Robust Control Scheme Research Articles

A two-stage distributed robust optimal control strategy for energy collaboration in multi-regional integrated energy systems based on cooperative game

In view of the weak energy supply stability of the single regional integrated energy system (RIES), the ability to deal with uncertainties is poor. To solve the problem of equilibrium optimization among system economy, robustness and efficiency, this paper interconnects multiple RIES with different resource to form multi-RIES through peer-to-peer energy trading. The control strategy of energy interactive operation involving multiple RIES is optimized. Using the uncertainty confidence set of renewable energy to depict the uncertainty on the source side, a two-stage distributed robust optimal control model for RIES based on data-driven is proposed, and the column and constraint generation (C&CG) algorithm is adopted to significantly improve the solution efficiency. Then, a multi-RIES cooperative game model with uncertain scenarios is proposed, and according to this model, a coupled C&CG-alternating direction multiplier method (ADMM) algorithm is proposed, which realizes the privacy protection of RIES and makes the system have good convergence performance. Finally, the case analysis proves that the strategy proposed in this paper can not only realize energy coordination and complementarity among multiple RIES, but also improve the stability of the system operation and significantly reduce the dependence of the system on the distribution network.

A variable-order fractional memristor neural network: Secure image encryption and synchronization via a smooth and robust control approach

In this research, we introduce and investigate a variable-order fractional memristor neural network, focusing on its engineering applications in synchronization and image encryption. This study stands out as a pioneering effort in proposing such an architecture for image encryption purposes. Distinct from conventional fractional-order systems, our model incorporates a time-varying fractional derivative, leading to more complex behaviors. Through numerical simulations, we vividly demonstrate the chaotic dynamics of the system. Our results further reveal the system's outstanding performance in image encryption applications. To augment the system's efficiency, we introduce a robust control strategy that guarantees smooth stabilization and synchronization of the variable-order fractional system. Considering the unique variable-order fractional nature of the system, we provide theoretical validations and empirical evidence supporting its stability and convergence properties. Additionally, we present synchronization outcomes between pairs of such neural networks employing our robust control approach. Our numerical analyses firmly substantiate the superiority of our control strategy, particularly highlighting its precision, robustness, and ability to maintain chattering-free performance under external disturbances.

IMC based robust and innovative control strategies for higher-order DC-DC converter in DC microgrid applications

The extensive practice of DC-DC converter interfacing circuits to maintain DC link voltage in DC microgrid applications introduces unavoidable voltage regulation problems due to its nonlinearity. These unregulated voltages demean the performance and result in an unstable system. This brief proposes an internal model control (IMC) non-linear approach based robust proportional-integral-lead (PI-Lead) controller design for regulating a practical fifth-order DC-DC boost converter. The main innovation of this paper is the developed procedure for designing controller. The prominent feature of IMC filter coefficient is to fulfil the desired level of gain margin and phase margin to show the trade-off between performance and robustness. The proposed controller's performance is evaluated in the presence of uncertain disruptions and its effectiveness is verified by comparing it with the some conventional proportional-integral (PI) as well as with recently published IMC-proportional-integral-derivative (IMC-PID) and IMC-proportional integral derivative double derivative (IMC-PIDD2) controllers. The applicability of the proposed control methodology has been validated under varying supply and load current levels in presence of parametric uncertainty using a MATLAB/Simulink tool along with a low-cost microcontroller DSP F280379D based laboratory prototype.

Adaptive neural sliding mode control of an uncertain permanent magnet linear motor system with unknown input backlash in laser processing

In this article, a novel robust and high-precision control scheme is developed for a permanent magnet linear motor system in large-range laser processing subject to unknown system uncertainties and input backlash. First, to enhance the robustness of the controller, an adaptive neural sliding mode control (SMC) scheme is proposed. Besides, to address the system uncertainties, a novel simplified radial basis neural network (RBFNN) is applied in the adaptive SMC scheme. Subsequently, considering that the unexpected chattering introduced by SMC strategy, another RBFNN is employed to approximate the optimal switching gain. In addition, considering that the nonlinear input backlash caused by actuator can also cause the fluctuation of the input signal, an inverse backlash model with the method of reverse compensation is proposed to further minimize the effect of the input backlash on control accuracy. Finally, simulation results, validation experiments, and laser processing experiments are provided to demonstrate that the proposed control scheme significantly reduces the impact of chattering and input backlash. Additionally, it exhibits outstanding control accuracy.

A novel RMPC strategy for three-phase inverters operating in grid-connected and standalone modes

One of the main features of microgrids is the capability of operating in both grid-connected (GC) and standalone (SA) modes. This paper presents a novel dual mode robust model predictive control (RMPC) strategy for a three-phase inverter with an LCL filter in both GC and SA operating modes under the filter’s parameter uncertainty. At first, a disturbance observer gain is obtained by solving a linear matrix inequality (LMI), which is determined to preserve the stability of the algorithm. The designed disturbance observer takes into account the polytopic uncertainty of system parameters. A performance index with two weighting matrices is then defined and solved in an infinite horizon by turning it into an optimization problem under LMI constraints. The performance of the control strategy highly depends on the weighting matrices. Hence, an optimization algorithm is formulated to ascertain the best matrices values for both GC and SA operation modes. Given the nonlinearity issue, particle swarm optimization (PSO) is employed to derive the optimal weighting matrices offline. To evaluate the proposed control strategy’s effectiveness, simulations and experiments are performed under several scenarios in both GC and SA operating modes. The results reveal the proposed control strategy’s powerfulness compared to other techniques in the presence of grid voltage and load current disturbances for both inverter operating modes.

Robust fault detection and adaptive fixed-time fault-tolerant control for quadrotor UAVs

This note scrutinizes an adaptive fault-tolerant control (FTC) approach tailored for unmanned aerial vehicles (UAVs), addressing the critical need for both fault accommodation and disturbance suppression. Departing from traditional reliance on robust discontinuous control strategies prone to chattering and demanding precise uncertainty bounds, our FTC method ensures fixed-time stability, guaranteeing the convergence of attitude tracking errors to zero. Central to our approach is an adaptive algorithm adept at concurrently estimating unknown actuator faults and upper bounds of lumped uncertainties. Moreover, our adaptive schemes accurately estimate the upper bound of the lumped uncertainty term, encompassing model uncertainties, external disturbances, and unmodeled dynamics, thereby eliminating the need for assuming known bounds on uncertainties. Stability analysis under the developed control law is thoroughly performed using the Lyapunov stability theory. Notably, our strategy employs an extended Kalman filter (EKF) observer for state estimation and fault detection, facilitating fault detection through an adaptive threshold technique dynamically adjusted based on real-time mean and variance of the residual signal. Through comprehensive simulation and experimental validations, our proposed methodology demonstrates significant advancements in ensuring safety and reliability in UAVs.

Robust fault-tolerant preview control for automatic landing of carrier-based aircraft

PurposeThis paper aims to propose the automatic carrier landing system with the fault-tolerant ability for carrier-based aircraft in the presence of deck motion, external airwake disturbance and actuator fault, which consists of the reference trajectory generation module and flight control module.Design/methodology/approachThe longitudinal and lateral basic controllers are designed based on the optimal preview control (OPC), which can ensure favorable tracking performance and anti-disturbance ability of system. Furthermore, based on the OPC, the robust fault-tolerant preview control scheme is proposed to attenuate the impact of actuator fault on system, which ensures the safe landing of carrier-based aircraft in case of actuator failure.FindingsBoth the Lyapunov method and simulations prove that the tracking errors can converge to zero and system states can be asymptotically stable both in normal and fault operations.Originality/valueThe fault-tolerant control strategy is introduced into preview control to deal with actuator fault, which combines feedforward control based on future previewable information and feedback control based on current information to improve the system performance.

Fully actuated system approach to robust control of uncertain multi‐order sub‐fully actuated systems

AbstractMotivated by the fully actuated system (FAS) approach, this paper proposes a direct robust control method for a type of uncertain multi‐order sub‐fully actuated systems (MOSFASs). Unlike numerous previous results, this paper broadens to a more general multi‐order model and the more challenging sub‐fully actuated case with the presence of singular problems. Firstly, a general uncertain MOSFAS model is presented, where the uncertainty merely needs to satisfy a common assumption. Secondly, a direct robust control method is proposed, giving a robust controller which only needs to satisfy very loose and basic requirements to ensure that the states ultimately converge into an arbitrarily small region. Simultaneously, by constraining the initial values of the states, it is perfectly guaranteed that the states always remain in a “safe” zone free of singular points, thus preserving the realizability of the controller. Finally, under certain conditions, a less conservative robust control scheme is introduced, which can relax the constraint of initial values easily. A simulation for a coarse‐fine angle system demonstrates the validity and value of the proposed robust control method.

Combined Control for a Piezoelectric Actuator Using a Feed-Forward Neural Network and Feedback Integral Fast Terminal Sliding Mode Control.

In recent years, there has been significant interest in incorporating micro-actuators into industrial environments; this interest is driven by advancements in fabrication methods. Piezoelectric actuators (PEAs) have emerged as vital components in various applications that require precise control and manipulation of mechanical systems. These actuators play a crucial role in the micro-positioning systems utilized in nanotechnology, microscopy, and semiconductor manufacturing; they enable extremely fine movements and adjustments and contribute to vibration control systems. More specifically, they are frequently used in precision positioning systems for optical components, mirrors, and lenses, and they enhance the accuracy of laser systems, telescopes, and image stabilization devices. Despite their numerous advantages, PEAs exhibit complex dynamics characterized by phenomena such as hysteresis, which can significantly impact accuracy and performance. The characterization of these non-linearities remains a challenge for PEA modeling. Recurrent artificial neural networks (ANNs) may simplify the modeling of the hysteresis dynamics for feed-forward compensation. To address these challenges, robust control strategies such as integral fast terminal sliding mode control (IFTSMC) have been proposed. Unlike traditional fast terminal sliding mode control methods, IFTSMC includes integral action to minimize steady-state errors, improving the tracking accuracy and disturbance rejection capabilities. However, accurate modeling of the non-linear dynamics of PEAs remains a challenge. In this study, we propose an ANN-based IFTSMC controller to address this issue and to enhance the precision and reliability of PEA positioning systems. We implement and validate the proposed controller in a real-time setup and compare its performance with that of a PID controller. The results obtained from real PEA experiments demonstrate the stability of the novel control structure, as corroborated by the theoretical analysis. Furthermore, experimental validation reveals a notable reduction in error compared to the PID controller.

Design and Implementation of Model Parameter Independent Robust Current Control Scheme of Three-Phase Inverter - A Neural Network-Based Classification Approach

Design and Implementation of Model Parameter Independent Robust Current Control Scheme of Three-Phase Inverter - A Neural Network-Based Classification Approach

Two-Stage Robust Voltage Control Strategy for Seaport Microgrids With Health-Aware All-Electric Ships

Two-Stage Robust Voltage Control Strategy for Seaport Microgrids With Health-Aware All-Electric Ships

Robust PWM control scheme for switched-capacitor MLI with leakage current suppression in grid-connected renewable energy application

Typically, parasitic capacitances exist between the ground and the solar panel terminals in grid-connected PV systems. These parasitic capacitances provide a path for a leakage current, which leads to significant safety concerns, observable and seriously hazardous harmonic orders aligned with the injected grid current, and significant safety difficulties. In this research, a robust PWM controlling method that used competently in reducing the level of the leakage current and improving the power quality of a switched-capacitor Multilevel Inverter. This technique creates developed reference signals from the main signal to generate the switching scheme for the converter circuit. Additionally, the suggested control strategy only works with a small number of carrier signals, resulting in a quick system response and a simpler controller algorithm. Likewise, this controlling approach offers a stable way to maintain a constant output voltage in the suggested converter by adjusting the switching capacitors' voltages, which is not possible with traditional control techniques. MATLAB/Simulink is used to simulate the outcomes for both the suggested control approach and the traditional Phase Disposition (PD) PWM control method whereas the leakage current component reduces to 25 % compared to the captured component with the PDPWM. The simulation and the practical results based on the dSPACE-1103 hardware are quite similar.

Coordinated torque control for enhanced steering and stability of independently driven mobile robots

PurposeMobile robots with independent wheel control face challenges in steering precision, motion stability and robustness across various wheel and steering system types. This paper aims to propose a coordinated torque distribution control approach that compensates for tracking deviations using the longitudinal moment generated by active steering.Design/methodology/approachBuilding upon a two-degree-of-freedom robot model, an adaptive robust controller is used to compute the total longitudinal moment, while the robot actuator is regulated based on the difference between autonomous steering and the longitudinal moment. An adaptive robust control scheme is developed to achieve accurate and stable generation of the desired total moment value. Furthermore, quadratic programming is used for torque allocation, optimizing maneuverability and tracking precision by considering the robot’s dynamic model, tire load rate and maximum motor torque output.FindingsComparative evaluations with autonomous steering Ackermann speed control and the average torque method validate the superior performance of the proposed control strategy, demonstrating improved tracking accuracy and robot stability under diverse driving conditions.Research limitations/implicationsWhen designing adaptive algorithms, using models with higher degrees of freedom can enhance accuracy. Furthermore, incorporating additional objective functions in moment distribution can be explored to enhance adaptability, particularly in extreme environments.Originality/valueBy combining this method with the path-tracking algorithm, the robot’s structural path-tracking capabilities and ability to navigate a variety of difficult terrains can be optimized and improved.

A generalized Udwadia–Kalaba control design for uncertain belt conveyor systems with inequality constraints

Uncertainty can lead to jitter or overshoot in mechanical systems, necessitating the design of multiple constraints to stabilize them. This paper proposes a control structure based on the generalized Udwadia–Kalaba equation to address these constraints simultaneously. An uncertain dynamical model is developed, incorporating both equality and inequality constraints. By integrating diffeomorphism theory, a robust control strategy is designed to ensure compliance with these constraints. Utilizing the Lyapunov approach, the uniform boundedness and uniform ultimate boundedness of the dynamical system are demonstrated. Finally, the feasibility of the proposed control method is validated through its application to a belt conveyor system.

Constrained trajectory optimization and force control for UAVs with universal jamming grippers

This study presents a novel framework that integrates the universal jamming gripper (UG) with unmanned aerial vehicles (UAVs) to enable automated grasping with no human operator in the loop. Grounded in the principles of granular jamming, the UG exhibits remarkable adaptability and proficiency, navigating the complexities of soft aerial grasping with enhanced robustness and versatility. Central to this integration is a uniquely formulated constrained trajectory optimization using model predictive control, coupled with a robust force control strategy, increasing the level of automation and operational reliability in aerial grasping. This control structure, while simple, is a powerful tool for various applications, ranging from material handling to disaster response, and marks an advancement toward genuine autonomy in aerial manipulation tasks. The key contribution of this research is the combination of a UG with a suitable control strategy, that can be kept relatively straightforward thanks to the mechanical intelligence built into the UG. The algorithm is validated through numerical simulations and virtual experiments.

Robust control strategies applicable to DC–DC converter with reliability assessment: A review

AbstractThis paper provides an overview of various control strategies for DC–DC converters along with a brief discussion on various performance indices for evaluating the reliability of designed controller. DC–DC converters are emerging as the fastest‐growing interfacing devices in the world because of their emergent applications in almost all domains of engineering. Notably, the non‐linear behavior of DC–DC converters offers a perplexing problem in designing a robust controller. A robust controller must ensure proper closed‐loop stability with desired performance and reliable control for output voltage regulation in the event of various possible perturbations. This creates the requirement for practically implementable non‐linear controllers that can effectively overlook the drawbacks of linear controllers. The prominence of this composition is based on the expansion in tuning techniques of proportional‐integral‐derivative controller gain parameters using linear strategies and heading towards non‐linear strategies by reviewing 196 research papers. The modification in non‐linear control strategies in terms of their fundamental features associated with key benefits and limitations are comprehensively reviewed. This study mostly revolves around non‐linear internal model control (IMC) strategies for parameter tuning of IMC‐based controllers for various order of plants with an improved degree of freedom.

Nonlinear Robust Control of Vehicle Stabilization System with Uncertainty Based on Neural Network

To effectively suppress the effects of uncertainties including unmodeled dynamics and external disturbances in the vehicle stabilization system, a nonlinear robust control strategy based on a multilayer neural network is proposed in this paper. First, the mechanical and electrical coupling dynamics model of the vehicle stabilization system, considering model uncertainty and actuator dynamics, is refined. Second, the lumped uncertainty of the vehicle stabilization system is estimated by a multi-layer neural network and compensated by feedforward control. The high robustness of the system is ensured by constructing the sliding mode feedback control law. The proposed control method overcomes the limitations of sliding mode technology and the neural network and is naturally applied to the vehicle stabilization system, avoiding the adverse effects of high-gain feedback. Based on Lyapunov theory, it is demonstrated that the proposed controller is able to achieve the desired stability tracking performance. Finally, the effectiveness of the proposed control strategy is verified by co-simulation and comparative experiments.

Small Signal Modelling and Stability Analysis of a Grid Connected Inverted Based Microgrid

Objectives: This work focuses on the stability analysis of grid connected microgrids. It considers the impact of load disturbance and grid voltage change on voltage and current levels, as well as reactive and active power responses, is analysed. Methods: A comprehensive small-signal state-space model is developed for an inverter-based microgrid, incorporating submodules of inverters, phase-locked loops (PLLs), and LCL filters. The model is linearized around a stable operating point, and eigenvalue analysis is performed and validated through MATLAB/Simulink simulations. A current controller operating in the d-q frame is proposed to enhance stable power conversion and maintain microgrid stability. Findings: The proposed model and control strategy demonstrate the microgrid's ability to maintain transient voltage stability under severe dynamic conditions. During a 10% grid voltage fluctuation, the microgrid exhibits stable active and reactive power responses, with currents and voltages at the point of common coupling stabilizing within 0.2 seconds. Furthermore, when a 25 kVA active load is disconnected, the microgrid effectively manages the power transition, maintaining stable operation with minimal deviations in key parameters. The current controller simplifies AC current control, integrating active power management from solar input, DC-link voltage stability, and reactive power control. Novelty: The novelty lies in the comprehensive analysis of transient voltage stability in grid-connected microgrids under grid voltage fluctuations and load disturbances, areas that have received limited attention in previous research. By developing a detailed small-signal state-space model incorporating PLL and LCL filter dynamics and proposing a robust control strategy with the current controller, this study offers new insights into enhancing the resilience and reliability of grid-connected microgrids during transient events. Keywords: Microgrid, Small Signal Stability, Voltage Source Inverter, State Space model, Eigen Values

Robust predictive control strategy of SCIG-based drive system

This article presents a robust finite control set predictive scheme for a stand-alone squirrel cage induction generator (SCIG) drive. This technique is considered an alternative to the drive system due to the inclusion of system nonlinearities and fast dynamic response. The control objective in the distributed generation environment is to fix the output voltage to follow the stand-alone requirement. The strategy establishes optimized switching instants for cost function minimization for both source and load converter control and diminished cross-coupling amid active and reactive power during transient scenarios. The scheme is designed to achieve the minimal effect caused by the parameter uncertainties. During source and load changes, this work will also address the maintenance of dc-link voltage, machine, and load variables at the set value, supported by machine and load-end converter control to achieve stand-alone load objectives. In addition, the presented scheme is also tested with random variation of speed to check the efficacy of the control configuration. The drive performance is evaluated by simulation using MATLAB/Simulink environment. Comprehensive real-time findings obtained from a scaled laboratory test bench using dSPACE-1104 are provided to verify the feasibility of the predictive solution.

Variational quantum circuit learning-enabled robust optimization for AI data center energy control and decarbonization

As the demand for artificial intelligence (AI) models and applications continues to grow, data centers that handle AI workloads are experiencing a rise in energy consumption and associated carbon footprint. This work proposes a variational quantum computing-based robust optimization (VQC-RO) framework for control and energy management in large-scale data centers to address the computational challenges and overcome limitations of conventional model-based and model-free strategies. The VQC-RO framework integrates variational quantum circuits (VQCs) with classical optimization to enable efficient and uncertainty-aware control of energy systems in AI data centers. Quantum algorithms executed on noisy intermediate-scale quantum (NISQ) devices are used for value function estimation trained with Q-learning, leading to the formulation of a robust optimization problem with uncertain coefficients. The quantum computing-based robust control strategy is designed to address uncertainties associated with weather conditions and renewable energy generation while optimizing energy consumption in AI data centers. This work also outlines the computational experiments conducted at various AI data center locations in the United States to analyze the reduction in power consumption and carbon emission levels associated with the proposed quantum computing-based robust control framework. This work contributes a novel approach to energy-efficient and sustainable data center operation, promising to reduce carbon emissions and energy consumption in large-scale data centers handling AI workloads by 9.8 % and 12.5 %, respectively.