Varying System Dynamics Research Articles

Piezoelectric Actuators in Smart Engineering Structures Using Robust Control.

In this study, piezoelectric patches are used as actuators to dampen structural oscillations. Damping oscillations is a significant engineering challenge, and the use of piezoelectric patches in smart structures allows for a reduction in oscillations through sophisticated control methods. This analysis involved H-infinity (H∞) robust analysis. H∞ (H-infinity) control formulation is a robust control design method used to ensure system stability and performance under disturbances. When applied to piezoelectric actuators in smart structures, H∞ control aims to design controllers that are robust to variations in system dynamics, external disturbances, and modeling uncertainties, while meeting specified performance criteria. This study outlines the piezoelectric effects and advanced control strategies. A structural model was created using finite elements, and a smart structural model was analyzed. Subsequently, dynamic loads were applied and oscillation damping was successfully achieved by employing advanced control techniques.

Intelligent Structure Identification and Robust Control Implementation

This study outlines a comprehensive strategy for designing and implementing robust controllers tailored for intelligent structures. This study presents a robust control-based structural identification technique that uses the input/output data of the system to construct a state-space mode and frequency domain. To reduce vibrations, a robust controller is created using the control Simulink model. The identification and robust control of smart structures using Simulink involve a combination of system identification techniques and control design within the MATLAB Simulink environment. The key challenge is dealing with uncertainties and variations in system dynamics. Robust control methods have been employed to suppress the vibrations during dynamic disturbances. These methods are important for mechanical systems operating under stochastic loading conditions.

Run-indexed time-varying Bayesian optimization with positional encoding for auto-tuning of controllers: Application to a plasma-assisted deposition process with run-to-run drifts

Bayesian optimization (BO) has emerged as a useful paradigm for automatic calibration (aka auto-tuning) of advanced optimization- and learning-based controllers whose closed-loop performance depends on the choice of several tuning parameters in highly nonlinear and nonconvex ways. However, BO approaches to controller auto-tuning commonly rely on the assumption that system dynamics remain constant, which does not hold for systems with time-varying dynamics, for example, due to gradual aging or persistent environmental drifts. This challenge can be further compounded when gradual and persistent system drifts occur over a series of process runs. Existing time-varying BO (TVBO) approaches with spatio-temporal kernels fall short of effectively handling an integer run index, which is imperative for capturing run-to-run changes in the system behavior. To this end, this paper presents a run-indexed TVBO (RI-TVBO) approach that can systematically account for run-to-run process drifts as the system is queried over sequential process runs. The proposed approach relies on adapting the non-stationary Wiener process kernel to accommodate an integer run index, instead of time. This is done via positional encoding that incorporates the integer run index and, thus, enables describing run-to-run variations in system dynamics. The positional embedding vector associated with each run index is then mapped onto a scalar value to leverage the relationships between different process runs within the probabilistic surrogate model of the objective function in RI-TVBO. The performance of RI-TVBO is evaluated for auto-tuning of an offset-free model predictive controller for a low-temperature plasma-assisted process for thin film deposition. Simulation results demonstrate the superior performance of RI-TVBO over standard BO and TVBO under different scenarios of run-to-run process drifts encountered in plasma-assisted deposition processes in semiconductor manufacturing.

A Modular IoT-Based Architecture for Logistics Service Performance Assessment and Real-Time Scheduling towards a Synchromodal Transport System

Logistics is significantly impacted by quality/quantity issues associated with data collection and data sharing restrictions. Nonetheless, public data from national entities and internet-of-things (IoT) solutions enable the development of integrated tools for performance analysis and real-time optimization of logistics networks. This study proposes a three-module data-driven system architecture that covers (a) logistics data collection tools, (b) logistics services performance evaluation, and (c) the transition to synchromodal systems. Module 1 integrates multisource data from national logistics platforms and embedded devices placed within intermodal containers. A multigraph representation of the problem is conceived. Environmental, economic, and operational data are generated and injected into a digital twin. Thus, key performance indicators (KPIs) are computed by simulation or direct transformation of the collected data. Module 2 uses Multi-directional Efficiency Analysis, an optimization algorithm that benchmarks multimodal transportation routes of containers using prior KPIs. Outputs are a technical performance index relevant to logistics clients and improvement measures for logistics service providers. A real case study application of the solution proposed for Module 2 is presented. Module 3 provides real-time scheduling and assignment models using CP-sat solvers, accommodating varying system dynamics and resource availability, minimizing makespan and operational costs.

Emerging robust and data‐driven control methods for uncertain learning systems

Emerging robust and data‐driven control methods for uncertain learning systems

Decentralized iterative learning control for constrained collaborative tracking

ABSTRACTHigh performance collaborative tracking where multiple subsystems work together repetitively to track a reference has found applications in various areas. To meet the high accuracy requirements, iterative learning control (ILC) has recently been used. However, most existing designs ignore the system constraints that are often encountered in practice. Furthermore, they usually have a centralized structure (or controller parameter) that needs to be redesigned or re‐tuned when the system changes which may cause difficulties for handling large scale systems. In this article, we consider the constrained collaborative tracking problem and propose a novel constrained ILC design. The resulting ILC algorithm guarantees not only the satisfaction of the system constraints, but also the monotonic convergence of the collaborative tracking error norm to a minimum (possible) value. We further develop a decentralized implementation of the proposed algorithm using the alternating direction method of multipliers, allowing the design to scale up to large scale and varying system dynamics. Finally, we provide numerical examples to verify the algorithms' effectiveness.

Real-Time Fault Estimation for a Class of Discrete-Time Linear Parameter-Varying Systems

Estimating and detecting faults is crucial in ensuring safe and efficient automated systems. In the presence of disturbances, noise, or varying system dynamics, such estimation is even more challenging. To address this challenge, this article proposes a novel filter to estimate multiple fault signals for a class of discrete-time linear parameter-varying (LPV) systems. The design of such a filter is formulated as an optimization problem and is solved recursively, while the system dynamics may vary over time. Conditions for the existence and detectability of the fault are introduced and the problem is formulated and solved using the quadratic programming framework. We further propose an approximate scheme that can be arbitrarily precise while it enjoys an analytical solution, which supports real-time implementation. The method is illustrated and validated on an automated vehicle’s lateral dynamics, which is a practically relevant example for LPV systems. The results show that the estimation filter can decouple unknown disturbances and known or measurable parameter variations in the dynamics while estimating the unknown fault.

Robust high-bandwidth control of nano-positioning stages with Kalman filter based extended state observer and H∞ control.

The achievable performance of the piezo-actuated nano-positioning stages is severely limited by the intrinsic nonlinearities of the actuators, the lightly damped resonant mode of the flexure-hinge mechanism, and the external disturbances. To overcome all these limitations, this paper presents a novel robust dual-loop control scheme with a Kalman filter-based extended state observer and H∞ control for nano-positioning stages to implement high-bandwidth tracking operations. In this scheme, the extended state observer (ESO) is first developed and assisted by the identified system model to estimate both the system states and total disturbances, where the estimated disturbance is compensated by the direct feedback. In particular, to further improve the estimation performance, the Kalman filter is thus incorporated into the ESO to optimize the observer gain. Then, the state-feedback-based inner-loop controller is designed via the pole-placement method to damp the resonant mode of nano-positioning stages. Finally, a H∞ robust controller is adopted in the outer-loop to eliminate the influence resulting from the external disturbances, nonlinearities, and unmodeled dynamics on tracking operations. To validate the effectiveness of the proposed approach, comparative experiments are conducted on a piezo-actuated nano-positioning stage. Experimental results demonstrate that the proposed control scheme improves the control bandwidth of the system from 3.6 kHz (the stand-alone H∞ controller) to 5.52 kHz, which is 93.5% of the first resonant frequency of the original system. Moreover, it shows excellent robustness against the variation of system dynamics due to the change in the mounted mass and the external disturbances.

Diagnosing Spatiotemporal Traffic Anomalies With Low-Rank Tensor Autoregression

Traffic data collected from sensor networks often exhibit strong spatial correlations and recurrent temporal patterns. Learning these patterns and diagnosing anomalies in such spatiotemporal traffic data is critical to improving transportation systems and services. This paper proposes a dynamic framework to model spatiotemporal traffic data, with a particular application on diagnosing anomalies. Within the framework, we focus on characterizing the variation in system dynamics with a time-varying vector autoregressive model. We impose a low-rank tensor structure to model the collection of time-varying system matrices. As the temporal factor matrix captures the principal patterns/signatures across all time-varying system matrices, it is a useful tool to diagnose abnormal generative mechanisms and unexpected temporal patterns. We demonstrate the proposed tensor learning framework’s effectiveness by experimenting with a synthetic data set and real-world spatiotemporal traffic speed data set. The results show the superiority of the proposed model in uncovering anomalous traffic network dynamics.

Optimized Artificial Neural Network Model and Compensator in Model Predictive Control for Anomaly Mitigation

Abstract This paper presents a new artificial neural network (ANN)-based system model that concatenates an optimized artificial neural network (OANN) and a neural network compensator (NNC) in series to capture temporally varying system dynamics caused by slow-paced degradation/anomaly. The OANN comprises a complex, fully connected multilayer perceptron, trained offline using nominal, anomaly free data, and remains unchanged during online operation. Its hyperparameters are selected using genetic algorithm-based meta-optimization. The compact NNC is updated continuously online using collected sensor data to capture the variations in system dynamics, rectify the OANN prediction, and eventually minimize the discrepancy between the OANN-predicted and actual response. The combined OANN–NNC model then reconfigures the model predictive control (MPC) online to alleviate disturbances. Through numerical simulation using an unmanned quadrotor as an example, the proposed model demonstrates salient capabilities to mitigate anomalies introduced to the system while maintaining control performance. We compare the OANN–NNC with other online modeling techniques (adaptive ANN and multinetwork model), showing it outperforms them in reference tracking of altitude control by at least 0.5 m and yaw control by 1 deg. Moreover, its robustness is confirmed by the MPC consistency regardless of anomaly presence, eliminating the need for additional model management during online operation.

Modeling and Dynamic Assignment of the Adaptive Buffer Spaces in Serial Production Lines

Abstract In production systems, the buffer capacities have usually been assumed to be fixed during normal operations. Inspired by the observations from the real industrial operations, a novel concept of Adaptive Buffer Space (ABS) is proposed in this paper. The ABS is a type of equipment, such as movable racks or mobile robots with racks, which can be used to provide extra storage space for a production line to temporarily increase certain buffers’ capacities in a real-time fashion. A good strategy to assign and reassign the ABS can significantly improve real-time production throughput. In order to model the production systems with changing buffer capacities, a data-driven model is developed to incorporate the impact of buffer capacity variation in system dynamics. Based on the model, a real-time ABS assignment strategy is developed by analyzing real-time buffer levels and machine status. The strategy is demonstrated to be effective in improving the system throughput. An approximate dynamic programming algorithm, referred to as ABS-ADP, is developed to obtain the optimal ABS assignment policy based on the strategy. Traditional ADP algorithms often initialize the state values with zeros or random numbers. In this paper, a knowledge-guided value function initialization method is proposed in ABS-ADP algorithm to expedite the convergence, which saves up to 80% computation time in the case study.

Direct Adaptive Controllers for a Multi Input Multi Output Process

Control of nonlinear dynamic systems is of great practical importance, since virtually all real world systems belong to this class. The prevalent approach is to use a model of the process linearized about the steady state operating point to design linear controllers. The Multi Input Multi Output (MIMO) process considered in this paper has varying system dynamics. This paper focuses on the performance evaluation of multi-loop Adaptive MIT(AMIT) controller and Adaptive PI(API) controller with vanishing adaptation technique for a laboratory interacting coupled tank MIMO process.

System identification and control design of a nonlinear continuously stirred tank reactor

This study presents a black-box identification and a self-tuning control design of a nonlinear continuously stirred tank reactor (CSTR). Analyzing and controlling the CSTR is a challenging topic because of its highly nonlinear dynamics and its sensitivity, which, in turn, requires a high-performance control design. The data set used in the identification is collected from the nonlinear dynamical equations of the CSTR in the presence of white and colored noises for considering realistic conditions. Then, linear model structures are selected, and by employing the online and offline estimation methods, the best representation of the CSTR is determined based on a fitness index. By employing the results of the online identification algorithms, a self-tuning proportional–integral–derivative (PID) controller is designed which provides an ability to control the CSTR in the presence of unwanted signals (i.e., noise and disturbance) and variations in system dynamics by considering several tuning parameters including forgetting factor and suitable initial values. The performance of the proposed approach is compared with several works in the literature, reflecting the superiority of the presented technique.

Range control MPC with application to Vapor Compression Cycles

A range control MPC formulation is presented for control of Vapor Compression Cycles (VCC). Variation in system dynamics is handled through application of range control which is a generalization of a tracking problem whereby the set-point for the system output is replaced by a funnel (time-varying upper and lower bounds on the system output) which is implemented as a set of soft constraints. Vapor cycle is optimized to maximize device efficiency while ensuring feasibility of operation. Experimental validation of the control design, embedded in a heat-pump controller, is presented.

Gain-scheduled control of wind turbine exploiting inexact wind speed measurement for full operating range

This note tackles the problem of variable-speed variable-pitch wind turbine control in the whole wind speed range. Gain-scheduled (GS) control strategy is exploited to provide maximum power point tracking operation and to achieve effective pitch angle regulation. The wind turbine is modeled in the form of a linear parameter varying (LPV) system using wind speed as the scheduling parameter to cope with the varying system dynamics. The wind speed measurement errors are systematically considered in the design procedure. Modeling error is handled through employing an induced L2-gain performance criterion. The proposed method results a common gain-scheduled controller for the whole operating region. A benchmark example illustrates the advantages of the presented method.

Fault compensation by online updating of genetic algorithm-selected neural network model for model predictive control

This paper presents a methodology that combines a dual-net model and the model predictive control (MPC) to compensate degraded system performance caused by slow-paced faults/anomalies. The dual-net model is comprised of an offline and an online artificial neural networks (ANNs) along with a switch that selects one of them for MPC. Through selective online updating of weight parameters, the online ANN is able to accurately capture the fault-induced variations in system dynamics, and can be used for MPC reconfiguration and fault compensation. Specifically, the system dynamics is identified by training a multilayer perceptron (MLP). To improve the model accuracy, a meta-optimization approach based on the genetic algorithm is applied to optimize the MLP hyperparameters and the training algorithm. A dual-thread decision maker is proposed to manage the robust model updating scheme and the dual-net model switch. A case study of numerical simulation using an unmanned quadrotor is undertaken to verify the feasibility of the proposed method to mitigate performance degradation. Salient performance in the response prediction and control, subject to gradually growing anomaly is successfully demonstrated. Quantitatively, the proposed updating model outperforms the offline ANN model and yields 2× and 4× lower errors, respectively, for prediction and control of the system response.

Time-varying parameter model reference adaptive control and its application to aircraft

A new Second Order (SO) adaptive control approach, that accounts for both matched and unmatched unknown time-varying uncertainties, is proposed in this paper. It is developed based on the Predictor based Model Reference Adaptive Control (PMRAC) structure. In this approach, the estimated parameter dynamics is composed of a σ-modified PMRAC update law and a predicted parameter dynamics, with projection operator. The predicted parameter dynamics consists of a stable known eigendynamics, a feedback of an approximate parameter error, and the remaining part linearly parameterized by a set of known regressor functions combined with an adaptive estimate of unknown second-order parameters, which are updated by a Lyapunov design. Due to the estimation of time derivatives of the parameters, the proposed predictor based SO-MRAC is hence capable of capturing the rapid parameter variations in system dynamics. Furthermore, a generalized ultimate boundedness formulation based on the Lyapunov theory is presented for its stability analysis. This formulation provides separate bounds for each of the state vector partitions, mainly tracking and parameter estimation errors. Finally, this proposed approach is applied to control of lateral dynamics of an unmanned aircraft to illustrate the usefulness of the proposed approach. The simulation results are found to be quite satisfactory both in terms of tracking and adaptation performance.

Adaptive model predictive control for linear time varying MIMO systems

A robust, adaptive Model Predictive Control (MPC) approach for asymptotically stable, constrained linear time-varying (LTV) systems with multiple inputs and outputs is proposed. The approach consists of two-steps, carried out on-line with a receding horizon strategy. In the first one, a real-time Set Membership identification algorithm exploits the measured input–output data and the available prior knowledge to build and refine a set of admissible models of the plant (Feasible Parameter Set, FPS). This set is guaranteed to contain also the true system dynamics under the considered working assumptions. In the second step, a robust finite-horizon optimal control problem is formulated and solved. The variation of system dynamics is taken into account by inflating the FPS over the prediction horizon, according to worst-case bounds, assumed a priori, on the parameters’ rate of change. The resulting optimal control sequence guarantees that the outputs of all possible plants inside the FPS satisfy the operational constraints, also considering all possible future parameter changes. The main theoretical properties of the proposed approach are demonstrated and the method is showcased in numerical simulations, highlighting the fundamental improvement over previous approaches not designed for LTV systems.

Chatter Suppression in Robotic Milling by Control of Configuration Dependent Dynamics

Serial industrial robots tend to have low static stiffness and possess configuration dependent dynamics. The former results in an earlier onset of chatter compared to machine tools whereas the latter introduces robot configuration dependency into the process stability. In this paper, the robot configuration is continuously varied around its functional redundancy to create time varying system dynamics to suppress and/or avoid chatter in a robotic milling operation. It was shown that, at low spindle speeds it is possible to suppress chatter even though the quasi-static stability predictions may indicate instability at different robot configurations for the chosen process parameters.

Development of a protocol for environmental impact studies using causal modelling

1- A major challenge when assessing the impacts of human activities on the globe's natural resources is to account for the impacts of land use versus natural spatiotemporal variation in system dynamics. Current freshwater assessments are unable to resolve spatiotemporal confounding through study designs or statistical analyses. Observational studies typically fail to consider the period-by-location interaction in the absence of an impact.2- Here, I address the problem of spatiotemporal confounding using causal modelling based on spatiotemporal data to infer causal effects of wastewater on biotic ecosystems. A combination of statistical analysis and causation theory was used to address confounding bias. Benthic macroinvertebrate and environmental variable data were collected from locations upstream and downstream of a wastewater treatment plant discharge point in south-eastern Australia over 1.5 years. The composite hypotheses based on the theoretical relationships among these variables were summarised in a causal diagram. Model building and testing was conducted using Structural Equation Modelling (SEM). Distance-based redundancy analysis (dbRDA) was used for model building and hypothesis testing.3- The results indicated that the causal effects of effluent on macroinvertebrate communities could be inferred using causal modelling. The macroinvertebrate communities responded to water quality degradation with a clear shift in community composition after the discharge point and this change varied seasonally. Chlorophyll a, total organic carbon, zinc, conductivity, temperature, and its interaction with conductivity, were important determinants of the macroinvertebrate community composition. Causal models also explained the spatiotemporal variations in environmental variables. The consistency of data with the structure of the causal diagram was confirmed with global Fisher's C-test.4- Causal modelling has been shown to be a useful tool in environmental impact studies. In this study, the usefulness of causal modelling was attributed to its proficiency in combining all of the elements necessary for an efficient risk assessment through dealing with spatial and temporal confounding, facilitating communication between scientists and resource managers, and supporting management decision making.