Sophisticated Control Strategies Research Articles

Analysis and control of Hall effect thruster using optical emission spectroscopy and artificial neural network

This study presents a proof-of-principle for using optical emission spectroscopy and artificial neural networks for real-time monitoring and control of the operational parameters of a Hall effect thruster: the anode voltage, the anode xenon injection, the discharge current, and the coil current. In that regard, we build an optical database of 26 spectral lines across 6469 operating conditions to train and test the neural network. We then reduced the learning lines from 26 to 15 based on their statistical correlation with the target parameters. After tuning the hyperparameters of the network, the network predicted the thruster’s parameters with notable accuracies: 95% for the anode voltage, 84% for the coil current, and 99% for both the anode flow rate and the discharge current. The estimated uncertainty of predictions, at 3σ, is ±51V for voltage, ±1A for coil current, ±0.15A for discharge current, and ±0.15mgs−1 for anode flow rate. The prediction calculations were within milliseconds and enabled real-time monitoring of the thruster parameters. Therefore, a proportional-integrator-derivative controller (PID) controller was implemented to regulate the anode voltage and flow rate based on the optical emission of the plume. The PID showcased short settling times from 0.1 to 0.4 s and overshoot levels up to 3% of the target value for the voltage and 10% of the target value for the flow rate. These results were for a fixed coil current at 4A. The study showed that changing the coil current may necessitate more sophisticated prediction models and control strategies. Future work will expand the model’s generalizability to different thruster types, propellants, and magnetic field configurations.

Study of parabolic trough collector with crucial analysis on controller and estimator with variable mirror efficiency

Solar Thermal Power (STP) plants are a crucial technology for converting solar energy into electricity. Among the STP, the Parabolic Trough Collector (PTC) is one of the integral parts of STP systems for electrical power generation. Maximizing PTC performance is a challenge due to dynamic and unpredictable solar radiation and environmental disturbances, such as cloud cover and dust accumulation. The optical efficiency parameter of the PTC is critical for calculating the desired heat gain, which is difficult to measure in real-time, necessitating sophisticated control and estimation strategies for optimal heat regulation. This study introduces two control mechanisms for the PTC system and comparing their efficacy. A classical PI controller supplemented by Static Feed-Forward (SFF) control demonstrates improved performance with an optimal transfer function model. In contrast, Nonlinear Model Predictive Control (NMPC) excels in disturbance rejection and setpoint tracking, considering operational constraints. The NMPC controller notably outperforms the PI controller with SFF in performance metrics, with the Integral Time Squared Error (ITSE) decreasing by approximately 99% across case studies. Also, in the case of heat gain, NMPC controller exhibits percentage increases when compared to the PI controller. Furthermore, since the measurement of optical efficiency is essential, but due to difficulty in the measurement for various reasons, the estimator is used as a virtual sensor to estimate those optical efficiency. The unmeasured states and parameters, including optical efficiencies of the PTC, are estimated using Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) techniques, with UKF exhibiting superior accuracy. However, considering the average computation time, EKF is preferred for real-time applications.

Adaptive Current Angle Compensation Control Based on the Difference in Inductance for the Interior PMSM of Vehicles

Achieving good performance in terms of a fast and accurate maximum torque per ampere (MTPA) control method depends on both accurate parameter estimation and a sophisticated control strategy. However, it often requires a complex and long computational process. This paper proposes an efficient control method using the relationship of torque and current angle for maximum torque per ampere control of the interior permanent magnet synchronous motor (IPMSM) for vehicle application. It was found that it is not necessary for the control method to determine the inductance in the d-q axes; the identification of the difference between each axis is enough. Furthermore, this paper presents a simple and effective procedure to estimate the difference in inductance online, where the linear iron loss calculation method is also designed to support the above process. The proposed control method was experimentally validated on a 5 kW prototype by a TMS320F28335 microcontroller and DSPACE synchronized with a personal computer. The results show that the control process has faster and more accurate performance than the conventional method.

Intelligent Control Strategy for Optimal Utilization of Energy in Smart Grid

Amidst the relentless surge in global energy demand, the optimal utilization of intelligent grid energy has emerged as a pivotal avenue for energy conservation, emission reduction, and sustainable development. The deployment of sophisticated control strategies within intelligent grids aims to elevate energy utilization rates and mitigate power losses. Drawing upon an extensive analysis of data, this study delves into the impact and significance of intelligent control strategies in the optimal utilization of smart grid energy. Our data analysis reveals that the implementation of intelligent control strategies enhances the energy utilization rate of smart grids by approximately 25%, significantly mitigating energy waste compared to traditional grids. Furthermore, power losses are effectively curbed and reduced by approximately 18%. This implies that, while fulfilling the same power demand, smart grids can reduce substantial energy losses and conserve vast resources for society. Furthermore, the intelligent control strategy boasts the capability to carry out real-time scheduling and control, tailored to the evolving energy demand and supply scenarios. This approach ensures the stability and reliability of power supply, subsequently elevating both the operational efficiency of the power system and the quality of power service delivered. Ultimately, the intelligent control strategy occupies a crucial position in optimizing the utilization of intelligent grid energy, serving as a catalyst for enhanced efficiency and sustainability within the global energy landscape. Our data analysis unequivocally demonstrates the remarkable impact achieved by sophisticated control strategies. Looking ahead, with the continuous advancement and refinement of technology, smart grids are poised to play an even more significant role in energy optimization, contributing meaningfully towards global energy conservation, emission reduction, and sustainable development.

A review of control strategies for optimized microgrid operations

AbstractMicrogrids (MGs) have emerged as a promising solution for providing reliable and sustainable electricity, particularly in underserved communities and remote areas. Integrating diverse renewable energy sources into the grid has further emphasized the need for effective management and sophisticated control strategies. This review explores the crucial role of control strategies in optimizing MG operations and ensuring efficient utilization of distributed energy resources, storage systems, networks, and loads. To maximize energy source utilization and overall system performance, various control strategies are implemented, including demand response, energy storage management, data management, and generation‐load management. Employing artificial intelligence (AI) and optimization techniques further enhances these strategies, leading to improved energy management and performance in MGs. The review delves into the control strategies and their architectures, and highlights the significant contributions of AI and emerging technologies in advancing MG energy management.

A comprehensive investigation of brushless direct current (bldc) motors: current state, advanced control strategies, and utilization systems

In the field of dynamic applications, specifically within automotive, pumping, and rolling sectors, there exists a noteworthy preference for the use of Brushless Direct Current (BLDC) motors. Projections show that, by the year 2030, BLDC motors are poised to supersede classic induction motors as the dominating force in industrial power transmission. This transformation, however, is accompanied by crucial issues and unresolved research challenges in the environment of BLDC motors.The core concern revolves around the dependability and endurance of BLDC motors. These motors presently encounter obstacles in achieving advanced fault tolerance, reduced electromagnetic interference, lowered acoustic noise, as well as mitigated flux and torque fluctuations. The path of closed-loop vector control emerges as a possible technique to address these challenges.In recent literature studies spanning the previous five years, a striking scarcity of exploration in the domain of BLDC motor controllers and design becomes clear. Furthermore, key areas such as the comparative study of existing vector control schemes, the increase of fault tolerance control, the attenuation of electromagnetic interference inside BLDC motor controllers, and other pivotal elements remain undiscovered. These research lacunae serve as a motivator for the undertaking of an intensive investigation to face the fundamental issues related with BLDC motors. BLDC motors have quickly become the motor of choice for electric vehicle (EV) applications due to the fact that they are reliable, simple, and energy efficient.This detailed survey goes deep into numerous sophisticated control strategies for BLDC motors. These encompass fault tolerance control, electromagnetic interference reduction, field orientation control (FOC), direct torque control (DTC), current shaping, input voltage control, intelligent control, drive-inverter topology, and the underlying operational principles aimed at the minimization of torque irregularities. Additionally, the study goes through the historical narrative of BLDC motors, the categorization of BLDC motor kinds, their structural complexity, mathematical modeling, and the standards that govern BLDC motor uses in varied industries.

Dynamic numerical modeling and performance optimization of solar and wind assisted combined heat and power system coupled with battery storage and sophisticated control framework

Incorporating distributed renewable energy sources into heating, power, and cooling systems is facilitating the drive toward intelligent building energy solutions. However, the inherent uncertainties associated with controllable renewable resources pose challenges for the thermo-economic scheduling of smart buildings. Nonetheless, the flexibility inherent in smart buildings can be leveraged through various market mechanisms. Hence, this research introduces a sustainable energy-building system driven by an autonomous solar/dish Stirling engine (SDSE) and wind turbine combined with a sophisticated control strategy and battery storage, which is designed to provide heat and power requirements. The proposed control strategy is also devised to optimize energy generation, ensuring prudent conservation and minimal waste, thereby amplifying overall efficiency and financial viability. The meticulous design of the system is accomplished through advanced MATLAB/Simulink® modeling. A thorough technical sensitivity analysis meticulously hones design parameters, revealing optimal operational thresholds. Simulation outcomes unveil a consistent Stirling engine (SE) efficiency, achieving a pinnacle of 35 %, while the SDSE attains over 28 %, respectively. The horizontal axis wind turbine, encompassing 100 kW and 500 kW modules, demonstrates power coefficients spanning from 0.18 to 0.09, with corresponding land area requirements ranging from 4.22 m2 to 21.10 m2, emphasizing the pivotal roles of module power and land area optimization. This paper also casts a spotlight on the environmental repercussions of the system, illustrating its potential to avert up to 30,000 kg of CO2 emissions per kWh/year. Moreover, the levelized cost of electricity ranges from 0.13 to 0.16 $/kWh, accompanied by an hourly cost that fluctuates between 3 $/h and 40 $/h, respectively. Conclusively, the developed modeling can be regarded as a significant stride in the realm of hybrid renewable energy systems, replacing the conventional photovoltaic/wind models with cutting-edge solar/wind configurations managed by a sophisticated control strategy and battery storage system.

Design of an Enhanced Dynamic Regulation Controller Considering the State of Charge of Battery Energy Storage Systems

In recent years, the escalating electricity demand in Taiwan has heightened the prominence and discourse surrounding the issue of power supply. With the enactment of the European climate law, global commitment to achieving net-zero emissions has gained momentum. Concurrently, the Taiwanese government has articulated the Taiwan 2050 net-zero emissions policy. To realize this objective, Taiwan has vigorously promoted renewable energy in recent years, increasing the proportion of renewable energy in its energy mix. However, confronted with the intermittent and unpredictable nature of renewable energy generation, challenges arise concerning the stability and quality of power supply. In response to the impact of integrating renewable energy into the grid, the Taiwan Power Company (Taipower) has introduced the day-ahead ancillary service market. Through this platform, power generation and battery energy storage systems (BESSs) engage in competitive bidding, fostering the integration of diverse distributed energy resources into the system. These resources serve as dispatchable assets for Taipower, contributing to grid stability by providing ancillary services. This study has developed an advanced dynamic regulation reserve controller by employing an industrial PC (IPC) in conjunction with a multifunctional power meter. Through real-time measurement of the power grid frequency by the multifunctional power meter, a sophisticated dynamic frequency reserve control strategy has been devised, taking into account the charging state of energy storage systems. This strategy exhibits high operational quality, effectively regulating the charging and discharging of energy storage systems. In addition to swiftly adjusting the current grid frequency, it encompasses the capability to facilitate the transfer of peak electrical energy. This effectively stabilizes the power grid, alleviating supply pressures during nighttime peaks and enhancing the overall supply stability of the power system.

A graphical tuning rule for higher-order systems with H∞ complementary sensitivity function framework

This paper presents a novel approach for designing a decentralized control law for higher-order systems using the [Formula: see text] complementary sensitivity function (CSF) framework. Higher-order systems often exhibit complex dynamics and interactions between the variables, and hence, sophisticated control strategies are required to attain the desired performance. The proposed method exploits the robustness properties of the [Formula: see text] CSF to achieve effective decentralized control for higher-order systems. The CSF approach focuses on shaping the sensitivity of the system to disturbances, thereby minimizing the impact on system performance. The decentralized nature of the control law allows for independent control of each subsystem, reducing the complexity associated with higher-order systems. Furthermore, the controller parameters are determined graphically by enforcing the [Formula: see text] robust criterion. The effectiveness of the proposed methodology is demonstrated through simulations on higher-order systems from various domains, including process control and other systems. Comparative evaluations against existing control methods highlight the superiority of the [Formula: see text] CSF-based decentralized control law in terms of robustness and performance.

Mathematical Modeling Approach to the Optimization of Biomass Storage Park Management

This paper addresses the critical issue of managing biomass parks, a key component in the shift towards sustainable energy sources. The research problem centers on optimizing the management of these parks to enhance production and economic viability. Our aim was to bridge the gap in current research by developing and applying mathematical models tailored for biomass park management. The study commenced by constructing a basic model based on assumptions such as uniform biomass and steady input rates. Progressing from this initial model, we explored sophisticated control strategies, including Pontryagin’s maximum principle and dynamic programming, and employed numerical methods to tackle the nonlinearities and complexities inherent in biomass management. Our approach’s scope extended to predicting and managing biomass flow, highlighting each method’s distinct advantages. The simple model laid the groundwork for understanding, while optimal control techniques revealed the system’s intricate dynamics. The numerical methods provided practical solutions to complex equations. We found that while each method is beneficial on its own, their combined use can significantly improve decision-making in biomass park management. This research emphasizes the importance of aligning the chosen method with specific operational challenges and desired outcomes for optimal efficacy, offering both theoretical insights and practical applications in the field of renewable energy management.

A Coordinated Optimal Operation of a Grid-Connected Wind-Solar Microgrid Incorporating Hybrid Energy Storage Management Systems

The hybrid-energy storage systems (ESSs) are promising eco-friendly power converter devices used in a wide range of applications. However, their insufficient lifespan is one of the key issues by hindering their large-scale commercial application. In order to extend the lifespan of the hybrid-ESSs, the cost functions proposed in this paper include the degradation of the hydrogen devices and the battery. Indeed, this paper aims to develop a sophisticated model predictive control strategy for a grid-connected wind and solar microgrid, which includes a hydrogen-ESS, a battery-ESS, and the interaction with external consumers, e.g., battery/fuel cell electric vehicles. The integrated system requires the management of its energy production in different forms, i.e., the electric and the hydrogen ones. The proposed strategy consists of the economical and operating costs of the hybrid-ESSs, the degradation issues, and the physical and dynamic constraints of the system. The mixed-logic dynamic framework is required to model the operating modes of the hybrid-ESSs and the switches between them. The effectiveness of the controller is analyzed by numerical simulations which are conducted using solar and wind generation profiles of solar panels and wind farms located in Abu Dhabi, United Arab Emirates. Such simulations, indeed, show that the proposed strategy appropriately manages the plant by fulfilling constraints and energy requests while reducing device costs and increasing battery life.

Tillage depth regulation system via depth measurement feedback and composite sliding mode control: A field comparison validation study

The existing methodologies employed for the quantification and regulation of tractor’s tillage depth present considerable shortcomings, primarily characterized by their low accuracy and poor disturbance rejection proficiency in complex agricultural terrains. In this study, we present a sophisticated feedback control strategy designed to mitigate these challenges. Our innovative approach hinges on calculating tillage depth from the alignment of the tractor’s hydraulic lifting arm, achieved by employing a mechanical angle sensor. This sensor adeptly gages the angle of the lifting arm, aligning it with the tillage angle of the pull rod and the implement’s angle, resulting in a robust relational model correlating the lifting arm angle with the tillage depth. This pioneering method amalgamates the accuracy inherent in the static model, derived from the tillage angle-based depth measurement, with the dynamic stability afforded by the mechanical ascertainment of the lifting arm angle. In conjunction, we introduce a Hybrid Extended State Observer-Based Backstepping Sliding Mode Controller (HESO-BacksteppingSMC). The HESO is instrumental in estimating unmeasured state variables and lumped disturbances, utilizing the system’s output feedback signal. Our control frame component capitalizes on the fast power-reaching law to yield a continuously smooth control signal, effectively eradicating the conventional chattering phenomenon inherent in controllers and amplifying its functional applicability. Theoretical evaluations affirm the uniformly and ultimately bounded stability of the errors associated with our proposed observer and controller, underscoring their robustness. The superior performance of our proposed tillage depth measurement and control methodology has been corroborated through a series of comprehensive simulation and field plowing trials, attesting to its precision and reliability in complex agricultural settings.

Software-defined control of an emulated hydrogen energy storage for energy internet ecosystems

The increasing heterogeneity and scalability of the Internet of everything, especially, the Energy Internet (EI), is a prompt for novel engineering paradigms. The current infrastructure of hardware technologies besides the available software stack is becoming obsolete for handling the advanced requirements of the smart energy ecosystem. Accordingly, software definitions of the Internet of Things (IoT) stack functions are gaining interest to provide more flexible and scalable implementations. This paper investigates a sophisticated softwarization explicit hybrid model predictive control strategy for energy storage facility systems through the accessor design pattern. This strategy is presumed to support and facilitate the integration of higher-level analytic and control functionalities within the IoT stack at edges and nodes. Simulation is conducted with the Ptolemy II software to evaluate the execution semantics of the proposed strategy. In addition, real-time experiments on a lab-scale hydraulic tank process with both abrupt and gradual commands test scenarios showed the efficiency and reliability of the accessor-based controllers for achieving the control objectives for the benchmark storage facility with an abstract implementation. The analysis shows that this process can serve as a benchmark to mimic the charge and discharge cycles of storage elements in EI ecosystems.

DESIGNING A PID CONTROL SCHEME FOR DIRECT CURRENT MOTORS WITHOUT USING ELECTRICAL SENSORS

This paper delves into the development of an innovative PID controller for direct current (DC) motors, designed to function independently from mechanical sensors such as encoders. The study emphasizes the importance of confronting this challenge and the pressing need for a viable solution. The proposed method capitalizes on the armature current to estimate the motor's electrical torque, providing a multitude of benefits in tackling this problem. By harnessing this information, a sophisticated control strategy based on pole placement is implemented, enabling precise motor control without reliance on mechanical sensors. The PID control law works in synergy with state feedback to govern the control system's actions, ensuring optimized motor performance and efficiency. To validate the effectiveness of the proposed scheme, a rigorous evaluation process is conducted, encompassing both simulation and real-world testing on a prototype integrated with an ESP32 microcontroller. The findings reveal that the motor's speed demonstrates asymptotically stable behavior in response to fluctuations in load torque and reference speed values, corroborating the efficacy of the proposed solution. Furthermore, the elimination of mechanical sensors simplifies the system's design and minimizes potential failure points, consequently bolstering overall reliability.

Improved control strategies for the environment within cell culture bioreactors

This paper describes the development of improved control strategies for the standard environmental conditions in a fed-batch bioreactor used for monoclonal antibody cell culture. The consequences of relying on fixed parameter PID based controllers are considered and poor performance is demonstrated as a consequence of non-linearity and loop interactions. The benefits from adopting a more sophisticated control strategy are considered. Model Predictive Control (MPC) relies on a process model that can be identified from small system perturbations. It considers the predicted longer-term response and consequently can deliver improved control and satisfy user defined constraints. Results from experimental trials demonstrate the capability of MPC and the merits are discussed with regards to industrial application.

Coupled fluid-structure simulations and experiments of DBD plasma actuator for damping of plate vibrations

In the present work, an experimental and numerical study has been performed to evaluate the suitability of the dielectric barrier discharge plasma actuators to reduce free vibrations of plates. Two configurations composed of two different plexiglass plates and identical actuators were manufactured. Experimental campaigns, in terms of laser doppler anemometer velocity measurements, flow visualization and proper orthogonal decomposition were carried out to characterize the plasma actuator and to validate the numerical models. The plasma-induced virtual body force for the numerical analysis has been calibrated based on the experimental body force and the induced flow field. Then numerical studies of the plasma effect on the flow around an oscillating plate was performed. An experimental modal analysis has been also performed to understand the vibration characteristics of the plate and to support the numerical study. A reduced-order model of the system, based on the harmonic oscillator, was developed to properly design an open loop controller (system natural frequency, amplitude and timing of the attenuation force). Eventually, the work demonstrates that the control strategy based on the plasma actuation is effective in reducing the plate vibrations. However, a deeper study and a more sophisticated control strategy would be required to significantly mitigate the flutter oscillations of airfoils in real applications.

Iterative Dynamic Programming—An Efficient Method for the Validation of Power Flow Control Strategies

The operation of electrical networks, microgrids, or heterogeneous battery systems, especially the dispatch of single units within the system, requires sophisticated power flow control strategies. If objectives such as efficiency are demanded for the operation of the energy system, typical control strategies lack the ability to verify the optimality of the operation. Dynamic programming is a widely used method for determining the global optima of trajectory problems. In the context of energy systems and power flow optimization, it is restricted to applications with a low number of states and decisions. The reason for this is the rapid growth of computational effort with increasing dimensionality of the state and decision space. The approach of iterative dynamic programming (iDP) makes dynamic programming applicable to the planning and benchmarking of complex power flow optimization problems. To illustrate this, a heterogeneous battery energy storage system is introduced for which the iDP optimizes the power split at the point of common coupling to minimize the total cumulative loss of energy. The method can be adopted for a broad range of energy systems such as microgrids, utility grids, or electric vehicles. The applicability is limited only by the computation time, which depends on the model complexity and the length of the time series. To verify the functionality of the iterative dynamic programming, its results are directly compared to those of the standard dynamic programming. The total computation time can be reduced by 98% in the tested scenario. As relevant use cases, static and dynamic methods of power sharing are validated and benchmarked. The iDP offers a novel and computationally efficient method for the design and validation of power flow control strategies.

Critical Assessment of Control Strategies for Industrial Systems with Input–Output Constraints

Safety and economic considerations pose constraints on inputs and outputs of industrial processes. A conventional feedback control system is not equipped to handle these constraints, and hence, industrial practitioners resort to the addition of ad hoc elements such as selector blocks, in conjunction with more sophisticated control strategies such as split range control. However, this approach lacks an organized and prudent methodology and is usually carried out with the aid of experiential knowhow of plant operators and control engineers. Some recent developments in the literature address this issue, however simultaneously alluding to model predictive control (MPC) as a more natural approach for such systems. In this work, we evaluate the efficacy of the aforementioned approach of simple ad hoc additions to accommodate constraints in a process. To this end, with the aid of a continuously stirred tank reactor model, we illustrate the ease of deployment of selector blocks for handling constraints in simple systems and assess their limitations as the system becomes more complex. Next, we consider a heat exchanger employed in a South-East Asian processing facility where the performance of the currently used selector-based architecture is deemed unsatisfactory by the operators. The current control structure was evaluated for conformity with the approaches suggested in the recent literature and was found to be sound; hence, the unsatisfactory performance stems from the inherent limitations of the proportional integral control structure and/or improper tuning. The implementation of MPC on the exchanger system leads to performance amelioration and enhanced robustness.

Estimation of the interaction force between human and passive lower limb exoskeleton device during level ground walking

To develop sophisticated and efficient control strategies for exoskeleton devices, acquiring the information of interaction forces between the wearer and the wearable device is essential. However, obtaining the interaction force via conventional methods, such as direct measurement using force sensors, is problematic. This paper proposes a kinematic data-based estimation method to evaluate the interaction force between human lower limbs and passive exoskeleton links during level ground walking. Unlike conventional methods, the proposed method requires no force sensors and is computationally cheaper to obtain the calculation results. To obtain more accurate kinematic data, a marker refinement algorithm based on bilevel optimization framework is adopted. The interaction force is evaluated by a spring model, which is used to imitate the binding behavior between human limbs and the exoskeleton links. The deflection of the spring model is calculated based on the assumption that the phase delay between human limb and exoskeleton link can be presented by the sequence of frames of kinematic data. Experimental results of six subjects indicate that our proposed method can estimate the interaction forces during level ground walking. Moreover, a case study of bandage location optimization is conducted to demonstrate the usefulness of obtaining the interaction information.

Active flow control of a wing section in stall flutter by dielectric barrier discharge plasma actuators

This paper investigates the potential of using an active flow control technique to modify stall flutter oscillations of a NACA (National Advisory Committee for Aeronautics) 0015 wing section. Wind tunnel experiments have been performed with a test-rig that provides the elastic degree of freedom in pitch. Measurements of the clean airfoil are taken at preset angles of θ0=6°−12°, and for Reynolds numbers of Rec=6.2×104−1.25×105, which reveal the dependency of the stall flutter oscillations to Rec and θ0. Then, flow control experiments are carried out at θ0=10° and Rec=1.04×105. Two dielectric barrier discharge plasma actuators have been employed simultaneously to exert dual-point excitation to the baseline flow. It is shown that during the upstroke half-cycle, plasma actuation postpones the dynamic stall of the airfoil and increases the maximum pitch angle of the stall flutter cycle. On the downstroke, dual-point excitation effectively improves the rate of pitching moment recovery and leads to flow reattachment at a larger pitch angle. Normalized excitation frequencies Fex=fex/fw (where fw is the wake mode frequency of the stalled airfoil) ranging from 0.1 up to 3 have been examined. Among the controlled cases, excitation with Fex=0.6 and Fex=2.2 provides the largest and smallest pitch amplitude, respectively, and the case of Fex=3 demonstrates the most impact on flow reattachment. Finally, it has been concluded that the employed control strategy is effectively capable of modifying the dynamic stall process and associated pitching moment. However, a more sophisticated control strategy would be required to significantly mitigate the stall flutter oscillations.