System Parameter Uncertainties Research Articles

Development of a novel model matching decentralized controller design algorithm and its experimental validation through load frequency controller implementation in restructured power system using TMS320F28379D controlCARD

A three-stage decentralized controller design algorithm is developed to achieve setpoint tracking and disturbance rejection in MIMO systems with communication time delay, and nonlinearities such as saturation and dead band. The first stage involves reference model formulation. The second stage comprises equating approximate generalized time moments/approximate generalized Markov parameters of closed-loop system model with reference model at certain expansion points in s-plane to obtain synthesis-like equation, concerning products of unknown controller numerator and denominator polynomials. This process yields simultaneous linear equations, whose solution provides the coefficients of the product polynomials. In the third stage, the controller parameters are extracted from the product polynomials using exact model matching. The proposed method is illustrated by designing load frequency controller in traditional and restructured power systems under scenarios like system parameter uncertainties, random load variation, and time-varying communication delays. Simulation studies reveal the efficacy of the proposed technique over existing techniques. Furthermore, the practical implementation feasibility of the decentralized controller designed for the restructured power system is validated using the TMS320F28379D controlCARD.

Saturated STA-based sliding-mode tracking control of AUVs: Design, stability analysis, and experiments

In this study, we developed a saturated super-twisting algorithm for autonomous underwater vehicles to address the trajectory tracking problem. On the basis of Lyapunov arguments, we performed a stability analysis of the resulting closed-loop system under the proposed controller, considering the MIMO system dynamics, parametric uncertainties, and external disturbances. Finally, we conducted several real-time experiments to demonstrate the effectiveness and robustness of the proposed control solution as compared to the traditional super-twisting algorithm.

Multi-factors effects analysis of nonlinear vibration of FG-GNPRC membrane using machine learning

Functionally graded graphene nanoplatelet reinforced composite (FG-GNPRC) have exhibited significant potential for the development of high-performance and multifunctional structures. In this paper, we present a machine learning (ML) assisted uncertainty analysis of nonlinear vibration of FG-GNPRC membranes under the influence of multi-factor coupling. Effective medium theory (EMT), Mori-Tanaka (MT) model and rule of mixture are utilized to evaluate the effective material properties of the composite membrane. Governing equations are derived via an energy method with the frameworks of the hyperelastic membrane theory, Neo-Hookean constitutive model and the couple dielectric theory. Randomly generated inputs after data pre-processing are fed into governing equations, which are solved by numerical methods for outputs. Three ML models, including artificial neural network (ANN), support vector regression (SVR) and AutoGluon-Tabular (AGT), are adopted to capture the complex relationship between the systematic inputs (i.e., structural dimensions, attributes of GNPs and pores, external electric field, etc.), frequency ratio and dimensionless amplitude of FG-GNPRC membranes. The results demonstrate that all three ML models demonstrate exceptional computational efficiency, and AGT presents higher prediction accuracy compared to the other two models. Based on the Shapley additive explanations (SHAP) approach, the effects of uncertainties of system parameters and multi-factor coupling on the nonlinear vibration of the FG-GNPRC membrane are analyzed. It is found that the uncertainty of structural parameters has the greatest impact on the nonlinear vibration of FG-GNPRC membranes, particularly when the membrane is subjected to a voltage of 10 V and smaller stretching ratio.

A disturbance rejection control for urban air mobility using artificial sensor-based Gaussian process regression

This paper presents a learning-based disturbance rejection control strategy for Urban Air Mobility (UAM) with vertical take-off and landing capability, which is subject to uncertainties in system parameters. The two primary sources of uncertainty during UAM operation, specifically moment of inertia uncertainty and center of gravity variation, are thoroughly analyzed as they negatively impact control performance. Building upon the analysis outcomes, a novel adaptive scheme is proposed that employs the modeling capabilities of Gaussian process regression for online learning to estimate model uncertainties. To ensure the collection of high-quality training data without relying on state derivatives, a nonlinear disturbance observer is employed as an artificial sensor. The suggested control algorithm is formulated by integrating Gaussian process regression with a baseline control derived using the feedback linearization control technique. Theoretical analysis grounded in the Lyapunov theorem reveals that the tracking error of the closed-loop system is semi-globally uniformly and ultimately bounded. Numerical simulations are conducted to validate the effectiveness of the proposed approach. The results obtained confirm that the proposed method can achieve superior tracking performance, even in the presence of model uncertainties and time-varying disturbances, surpassing existing approaches.

Optimal design of time‐varying parameter fractional order controller using ameliorated gazelle optimization algorithm

AbstractThe model parameter uncertainty and controller gain disturbance of the factory servo system are challenges that affect the robustness and control performance of the system. In this paper, a class of factory servo systems with non‐integer order is studied. The stable boundary trajectory method of the fractional order system is used to determine the parameter stability domain that makes the control system stable. An optimal gain trade‐off design method for time‐varying parameter fractional order PID controller () is proposed. The time function is introduced as the adjustment formula to realize the adaptive adjustment of the controller gain. The Lyapunov theorem analyzes the stability of the method. At the same time, an ameliorated gazelle optimization algorithm (AGOA) is proposed to optimize the parameters of the controller, and the weight relationship is changed to set the objective function to obtain the optimal performance combination after optimization. The benchmark function optimization test is completed. Statistical analysis shows that AGOA can enhance the global search ability, prevent the acquisition of local optimum, and have faster convergence speed. The final simulation results show that the proposed scheme is a promising alternative to improve the system control performance.

Adaptive Active Disturbance Rejection Control for Vehicle Steer-by-Wire under Communication Time Delays

In this paper, an adaptive active disturbance rejection control is newly designed for precise angular steering position tracking of the uncertain and nonlinear SBW system with time delay communications. The proposed adaptive active disturbance rejection control comprises the following two elements: (1) An adaptive extended state observer and (2) an adaptive state error feedback controller. The adaptive extended state observer with adaptive gains is employed for estimating the unmeasured velocity, acceleration, and compound disturbance which consists of system parameter uncertainties, nonlinearities, exterior disturbances, and time delay in which the observer gains are dynamically adjusted based on the estimation error to enhance estimation performances. Based on the accurate estimations of the adaptive extended state observer, the proposed adaptive full state error feedback controller is equipped with variable gains driven by the tracking error to develop control precision. The integration of the advantages of the adaptive extended state observer and the adaptive full state error feedback controller can improve the dynamic transient and static steady-state effectiveness, respectively. To assess the superior performance of the proposed adaptive active disturbance rejection control, a comparative analysis is conducted between the proposed control scheme and the classical active disturbance rejection control in two different cases. It is worth noting that the active disturbance rejection control serves as a benchmark for evaluating the performance of the proposed control approach. The results from the comparison studies executing two simulated cases validate the superiority of the suggested control, in which estimation, tracking response rate, and steering angle precision are greatly improved by the scheme proposed in this article.

Synthesis of Adaptive Sliding Mode Control for Twin Rotor MIMO System with Mass Uncertainty based on Synergetic Control Theory

In this paper, the authors present a new method to synthesize an adaptive sliding controller for Twin Rotor MIMO System (TRMS) based on Synergetic Control Theory (SCT). This system represents a prototype of a helicopter with two degrees of freedom and is widely used in automatic control laboratories. The complexity of the control problem is due to the nonlinear cross-coupling between the main and tail rotors. Uncertainty in system parameters further increases the complexity of the control problem. In Synergetic Control Theory, manifolds are designed for each channel. The control law is found based on sequential manifolds and the Analytical Design of Aggregated Regulators (ADAR) method. The adaptive law when the parameters are uncertain is given based on the analysis of system stability thanks to the Lyapunov function of the first manifold. Finally, the effectiveness of the proposed controller is demonstrated by numerical simulation results and comparison with conventional Sliding Mode Control (SMC).

Dynamics modeling and path following controller of tractor-trailer-wheeled robots considering wheels slip

To improve the payload capacity and maneuverability of a Differentially-Driven Wheeled Robot (DDWR), a wheeled vehicle, which is called trailer, is connected to the DDWR. In all of the previous studies of DDWRs with a trailer, the robot wheels are subject to pure rolling constraints. However, when these multibody systems move with high velocities/accelerations, transfer a heavy payload, or travel on a slippery surface, they experience slipping and/or skidding. In the present study, a Tractor-Trailer Wheeled Mobile Robot (TTWMR) is modeled whose wheels may slip in lateral and longitudinal directions. To this end, Lagrange formalism is employed along with the LuGre friction model to derive dynamics of the considered multibody system. Next, the problem of path following for the trailer is addressed. Toward this goal, the partial feedback linearization technique will be utilized. The obtained simulation results prove the superiority of the performance of the suggested method in comparison to the previous studies. Additionally, the response of the system in the presence of the external disturbances and uncertainties in system parameters will be examined.

Robust EMPC-Based Frequency-Adaptive Grid Voltage Sensorless Control for an LCL-Filtered Grid-Connected Inverter

A robust explicit model predictive control (EMPC)-based frequency-adaptive grid voltage sensorless control is developed for a grid-connected inverter (GCI) via a linear matrix inequality (LMI) approach under the model parametric uncertainties as well as distorted and imbalanced grid voltages. In order to ensure the quality of grid currents injected into the utility grid even when the system model parameters vary, the proposed control scheme is accomplished by an enhanced prediction model rather than the conventional prediction model obtained by fixed parameters. Furthermore, an LMI-based observer is integrated with the disturbance observer to improve the reference tracking performance and to reject disturbances. The proposed observer is employed for the grid frequency-adaptive control without the need for grid voltage sensors. The proposed current controller and observer employ the LMI scheme to maintain a stable and robust operation of the GCI. The discrete-time frequency response and pole-zero map analyses are utilized to examine the system performance including the stability and robustness against parametric uncertainties. Comprehensive simulation and experimental tests as well as theoretical analyses clearly validate the robustness of the proposed control scheme under various harsh test conditions with non-ideal and unexpected grid and system parametric uncertainties.

A nonlinear analysis of a Duffing oscillator with a nonlinear electromagnetic vibration absorber–inerter for concurrent vibration mitigation and energy harvesting

Several investigators have taken advantage of electromagnetic shunt-tuned mass dampers to achieve concurrent vibration mitigation and energy harvesting. For nonlinear structures such as the Duffing oscillator, it has been shown that the novel nonlinear electromagnetic resonant shunt-tuned mass damper inerter (NERS-TMDI) can mitigate vibration and extract energy for a wider range of frequencies and forcing amplitudes when compared to competing technologies. However, nonlinear systems such as the NERS-TMDI are known to exhibit complex stability behavior, which can strongly influence their performance in simultaneous vibration control and energy harvesting. To address this problem, this paper conducts a global stability analysis of the novel NERS-TMDI using three approaches: the multi-parametric recursive continuationWe emphasize that these assume method, Floquet theory, and Lyapunov exponents. A comprehensive parametric analysis is also performed to evaluate the impact of key design parameters on the global stability of the system. The outcome indicates the existence of complex nonlinear behavior, such as detached resonance curves, and the transition of periodic stable solutions to chaotic solutions. Additionally, a parametric study demonstrates that the nonlinear stiffness has a minimal impact on the linear stability of the system but can significantly impact the nonlinear stability performance, while the transducer coefficient has an impact on the linear and nonlinear stability NERS-TMDI. Finally, the global sensitivity analysis is performed relative to system parameters to quantify the impact of uncertainty in system parameters on the dynamics. Overall, our findings show that simultaneous vibration control and energy harvesting come with a considerable instability trade-off that limits the range of operation of the NERS-TMDI.

Optimum Fractional Tilt Based Cascaded Frequency Stabilization with MLC Algorithm for Multi-Microgrid Assimilating Electric Vehicles

An important issue in interconnected microgrids (MGs) is the realization of balance between the generation side and the demand side. Imbalanced generation and load demands lead to security, power quality, and reliability issues. The load frequency control (LFC) is accountable for regulating MG frequency against generation/load disturbances. This paper proposed an optimized fractional order (FO) LFC scheme with cascaded outer and inner control loops. The proposed controller is based on a cascaded one plus tilt derivative (1+TD) in the outer loop and an FO tilt integrator-derivative with a filter (FOTIDF) in the inner loop, forming the cascaded (1+TD/FOTIDF) controller. The proposed 1+TD/FOTIDF achieves better disturbance rejection compared with traditional LFC methods. The proposed 1+TD/FOTIDF scheme is optimally designed using a modified version of the liver cancer optimization algorithm (MLCA). In this paper, a new modified liver cancer optimization algorithm (MLCA) is proposed to overcome the shortcomings of the standard Liver cancer optimization algorithm (LCA), which contains the early convergence to local optima and the debility of its exploration process. The proposed MLCA is based on three improvement mechanisms, including chaotic mutation (CM), quasi-oppositional based learning (QOBL), and the fitness distance balance (FDB). The proposed MLCA method simultaneously adjusts and selects the best 1+TD/FOTIDF parameters to achieve the best control performance of MGs. Obtained results are compared to other designed FOTID, TI/FOTID, and TD/FOTID controllers. Moreover, the contribution of electric vehicles and the high penetration of renewables are considered with power system parameter uncertainty to test the stability of the proposed 1+TD/FOTIDF LFC technique. The obtained results under different possible load/generation disturbance scenarios confirm a superior response and improved performance of the proposed 1+TD/FOTIDF and the proposed MLCA-based optimized LFC controller.

Modelling and cycle‐by‐cycle control of an electromechanical engine valve actuator with impact speed limiter based on hydraulic damper

AbstractVariable valve actuation (VVA) systems based on electromechanical valve actuators (EMVAs) enable a flexible operation and management of internal combustion engines that results in a reduction of pollutant emission and fuel consumption while improving engine power and efficiency. However, to achieve the benefits promised by EMVAs, it is of utmost importance to guarantee the catch of the valve by the system magnets with an acceptable impact speed of the valve at the end‐strokes. Valve catching failures can severely damage engine valves while high impact speeds induce mechanical wear and unacceptable noise. The soft landing control of the valve at the end‐strokes is challenging because of system nonlinearities, parameter uncertainties, and external disturbances. The complexity of the control design increases further when industrial cost‐effective solutions are sought. This paper presents a novel solution for reducing the impact speed of the valve without the use of costly position valve sensors. The proposed solution leverages on a small hydraulic damper (HD) embedded into the EMVA and a feedback cycle‐by‐cycle controller. The passive element reduces the impact speed while the controller adjusts at every engine cycle the EMVA coil currents based on pressure peaks detected in the HD to guarantee valve catching while steering the impact speed toward its minimum. The design of the controller exploits a physics‐based model that maps the pressure peak in the HD onto the impact speed and a gradient descent algorithm is adopted searching the minimum. The closed‐loop permanence is assessed in simulation by using an experimentally validated EMVA model and considering detailed dynamics of the HD. Numerical results show the ability of the proposed control architecture to reduce the impact speed and its robustness to parameters variations (oil temperature) and external disturbances (gas pressure forces).

Heating ventilation air‐conditioner system for multi‐regional commercial buildings based on deep reinforcement learning

AbstractIn an era of significant energy consumption by commercial building HVAC systems, this study introduces a Deep Reinforcement Learning (DRL) approach to optimize these systems in multi‐zone commercial buildings, targeting reduced energy usage and enhanced user comfort. The research begins with the development of an energy consumption model for multi‐zone HVAC systems, considering the complexity and uncertainty of system parameters. This model informs the creation of a novel DRL‐based optimization algorithm, which incorporates multi‐stage training and a multi‐agent attention mechanism, enhancing stability and scalability. Comparative analysis against traditional control methods shows the proposed algorithm's effectiveness in reducing energy consumption while maintaining indoor comfort. The study presents an innovative DRL strategy for energy management in commercial HVAC systems, offering substantial potential for sustainable practices in building management.

Extended state observer design for uncertainty estimation in electronic throttle valve system

AbstractThe Electronic Throttle Valve (ETV) is the core part of automotive engines which are recently used in control-by-wire cars. The estimation of its states and uncertainty is instructive for control applications. This study presents the design of Extended State Observer (ESO) for estimating the states and uncertainties of Electronic Throttle Valve (ETV). Two versions of ESOs have been proposed for estimation: Linear ESO (LESO) and Nonlinear ESO (NESO). The model of ETV is firstly developed and extended in state variable form such that the extended state stands for the uncertainty in system parameters. The design of both structures of ESOs are developed and a comparison study has been conducted to show the effectiveness of the proposed observers. Numerical simulation has been conducted to assess the performance of observers in estimating the states and uncertainties of ETV. The simulated results showed that both full order and reduced order models of ETV have the same transient characteristics. Moreover, the effectiveness of two versions of observers has been examined based on Root Mean Square of Error (RMSE) indicator. The results showed that the NESO has less estimation errors for both states and uncertainties than LESO.

Parameter-Estimation-Based Gain-Scheduling Control of Weight on Bit in Drilling Process With Uncertain Penetration Resistance Coefficient.

It is inevitable to encounter through different formations in the drilling process for deep exploration, and the penetration resistance coefficient (PRC) is an uncertain parameter related to lithology. In this article, a parameter-estimation-based gain-scheduling controller is developed to eliminate undesired system performance deterioration due to the uncertain parameter. First, a drill-string axial finite element model with the uncertain PRC is established, and a control-oriented low-order model is derived via mode selection. A gain-scheduling controller is computed based on the quadratic stability condition of the closed-loop system, which can cope with the system's parameter uncertainty using variable low-frequency gain. An adaptive observer is designed to estimate the unmeasurable scheduling variable. Field data from a geothermal drilling well is obtained to validate our model. According to this drilling well scenario, both numerical and experimental results illustrate the effectiveness of our method. The explicit relationship between the controller gain and the uncertain parameter is presented. It is found that the performance of the closed-loop system is more sensitive to the controller gain when drilling in soft formations, requiring more attention in such scenarios.

Novel SMO-Based Detection and Isolation of False Data Injection Attacks Against Frequency Control Systems

The integration of modern power systems with information and communication technologies exposes them to various cyber threats. Load frequency control (LFC) is a communication-based automation in power systems that regulates the frequency of the grid. Its critical role makes it a highly attractive target for adversaries. This paper proposes a novel detection and isolation method of False Data Injection Attacks (FDIAs) against LFC. The defense method employs sliding mode observation techniques to detect FDIAs against LFC in real-time and discover which parts of the control loop have been compromised. Attacks are identified by comparing the generated residuals with a specific threshold that is designed in an adaptive manner. The proposed method is able to successfully distinguish the FDIAs from other system disturbances and is robust against uncertainties in power system parameters and noisy measurements. The effectiveness and scalability of the proposed defense method are confirmed on realistic power system models, considering nonlinearities, different topologies and diverse types of transmission links.

Research on robust adaptive control of strong nonlinear complex large power grids.

In this paper, a Multi-Index Nonlinear Robust Adaptive Control (MINRAC) method was proposed for ultra-complex multi-input multi-output power systems with uncertain parameter factors and external disturbances. The controller designed by this method has excellent static and dynamic characteristics. Under the condition of the uncertainty of system parameters and external disturbance at the same time, the MINRAC method can ensure that the multiple indexes concerned by ultra-complex power systems can be controlled at their expected values. The simulation results showed that the control mechanism of MINRAC method was consistent in both single-machine infinite bus system and multi-machine interconnected coupling system. The output function chooses power angle, angular frequency and terminal voltage as constraints. When the system has parameter uncertainty and external interference, the uncertain parameter values are adjusted by adaptive control to force these indicators to tend to the given expected value. For three-phase short circuit, which is the most serious fault in power system, the use of multi index nonlinear robust controller can ensure that the system is stable in a wide range and has better dynamic performance.

Observer-Based Nonlinear Proportional–Integral–Integral Speed Control for Servo Drive Applications via Order Reduction Technique

This study designs an advanced single-loop output feedback system for speed servo drive applications, in which a simple proportional–integral–integral (PII) controller equipped with nonlinear feedback and feed-forward gains is formed. The resultant feedback system shows the desired critically damped performance for wide-operating regions by actively handling the system parameter and load uncertainties. There are three contributions: first, the third-order observer estimates, independent from the system model, where the speed and acceleration are obtained using the position measurement with the order reduction property; second, the observer-based PII controller is compensated by active damping with a nonlinearly structured feedback and feed-forward gains; and, third, a guarantee is achieved on the desired critically damped performance through a closed-loop analysis. A hardware testbed that adopts a 500 W brushless DC motor is used to experimentally demonstrate performance improvements over certain constant torque regions under various scenarios.

Efficacy of a class of resonant nonlinear controllers of fractional-order for adaptive vibration control—Analysis, simulations and experiments

The efficacy of different class of resonant controllers has been successfully established for active vibration control. This class of controllers utilize a second-order filter fed back with the structural response (displacement, velocity or acceleration) and the control signal is synthesized as the linear/nonlinear function of the filter response. However, the performance of the controller largely depends on the tuning of the filter frequency to the structural natural frequency. Thus, for structural systems where the natural frequency and the excitation frequency are uncertain, one requires to tune the filter frequency adaptively. The present paper introduces two computationally efficient, fractional-order nonlinear adaptive resonant controllers with acceleration feedback for vibration control. The control signal is synthesized as the linear/nonlinear function of the filter variable and its fractional order derivative. Two parameters, namely the filter frequency and the control gain, are adaptively varied to control the phase and amplitude of the control signal, respectively. The objective of frequency adaptation is to maintain the phase of the control signal in the quadrature of the system displacement/acceleration. Theoretical analysis of the system is performed by the method of multiple scales under the assumption of slowly varying parameters. The results are verified by both numerical simulations and experiments. The theoretical and experimental results demonstrate the robustness of the proposed adaptive control against uncertainties in excitation force and system parameters. It is comprehensively shown that the fractional-order control is superior to the integer-order control already studied in previous works. Moreover, the effect of feedback time-delay is also studied, and it is shown that the fraction-order control can offset the detrimental effects of time-delay on the frequency response of the system.

Terminal sliding mode control based on recursive stochastic configuration network for ball mill lining replacement process

The precise control of multi-joint manipulators presents significant challenges in the context of replacing internal liners of large grinding machines. These challenges arise due to the uncertainty of system parameters and external disturbance. A terminal sliding mode control based on a recursive stochastic configuration network is proposed to ensure that the system is stable, and it reduces system oscillations in this article. In addition, a disturbance observer based on recursive stochastic configuration network is designed to estimate and compensate for the uncertainties in the system, thereby improving the robustness of the system. To enhance the realism of the simulations, the simulation modeling process incorporated uncertainty parameters and external disturbance information. The simulation results demonstrated the effectiveness of the proposed terminal sliding mode control approach in meeting the practical control requirements. This accomplishment presents a valuable new method for controlling mechanical arms. The method is applicable under conditions of system uncertainty and external disturbance.