-Driven Finite Control-Set Model Predictive Control for Modular Multilevel Converter

This article investigates a data-driven-based predictive current control (DD-PCC) approach for a modular multilevel converter (MMC) to circumvent the sensitiveness to parameter variation and unmodeled dynamics of a finite control-set model predictive control (FCS-MPC) method. By integrating a model-free adaptive control (MFAC)-based data-driven solution into the FCS-MPC framework, the performance deterioration caused by model uncertainties is suppressed. The design of the suggested controller is only based on input–output measurement data, where neither the parameter information nor the knowledge of detailed MMC models is required, leading to improved robustness against parameter drifts and model uncertainness. Moreover, a simplified cost function formula that takes into account output current tracking and circulating current regulation is constructed to efficiently determine the optimal insertion index of each arm. Finally, simulation and experimental results are obtained to verify the steady-state, dynamics, and robustness performance of the proposed approach.

Data-Driven Formation Control for Unknown MIMO Nonlinear Discrete-Time Multi-Agent Systems With Sensor Fault.

A data-driven distributed formation control algorithm is proposed for an unknown heterogeneous non-affine nonlinear discrete-time MIMO multi-agent system (MAS) with sensor fault. For the considered unknown MAS, the dynamic linearization technique in model-free adaptive control (MFAC) theory is used to transform the unknown MAS into an equivalent virtual dynamic linearization data model. Then using the virtual data model, the structure of the distributed model-free adaptive controller is constructed. For the incorrect signal measurements due to the sensor fault, the radial basis function neural network (RBFNN) is first trained for the MAS under the fault-free case, then using the outputs of the well-trained RBFNN and the actual outputs of MAS under sensor fault case, the estimation laws of the unknown fault and system parameters in the virtual data model are designed with only the measured input-output (I/O) data information. Finally, the boundedness of the formation error is analyzed by the contraction mapping method and mathematical induction method. The effectiveness of the proposed algorithm is illustrated by simulation examples.

Distributed Consensus Control of Vehicular Platooning Under Delay, Packet Dropout and Noise: Relative State and Relative Input-Output Control Strategies

In this article, we study the distributed control problem of vehicular platooning by taking into account network imperfections, namely the input delay and the packet dropout. The delay occurs within the inter network of each vehicle from its controller to its actuators and the packet dropout occurs within the intra network of vehicles when information is exchanged between vehicles. Relative state-feedback and relative input-output feedback control strategies are adopted for the vehicular platooning under network imperfection. To this end, and to avoid the requirement of relative state information, the relative state measurements are constructed for platoon systems based on a distributed observer via only relative input-output measurements and in the presence of delay, packet dropout and noise. Conditions for synchronization are provided in terms of the unique positive-definite solutions to some algebraic Riccati equations. Simulations validate the theoretical results.

Auto tuned PID and neural network predictive controller for a flow loop pilot plant

This paper presents an advanced control strategy that uses the neural network (NN) predictive controller to govern the dynamics of a Flow loop pilot plant. The set point tracking using NN Predictive controller and conventional PID strategies are investigated. The control objective is to keep the outlet flow of the Single Input Single Output Flow loop at a reference value. An orifice meter as a flow measuring device and Variable Frequency Drive as a final control element is selected to collect Input, Output data. System Identification toolbox in MATLAB is used to construct the model of a flow loop from measured input–output data. Various models are estimated, validated and realized that the transfer function model gives the best fit. This model is used to build the PID and NN Predictive controller. NN predictive controller parameters are set, by the designer's expertise. The performance of a NN predictive controller is then compared with the conventional auto-tuned PID controller. Experimental results are presented, which emphasizes the superiority and effectiveness of the NN predictive controller.

Linear Tracking MPC for Nonlinear Systems—Part II: The Data-Driven Case

In this article, we present a novel data-driven model predictive control (MPC) approach to control unknown nonlinear systems using only measured input–output data with closed-loop stability guarantees. Our scheme relies on the data-driven system parameterization provided by the fundamental lemma of Willems <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> We use new input–output measurements online to update the data, exploiting local linear approximations of the underlying system. We prove that our MPC scheme, which only requires solving strictly convex quadratic programs online, ensures that the closed loop (practically) converges to the (unknown) optimal reachable equilibrium that tracks a desired output reference while satisfying polytopic input constraints. As intermediate results of independent interest, we extend the fundamental lemma to affine systems and we derive novel robustness bounds w.r.t. noisy data for the open-loop optimal control problem, which are directly transferable to other data-driven MPC schemes in the literature. The applicability of our approach is illustrated with a numerical application to a continuous stirred tank reactor.

Bayesian Estimation of Oscillator Parameters: Toward Anomaly Detection and Cyber-Physical System Security.

Cyber-physical system security presents unique challenges to conventional measurement science and technology. Anomaly detection in software-assisted physical systems, such as those employed in additive manufacturing or in DNA synthesis, is often hampered by the limited available parameter space of the underlying mechanism that is transducing the anomaly. As a result, the formulation of anomaly detection for such systems often leads to inverse or ill-posed problems, requiring statistical treatments. Here, we present Bayesian inference of unknown parameters associated with a generic actuator considered as a representative vital element of a cyber-physical system. Via a series of experimental input-output measurements, a transfer function for the actuator is obtained numerically, which serves as our model for the proposed method. Linear, nonlinear, and delayed dynamics may be assumed for the actuator response. By devising a code-based malicious signal, we study the efficacy of Bayesian inference for its potential to produce a detection, including uncertainty quantification, with a remarkably small number of input data points. Our approach should be adaptable to a variety of real-time cyber-physical anomaly detection scenarios.

Output synchronization of multi-agent systems via reinforcement learning

In this paper, the measured input–output data sequences with pinning gain are proposed via the topology of multi-agent systems (MAS), where the requirement of reinforcement learning algorithm on internal state is avoided by pinning gain and the measured data of leader and neighbors. Besides, a data-based tracking state is given, which can be applied to MAS with different control matrices. According to the sequences and tracking state, a distributed control policy and corresponding reinforcement learning algorithm are proposed for the output synchronization. The proposed algorithm overcomes the shortcoming that previous algorithms can not be applied to MAS with different control matrices in the absence of model information and full-state vector. Finally, the effectiveness of proposed algorithm is verified by simulation examples.

Enhanced extended state observer-based model-free force control for a Series Elastic Actuator

With inherent mechanical compliance and programmable force controllability, the Series Elastic Actuator (SEA) has been a popular choice for modern mechanical systems. It is crucial to realize accurate and robust force control for a SEA, especially in a physical human–robot interaction condition. In this paper, a model-free force controller (MFFC) for the SEA is developed, which relies only on the input–output measurement. Firstly, based on the ultra-local model concept, the SEA force dynamics is reformulated in a simpler way, and all unknown terms are grouped as one term called lumped total disturbance. Secondly, an enhanced extended stated observer (EESO) is designed to simultaneously estimate the lumped total disturbance, the force velocity, and the control input gain. Thirdly, a disturbance rejective intelligent PD (iPD) control scheme is established. In addition, the effects of the control input gain are analyzed and the close-loop stability is demonstrated with Lyapunov method. The control accuracy and robustness of the MFFC are verified in a physical human–robot interaction environment.