Control Design For Regulation Research Articles

A fuzzy compensation-Koopman model predictive control design for pressure regulation in proten exchange membrane electrolyzer

Proton exchange membrane (PEM) electrolyzer have attracted increasing attention from the industrial and researchers in recent years due to its excellent hydrogen production performance. Developing accurate models to predict their performance is crucial for promoting and accelerating the design and optimization of electrolysis systems. This work developed a Koopman model predictive control (MPC) method incorporating fuzzy compensation for regulating the anode and cathode pressures in a PEM electrolyzer. A PEM electrolyzer is then built to study pressure control and provide experimental data for the identification of the Koopman linear predictor. The identified linear predictors are used to design the Koopman MPC. In addition, the developed fuzzy compensator can effectively solve the Koopman MPC model mismatch problem. The effectiveness of the proposed method is verified through the hydrogen production process in PEM simulation.

Towards High-Precision Quadrotor Trajectory Following Capabilities: Modelling, Parameter Estimation, and LQR Control

Abstract Quadrotor unmanned aerial vehicles (UAVs) are small, agile four-rotor systems suitable for various applications, from surveillance to disaster support missions. Hence, achieving high-precision trajectory tracking is crucial for their successful deployment. This research focuses on modelling, parameter identification, and Linear Quadratic Regulator (LQR) control design for quadrotors, aiming to enhance their trajectory following capabilities. The quadrotor dynamics are a sixth degree-of-freedom (6DOF) equation of motion derived from Newton’s second law, encompassing moment of inertia, centre of gravity, weight, and thrust of propeller parameters. Experimental measurements are conducted to accurately determine these parameters, ensuring a realistic representation of the quadrotor system. Subsequently, a linearized model is constructed to provide a suitable plant for control system development. The LQR control design is intended to improve the trajectory tracking performance. This control strategy is validated through simulation and practical experiments, demonstrating its effectiveness in achieving high-precision trajectory following capabilites. The proposed approach demonstrates that LQR control effectively guides the quadrotor to resemble a predefined trajectory, experiencing only 3 % overshoot observed during the initial phase of flight.

Design of Multi-Input Multi-Output Non-linear Model Predictive Control for Main Steam Temperature of Super Critical Boiler

Flexible operation of coal-fired power plants is becoming increasingly necessary for successful integration of large-scale renewable power generation into the power grid. The maximum ramp rate and the number of load cycles are generally limited by the thermal stress experienced by the boiler pressure parts, turbine metallurgy and creep and fatigue of critical thick-walled components Main steam temperature is a critical operating parameter that must be controlled within acceptable limits for safe operation. Main steam temperature deviation beyond acceptable limit has impact on boiler pressure parts and turbine material of construction due to creep and fatigue effect. Base load operating units do not require steep ramp rate and hence recommended ramping rates are kept low within the safe operating zone in comparison to the flexible operation of the units with wide range load change width. Thermal stresses are caused by the temperature changes inside the thick-walled components and turbine steam admission parameters. Hence, the quality of main steam temperature control plays a vital role in flexible operation of the coal fired units. Conventional cascaded PID temperature control loop architecture performs well at steady state condition within a limited variation of load change at low ramp rate but it acts slowly and performs poorly at transient operating conditions of flexible operation of the boiler turbine with wide range load variation and load cycle with high ramp rate and remains far from rated conditions. In this paper, a Multi-Input Multi-Output (MIMO) Non-linear Model Predictive Control (MPC) design for regulation of the main steam temperature of a Once-Through supercritical Boiler is proposed. The controller is based on a non-linear dynamic model which incorporates dynamics of the variables of interest. It has the capability to operate effectively across a wide load range while maintaining main steam temperature within acceptable limits. A notable advancement in this design of MPC is the incorporation of coal flow demand and feedwater flow demand as additional control inputs alongside primary and secondary spray flows. In simulation test cases, the MPC controller demonstrates satisfactory performance and computational efficiency.

Intelligent Renewable Energy Agent‐Based Distributed Control Design for Frequency Regulation and Economic Dispatch

The Distributed Renewable Energy Sources (DRESs) integrate hybrid microgrid and prosumer activities that constitute a dynamic system characterized by unknown network parameters. The dynamic system faces challenges, such as intermittent power supply due to low inertia, renewable intermittence, plug‐and‐play prosumer activities, network topology variations, and a lack of constraint handling. These complexities pose significant issues in designing effective control for frequency regulation and consensus‐based economic load dispatch (ELD) within DRES to meet varying load demands. To address the above challenges, this research employs a machine learning‐based distributed multiagent consensus design that offers a rapid and robust approach, mitigating the limitations associated with the Distributed Average Integral (DAI) control design. The proposed multiagent scheme empowers the successful implementation of ELD and frequency regulation, accommodating the intermittent DRES, diverse network topologies, and the dynamic plug‐and‐play activities of prosumers. Moreover, an optimization‐based DAI tuning model is introduced to overcome tuning limitations. Intelligent renewable energy agents are trained through machine learning‐based regression models that use root mean square error metrics for performance evaluations. The intelligent agents employ DAI control to overcome inherent limitations. The effectiveness of the machine learning‐based DAI is thoroughly evaluated using the DRES‐based IEEE 14‐bus hybrid microgrid system. The quantitative results prove its efficacy in addressing the complex challenges of integrated microgrid dynamics.

A robust optimal distributed control design for simultaneous voltage regulation and current sharing in DC microgrid

AbstractIn this article, a robust optimal distributed controller is designed for achieving average voltage regulation and current sharing in a DC microgrid. Each distributed generation unit (DGU) consists of a DC‐DC buck converter that supplies a local constant current load. The proposed controller only utilises output currents of converters and the possible maximum error values in estimation of series resistance in the RLC filter used in each DGU. The proposed controller is formed such that the possible estimation error is considered as an uncertain parameter. In addition to obtaining two main objectives, average voltage regulation, and current sharing, a robust control problem is solved through an optimal control technique to calculate controller parameters such that the stability of the grid is guaranteed. The stability of the closed loop system with parametric uncertainty is proved through Lyapunov analysis. The closed loop system with the suggested controller is tested by simulations and through different scenarios to reveal the reliable and satisfactory performance of the proposed robust optimal control structure.

Vibration Control Using Modern Control System for Hybrid Composite Flexible Robot Manipulator Arm

In this research, a model of a robotic manipulator flexible structure and an equation of motion for controller design is planned. The structural material for the robot structure is chosen as a hybrid composite for this investigation along with a comparison study is carried out for the aluminium 6082 alloy of flexible manipulator arm application. To analyses, the vibration behavior and control implementation by adding joint flexibility in the system is organized. Using simulation algorithm, system parameter calculation is carried out through MATLAB software for vibration amplitude, transient period, steady-state error, and settling time of flexible robotic arm system. In a systematized way of motion equation, flexible robotic deflections are organized via the assumed mode (AM) and Lagrange techniques (LT). The graph analysis of hybrid composite and AL6082 materials with high stiffness coefficients is plotted. These obtained values from the plot are utilized for Linear Quadratic Regulator (LQR) controller design. The LQR output facts for both aluminium structural robotic arm and composite material robotic arms are established.

A novel adaptive neuro linear quadratic regulator (ANLQR) controller design on DC‐DC buck converter

AbstractThis paper proposes an Adaptive Neuro Linear Quadratic Regulator (ANLQR) controller for Buck converter operating under harmful disturbances. Considering the real‐time condition of a converter with regular variations, Neural Network is adopted to improve and tune the gain of the LQR strategy with an adaptive mechanism. This strategy assumes the system as a Gray‐box process without the need for the exact mathematical model of the system which can result in lower computational burden, faster dynamics, and ease of implementation. ANLQR control strategy is a suitable alternative considering its significant robustness against different disturbances, particularly noise. It should be mentioned that the number of neurons is limited to 2 and 4 in each layer to decrease the computational burden with lower complexity. The advantages of this ANLQR method is justified for various operating situations through experimental and simulation outcomes. Moreover, LQR and PSO‐PID controllers are designed and compared with the presented strategy to carry on a comparison with the proposed method. Furthermore, this approach provides better outcomes with faster dynamics and better frequency adaption in the real‐time environments.

Resilient PIDA Control Design Based Frequency Regulation of Interconnected Time-Delayed Microgrid Under Cyber-Attacks

This paper addresses the robust and resilient PIDA control design for frequency regulation of the cyber-physical power system. PIDA control design is carried-out using a recently develop supply demand optimization algorithm (SDOA). This SDOA inspires by the supply-demand mechanism in economics theory. Besides, SDOA is a global optimization algorithm and simple in design. Further, this paper is introduced a new fitness function to obtain the unknown parameters of PIDA controller. From application point of view, the proposed control design is validated on the interconnected time-delayed microgrid system configuration with cyber-attacks. The effectiveness and efficacy of the proposed control design are analyzed under various practical conditions such as cyber-attacks, communication time-delay, parametric uncertainty in system parameters, stochastic load deviation, stochastic wind power deviation, and stochastic solar power deviation. Further, the superiority of SDOA shows in comparison to the grey wolf optimizer (GWO), and whale optimization algorithm (WOA). Finally, it is concluded that the proposed PIDA control design performs better, robust and resilient in comparison to the existing PID control design.

Multi-Rate Sampling Control Design and Stability Analysis for Frequency and Voltage Regulation in Islanded Microgrids

The wide use of inverters in microgrids motivates the development of digital controllers with sampling systems. This paper proposes a multi-rate sampling frequency and voltage regulation method for inverters-based distributed generators (DGs). The fast-sampling signals are adopted to achieve the primary control, which is composed of the VSG-based frequency control and the voltage droop control with a low-pass filter (LPF). Based on the unified primary control model, the low-sampling distributed model predictive control (DMPC) is proposed as secondary frequency and voltage control. Considering the microgrid network, the inverter-based DGs supply power for both the local load and loads on other buses. The proposed DMPC method can overcome the disproportionate impedances of interconnecting lines among buses. Then the frequency and voltage of islanded microgrid can be restored to rated values with accurate power sharing. The DMPC-based secondary control and the primary control have different sampling rates. The stability and dynamic performance of the proposed multi-rate sampling control system are investigated for the first time. Finally, both simulation and experiment tests are carried out to validate the effectiveness of the proposed control strategy.

Efficient PID Control Design for Frequency Regulation in an Independent Microgrid Based on the Hybrid PSO-GSA Algorithm

Microgrids are a part of the power system that consists of one or more units of distributed generation and are expected to remain in operation after being disconnected from the system. Since they rely on overlying networks, frequency control is very important for network-independent operation. Some of the most common problems in independently operating microgrids are frequency sustainability and its fluctuations. The main purpose of this study is to control the frequency of a microgrid in island mode in different scenarios. The objective function is defined based on time and changes in the system frequency. Thus, the variable parameters of the PID controller are transformed into an optimization problem and are solved through the hybrid PSO-GSA algorithm. The study considers four scenarios: (a) a microgrid dynamic model and optimal PID controller coefficients; (b) variable velocity disturbance applied to the studied system in order to observe power changes and the microgrid frequency; (c) stepped load changes applied to the studied system; and (d) the proposed methods on the standard test function. Simulations under different operating conditions are performed, indicating improvements in the stability of microgrid frequency fluctuations by means of the proposed control method.

Extended Kalman filter state estimation-based nonlinear explicit model predictive control design for blood glucose regulation of type 1 diabetic patient.

The computational burden of iterative online optimization-based model predictive control (MPC) process is solved by adapting off-line optimization-based nonlinear explicit model predictive control (NEMPC). In this paper, the application of NEMPC is verified to regulate blood glucose level in type 1 diabetes mellitus (T1DM) patients. The objective of glucose regulation is to avoid hyperglycemia (> 180 mg/dl) and hypoglycemia (< 50 mg/dl) by maintaining glucose level in the range of 70 to 180 mg/dl. It helps to avoid time complexity of iterative process during solution of optimization stage in MPC. In the nonlinear T1DM model, only the state dynamic of sugar is measurable with low complexity and high cost among other states of the model. Therefore, an extended Kalman filter (EKF) is used developed to estimate unavailable states. The information of the estimated states are used to develop the proposed control approach for the T1DM patients. The simulation results of the NEMPC along with EKF-based state estimator for T1DM model shows the regulation of blood glucose-level (BGL) within 70 to 180 mg/dl within 120 min. The robustness of the proposed scheme is also verified under the change in parameters and food disturbance. The control variability grid analysis (CVGA) of NEMPC for 50 numbers of virtual T1DM patients under random parametric changes and meal disturbances shows the avoidance of hypoglycemia and hyperglycemia in type 1 diabetic patients. The proposed control method is simple, robust and efficient to regulate glucose level in T1DM patients. Extended Kalman filter state estimator based nonlinear explicit model predictive control for blood glucose regulation in Type 1 diabetic patients.

Stabilization of Three Links Inverted Pendulum with Cart Based on Genetic LQR Approach

This academic paper demonstrates the implementation of a Linear Quadratic Regulator (LQR) controller design for optimal controlling a three connected links in an inverted pendulum form that attached to a moving cart to realize the stability of making a pendulum in a straight vertical line via translation of the cart left and right. To maintain a triple link inverted pendulum (TLIP) vertical, genetic algorithm has been employed to adjust and tune the parameters of LQR, which are the weighting matrices Q and R instead of the approach of try and error. In this article, a hybrid control algorithm (GA-LQR) proposed to select the optimal values of weighting matrices to overcome LQR design difficulties, which gives the best transient response requirements such as percentage overshoot and steady state error. The triple link inverted pendulum is model mathematically modelled in MATLAB platform to simulate the actual system where the results from the simulation gives acceptable and adequate performance of LQR controller in making the system stable.

The in situ approach to model identification and control design for pressure regulation in Water Distribution Networks: An in silico evaluation

In the context of Water Distribution Networks, service pressure regulation is an important technique that allows reductions in leakage, risk of pipe bursts and mechanical stress to the infrastructure. Recent works demonstrated in silico, i.e. numerically, that linear control systems can be effectively adopted for this task, provided that a careful tuning is performed. Specifically, the tuning should be based on high order, linear models, which describe the system dynamics around a nominal working point. These models can be straightforwardly derived in a simulated environment, but their in situ identification may be challenging due to the presence of non-measurable, exogenous disturbances. This work moves a step forward towards the application of service pressure regulation in situ, by proposing an effective model identification approach for the linear models, based on spectral analysis. The novel approach can cope with exogenous, non-measured disturbances acting during the identification experiments, and considers possible constraints limiting the experimental design. Moreover, the models identified in the in situ conditions are exploited to synthesise linear regulators and assess the closed-loop performances of the overall control methodology. Though being presented and tested in silico, this work assumes a strong practical relevance in view of the results achieved. It in fact demonstrates that novel control schemes, previously designed in nominal conditions only, can be actually designed and implemented in a real scenario, thus making pressure control safer, more reliable and more effective. Finally, the numerical analysis allows for a comparison of both identification and control results with to those obtained in nominal conditions, to provide further insight and stress the reliability of the proposed methodology.

A new technique for right half plane zero elimination from dynamics of a boost converter using magnetic coupling concept

PurposeControl-signal-to-output-voltage transfer function of the conventional boost converter has at least one right-half plane zero (RHPZ) in the continuous conduction mode which can restrict the open-loop bandwidth of the converter. This problem can complicate the control design for the load voltage regulation and conversely, impact on the stability of the closed-loop system. To remove this positive zero and improve the dynamic performance, this paper aims to suggest a novel boost topology with a step-up voltage gain by developing the circuit diagram of a conventional boost converter.Design/methodology/approachUsing a transformer, two different pathways are provided for a classical boost circuit. Hence, the effect of the RHPZ can be easily canceled and the voltage gain can be enhanced which provides conditions for achieving a smaller working duty cycle and reducing the voltage stress of the power switch. Using this technique makes it possible to achieve a good dynamic response compared to the classical boost converter.FindingsThe observations show that the phase margin of the proposed boost converter can be adequately improved, its bandwidth is largely increased, due to its minimum-phase structure through RHPZ cancellation. It is suitable for fast dynamic response applications such as micro-inverters and fuel cells.Originality/valueThe introduced method is analytically studied via determining the state-space model and necessary criteria are obtained to achieve a minimum-phase structure. Practical observations of a constructed prototype for the voltage conversion from 24 V to 100 V and various load conditions are shown.

Design of a switched‐capacitor boost converter utilizing magnetic coupling with capability of right‐half plane zero elimination

The dynamic operation of the conventional boost converters is limited in the continuous conduction mode, due to the presence of at least one right-half plane (RHP) zero in their control transfer function which can limit the open-loop bandwidth of the converter. This problem complicates the control design for the output voltage regulation and conversely influences the closed-loop system stability. To cancel this problem, a new step-up boost converter using the magnetic coupling and the switched capacitor (SC) is presented. In the proposed converter the effect of the RHP zero can be successfully eliminated, the voltage transfer ratio can be enhanced and the circumstances are provided to reach a small operating duty cycle as well as stress reduction of the diodes and power switch. Using the averaging technique, the state-space model of the converter is analytically studied and the essential conditions are given for achieving a minimum-phase structure. Based on the mentioned advantage, a simple classical PI controller for the output voltage regulation is designed. To confirm the theoretical analysis of the proposed converter, simulations and practical results are presented. Also, the performance of the proposed converter is compared with the other boost converters without RHP zero.

Discrete-Inverse Optimal Control Applied to the Ball and Beam Dynamical System: A Passivity-Based Control Approach

This express brief deals with the problem of the state variables regulation in the ball and beam system by applying the discrete-inverse optimal control approach. The ball and beam system model is defined by a set of four-order nonlinear differential equations that are discretized using the forward difference method. The main advantages of using the discrete-inverse optimal control to regulate state variables in dynamic systems are (i) the control input is an optimal signal as it guarantees the minimum of the Hamiltonian function, (ii) the control signal makes the dynamical system passive, and (iii) the control input ensures asymptotic stability in the sense of Lyapunov. Numerical simulations in the MATLAB environment allow demonstrating the effectiveness and robustness of the studied control design for state variables regulation with a wide gamma of dynamic behaviors as a function of the assigned control gains.

Novel algebraic meal disturbance estimation based adaptive robust control design for blood glucose regulation in type 1 diabetes patients.

This study designs a robust closed-loop control algorithm for elevated blood glucose level stabilisation in type 1 diabetic patients. The control algorithm is based on a novel control action resulting from integrating algebraic meal disturbance estimator with back-stepping integral sliding mode control (BISMC) technique. The estimator shows finite time convergence leading to accurate and fast estimation of meal disturbance. Moreover, compensation of the estimated disturbance in controller provides significant reduction in chattering phenomenon, which is inherent drawback of sliding mode control (SMC). The controller is applied to one of the most reliable models of type 1 diabetic patients, named Bergman's minimal model. The effectiveness and superiority of the designed controller is shown by comparing it to classical SMC and super-twisting sliding mode control. The designed controller is subject to three different cases for detailed analysis of the controller's robustness against meal disturbance. The three cases considered are hyperglycaemia, hyperglycaemia combined with meal disturbance and three meal disturbance. The simulation results confirm superior performance of algebraic disturbance estimator based BISMC controller for all the cases mentioned above.

Controller design for MTDC grid to enhance power sharing and stability

Voltage droop control is considered as a reliable control approach for multi-terminal DC (MTDC) grid system. A low value of droop constant may result in a high dynamic variation associated with oscillations for power and DC voltage deviation. Thus, the stability of the MTDC grid is at risk. This study presents a linear quadratic regulator (LQR) control design with an adaptive droop to achieve appropriate power sharing among the converter terminals. A control strategy with the combination of adaptive droop and LQR is designed in this study. The weights of LQR are tuned using the Gramian analysis. The closed-loop stability analysis is carried out using Lyapunov's stability approach. The proposed control scheme improves the closed-loop system dynamics by reducing the overshoots/undershoots and time to reach steady-state condition. In addition, power sharing according to converter rating is appropriately achieved. The performance of the proposed control scheme is validated on five terminal MTDC grid system simulated in MATLAB®.

Robust LPV control design for blood glucose regulation considering daily life factors

In blood glucose control, the daily life factors such as food intake, exercise, stress, fatigue and circadian rhythm that alter the level of blood glucose should be meticulously considered, which requires the design of a robust control approach for dealing with these uncertainties. In this paper, a robust LPV-based controller is designed in order to achieve a predefined level of blood glucose considering the effects of daily life factors. To be sure that the proposed control signal, which shows the insulin dosage, is practical, a filter with optimized parameters is added to limit the lower and upper bounds of the required exogenous insulin. The suggested approach is applied on an in-silico simulated model of a diabetic patient and the results are presented which show the efficacy of the algorithm for insulin dosage determination. The effects of several everyday life scenarios including announced and random unannounced meals and snacks, stress, fatigue, exercise and circadian rhythm are studied through various simulations and the results are presented.

Resilient Control Design for Lateral Motion Regulation of Intelligent Vehicle

This article studies the state-feedback resilient control problem for lateral motion regulation of an intelligent vehicle in the presence of randomly occurring uncertainties. First, the uncertain disturbance in an external yaw moment is compensated by the randomly occurring uncertainty when modeling the vehicle system, and a resilient controller by considering the randomly occurring uncertainty is developed, which is a more general case, but increases complexity of regulation design. Then, the resilient control condition is proposed to guarantee the sideslip angle and yaw rate satisfying the prescribed $\mathcal {H}_{\infty }$ and $\mathcal {L}_2-\mathcal {L}_\infty$ performance indexes. Moreover, $\mathcal {H}_{\infty }$ and $\mathcal {L}_2-\mathcal {L}_\infty$ performance indexes of control outputs are merged into one linear matrix inequality. Finally, an illustrative simulation is given to show the effectiveness of the proposed resilient control design.