Proposed Control System Research Articles

Feedback control system design for force-sensing concentric tube-based surgical robot: Kinematics using Bernstein polynomials

A novel force sensing module integrated into a concentric tube robot (CTR) was proposed for vitreoretinal surgery in our previous work, and this robot was able to provide tip force information to the operating surgeon. However, when the force-sensing module was integrated, the property of the tube changed, and the conventional modeling-based control algorithm proved to be unsuitable for the force-sensing concentric tube robot (FSCTR). In this work, we have proposed a shape optimization algorithm for the FSCTR to prevent snapping issue while providing an adequate workspace. A corollary actuation system was used to control the FSCTR. The forward kinematics of the FSCTR consisted of rigid body motion and shape variation, where the shape variation was determined using Bernstein polynomials. Inverse kinematics was derived using the Newton–Raphson method. A feedback controller was designed to compensate for the error caused by rigid body motion. Several experiments were conducted to evaluate the performance of the proposed control system. The experimental accuracy of the proposed forward kinematics was 0.15 ± 0.16 mm, and this result was superior to that of the torsionally compliant model. The accuracy of the inverse kinematics was 0.55 ± 0.24 mm, and the accuracy of point targeting under feedback control was 0.12 ± 0.05 mm. Overall, the results of this study indicate that the proposed motion control system satisfies the standards required for vitreoretinal surgery.

Optimized power flow control for PV with hybrid energy storage system HESS in low voltage DC microgrid

Due to the intermittent nature of solar irradiation, it is inevitable to integrate the system of energy storage in the PV standalone system. In this paper, Energy Storage System ESS composed of a battery bank and Supercapacitor SC has been employed to maintain power supply to the load. Because of the fast rate of charge/discharge of SC, it would handle the surge transient energy and that would reduce the required capacity of the battery, increase the life span, and lessen the maintenance cost. The control strategies ensure well power flow control between the PV system and ESS and thus meet the load demand, moreover sustain the DC-link voltage regulated. In the implemented control techniques, a protective improvement is proposed which would limit overcharging of the battery. That would minimize battery degradation and enhance the reliability of the system. This is performed by employing the battery uncompensated power to reduce the stress on the battery during the large fluctuations in the power due to load/irradiance variation. The control approach is based on providing optimal reference current for the ESS with fast dynamic response and reduce the computational burden when implementing in real-time, hence stable power could be delivered to the load. Moreover, the generated PV power is controlled based on the battery charging state and the demand power and this would ensure stable control of DC output voltage. The aforementioned points have led to vigorous PV system having a long-life battery and are capable of preserving energy balance between generations and load even at the abrupt load/solar irradiation variations. . A profile of solar irradiance for selected area has obtained and the system is tested under these data, and it has shown well dynamic response and this could be used for consideration of prototype implementation. The proposed control system is simulated by MATLAB/Simulink and the results show good performance under different scenarios of load/solar irradiation variations

Optimal control of high-rise building structures against earthquake excitations using independent stories as TMD

In this paper, in order to control the behavior of tall building structures against vibrations caused by earthquakes, the parameters of a new passive control system named “independent story” are optimized. This control system is installed like a large tuned mass damper in the main story of the building. Moreover, in order to improve the performance of this control system, six liquid viscous dampers connected from the roof of an independent story to the main story of the building, have also been used to increase damping of the system. The main goal of the present research is to optimize the parameters of this system, including selecting the optimal story/stories of the building to install the independent story/stories, such that the maximum reduction in the mean values of the maximum displacement and maximum acceleration responses of different stories of the building can be obtained, simultaneously. To evaluate the performance of the proposed control system, as a numerical example, a 3-D model of a twelve-story steel building structure is selected for which three degrees of freedom in each story have been considered. Then, a deep search is conducted to find the story/stories of the building at which the independent story control system along with the related supplemental liquid viscous dampers can be constructed/installed such that the best performance of this control system in response reduction of the building can be obtained. Finally, the optimal values of the mass and damping of the supplemental dampers of this system are found to be about 6% of the total mass of the building, and 13 kN-sec/mm, respectively; and the eighth and twelfth stories of the building are the best places for implementation of the proposed control system. By applying the optimal values of all the design parameters of the IS control system, the mean values of the building roof story response reductions were obtained to be about: 28.60% and 26.67% reduction on maximum displacement responses in x and y directions, respectively; and those of the maximum acceleration responses as 43% and 48.89%.

Design of nonlinear controllers and speed and angular position estimator for an electrically controlled cycloidal propeller

The current research focuses on the design and stability analysis of nonlinear controllers for an electrically driven marine cycloidal propeller, along with estimating propeller disc rotational speed, blade pitch angle, hydrodynamic torque, and thrust using the Extended Kalman Filter. The nonlinear controllers comprise a Lyapunov-based feedback controller for the propeller disc and six Backstepping controllers for the propeller blades. All states and control architecture are defined using an efficient, physics-based, electrically controlled marine cycloidal propeller model. The controller is being investigated to evaluate its ability to accurately receive, process, and respond to multiple control signals. The Monte Carlo simulation technique assesses the robustness of the developed controller and the proposed estimator. A validation investigation is performed to evaluate the performance of the proposed nonlinear controllers. The proposed control system is straightforward to implement for a wide range of applications, including marine cycloidal propellers and cyclorotor-driven unmanned vehicles.

Design and Control of Hydraulic Power Take-Off System for an Array of Point Absorber Wave Energy Converters

The development of wave energy converter (WEC) arrays is an effective way to reduce the cost of levelized energy and facilitate the commercialization of WECs. This study proposes a hydraulic power take-off (PTO) system for an array of point absorber wave energy converters (PA-WECs) and designs a control system using a novel algorithm called the improved simplified universal intelligent PID (ISUIPID) controller and the adaptive matching controller including an improved artificial gorilla troops optimizer (IGTO) to improve and stabilize the output power of PA-WEC arrays. Simulations under varying irregular wave states have been carried out to verify the validity of the mathematical model and the control system. The results show that the designed IGTO has faster convergence speed and better convergence accuracy in solving the optimal linear damping coefficient of the generator, and the proposed ISUIPID controller provides superior performance in tracking the speed of the hydraulic motor under the changing sea states. In addition, the capture power and output power of the array of PA-WECs are improved and the electrical energy can be output stably under the designed control system. The array of PA-WECs with the proposed control system will become an independent, stable, efficient, and sustainable power supply system.

Design and implementation of GCMBO control strategy for grid integrated SPV-DSTATCOM

In the modern world, there is a large need for both the conservation of electrical power and the improvement of its use in a variety of applications. So, this work implemented the solar and photovoltaic (SPV) based distributed static synchronous compensator (SPV-DSTATCOM), which is used to improve the power quality by integrating into grid. Further, hybrid controller is developed by combining perturb and observe (P&O) withMaximum power point tracking (MPPT) controller. The P&O based MPPT controller is used to extract the maximum amount of power generated from SPV array under variety of temperature, irradiance conditions. Then, DC-DC boost converter is used to integrate the SPV with the DC-link component of DSTATCOM. Then, the greedy control-based monarch butterfly optimization (GCMBO) is developed to control the DC-DC boost connected DSTATCOM voltage and current levels. Here, the individual monarch butterflies of GCMBO have a better degree of fitness than either of their parents or their grandparents. In addition to this, a self-adaptive crossover operator (SAC) is included into the butterfly adjusting operator (BAO). During the latter phase of the search, this SAC operator has the potential to make a significant contribution to increasing the diversity of the population, which helps to reduce the total harmonic distortion (THD). The simulation results shows that the proposed control system achieved THD as 1.42 %, which is less as compared to other existing methods.

Backpropagation Neural Network Adaptive Voltage Control of a High-Gain Transformerless DC/DC Boost Converter for solar applications

An artificial neural network (ANN) adaptive control method for high-gain transformerless DC-DC boost converter is designed in order to achieve better performance and robustness. An ANN controller have the ability to predict the duty cycle in each moment because it is already trained with a vast number of random values of input, output and reference voltages. In order to minimize the error of obtaining the most convenient duty cycle, a back-propagation algorithm has been used to train the neural network because it has the ability to deal with the weights constantly for the sake of diminishing the loss function which makes the ANN generates the fittest duty cycle. The control scheme has been done for a photovoltaic system and the simulation is done using Matlab/Simulink. Simulation results are given for irradiance, reference voltage and load variations. The results are designated to prove the effectiveness of the proposed control system.

Design and Development of Cascaded Current Control in DC Motor Variable Speed Drive using dSPACE

Even today, DC motors are still used in variety of applications, including home appliances, transportation, as well as industrial crane and rolling machine. However, achieving precise speed and torque control in DC drives at industry level could be challenging, as instability and reduced efficiency remains at large. This project focuses on developing a cascaded control system for a Separately Excited Brushed DC motor using dSPACE platform. The cascaded control system, designed using MATLAB Simulink, incorporates a proportional-integral (PI) controller at the speed loop and a Hysteresis controller at the current loop to improve robustness and dynamic performance. The experimental setup utilizes the dSPACE 1104 platform, an asymmetric bridge converter board, gate driver, and electrical load. Speed measurement is done using an incremental encoder, while current is measured using the ACS712 current sensor. The drive system was tested in alternate low and high speed cycle on various load level to test for stability, robustness and dynamic performance. The proposed control system was compared with PI-closed-loop control and open-loop control determine the best drive performance. Experimental results showed significant improvement in term of transient response and ripple reduction of speed and current for proposed cascaded current control over the closed-loop design.

Backstepping nonlinear control of a five-phase PMSG aerogenerator linked to a Vienna rectifier

The objective of this study is to create a nonlinear control system designed for a Vienna converter-connected five-phase aerogenerator. The control strategy will be based on the space vector pulse width modulation (SVPWM) technique, which is more suitable for controlling rectifier switches. Due to the higher complexity of the five-phase permanent magnet synchronous generator (PMSG) machine than the conventional three-phase generator, it poses a difficult control problem requiring an advanced mathematical model. Integrating an aerogenera tor with a Vienna converter enhances efficiency and power quality. The primary purpose of this study is to develop a nonlinear controller utilizing the retrogres sion control strategy alongside the SVPWM approach. Furthermore, a speed optimizer based on an artificial neural network is implemented to improve the system’s overall efficiency and meet multiple objectives. The proposed control system improves the overall operation and efficiency of Vienna’s integrated wind turbine and rectifier system

Analysis of AWPI Based Hybrid Grid-tied Inverter for Smart Energy Management System

Renewable energy sources are gaining popularity in recent years for power generation. The world has an abundance of pollution-free solar and wind energy; batteries play vital role for energy storage and all these sources combine to form a hybrid power system. Efficient management of the hybrid system via controlling the operation of its components is essential. In this article, a smart energy management system is proposed, where the control is focused on the hybrid grid-tied inverter. The inverter module is controlled through an anti-windup proportional-integral (AWPI) current and voltage controller. The performance of the proposed control system has been analyzed through current, and voltage Total Harmonic Distortion (THD) before and after grid integration. The suggested AWPI controller effectively mitigated the voltage drop and maintained the THD within the appropriate level. In addition, the proposed scheme of interest enhanced the steady-state response of the smart-grid system. The comparative analysis made among the intended controller and its counterparts inferred its superiority.

Electric Arc Furnace Electrode Movement Control System Based on a Fuzzy Arc Length Identifier

From the point of view of the synthesis of control influences, arc steelmaking furnaces are complex nonlinear objects with strongly expressed mutual influences. It has been demonstrated that at a given supply voltage, the distribution of the current values of the phase currents in the quasi-steady-state mode makes it possible to estimate the situation in the arc space of an arc steelmaking furnace and identify the value of arc lengths. This dependence is preserved in transient modes. In order to identify arc lengths from the phase currents, it is proposed to use an approach based on the theory of fuzzy sets. The construction of the fuzzy arc length identifier rule base was carried out in two stages: the first stage used data from quasi-steady-state modes and the Wang–Mendel algorithm; the second stage involved adding a new rule to the database if the activity level of the formed rules was lower than the established level α for the data obtained in the dynamic mode. Further optimization of the parameters of the fuzzy identifier for operation in dynamic modes was carried out using the “back-propagation” algorithm. Based on the identified values of arc lengths, a control system for the movement of electrodes in an arc steelmaking furnace was synthesised. The proposed control system makes it possible to eliminate unproductive electrode movements due to changes in the situation in other phases of the arc steelmaking furnace and simplifies the application of modern methods of synthesising a control system for such complex objects. The results obtained in the mathematical model have confirmed the effectiveness of the proposed control system for the movement of electrodes in an arc steelmaking furnace.

Heading control for quadruped stair climbing based on PD controller for the KRSRI competition

Quadruped, a robot that resembles four-legged animals, is developed for many purposes, such as surveillance and rescue. Such a caveat requires the robot to have the capability to overcome various terrain and obstacles. When moving across such a landscape, it is essential to maintain the robot's orientation steadily. Inclined terrains such as stairs have posed another challenge to the control strategy as the robot is unstable while climbing. Therefore, the contribution of this work is to address the need for heading control because of the relatively longer stairs used for the current competition compared to the past. The proposed control system simultaneously maintains the heading while keeping the body stable. The inertial measurement unit sensor carried by the robot would provide the pose needed for heading control calculations. The robot's heading becomes the base for the PD controller calculation. The final pose that stabilizes the robot while tackling heading error is a combination of correction from the PD controller and the stabilization part of the control strategy. Then, the leg servo angle determination deployed the inverse kinematics calculation from the suitable robot pose. The proposed method enabled the designed robot to maintain its heading with a 4.4-degree margin of error and stabilize the body. The quadruped also completes the stair climbing at the shortest time of 20 seconds with a speed of up to 5.5 centimeters per second.

Control system design for a class of non-cascade nonlinear under-actuated systems with an application to a rotary crane system

For cascade under-actuated systems, several effective control methods have been established. For non-cascade under-actuated systems with one under-actuation degree, several control methods, such as cascade transformation and energy-based control, have been studied. For non-cascade nonlinear under-actuated systems with two or more under-actuation degrees, approximate linearization is necessary to apply these methods, thus only stability at the equilibrium point has been guaranteed. In this paper, control system design and stability analysis are presented for a class of non-cascade nonlinear under-actuated systems with two or more under-actuated degrees. The stability condition obtained in the analysis is a conservative sufficient condition because norm estimation is used. Based on the sufficient condition, we propose a stabilization parameter search algorithm to numerically obtain the practical control parameters. The proposed algorithm is applied to a rotary crane system as one of the nonlinear systems with two under-actuated degrees. The effectiveness of the proposed control system design is verified by numerical simulations and experiments.

An Extended Topology Named Active Switched Capacitor/Switched Inductor Quasi-Z-Source with Multilevel Inverter (ASC/SL-QZSI) for Three-Phase Grid-Tie PV Power System: A SPBO–RBFNN Control Scheme

In this paper, the controller modelling and design for the Active Switched Capacitor/Switched Inductor Quasi-Z-Source with Multilevel Inverter (ASC/SL-QZSI) related three-phase grid-tied photovoltaic (PV) power system is proposed. The ASC/SL-QZSI control method consists of two phases: these are assessed with the use of the proposed effective controller. The proposed control system is the hybridization of the Student Psychology-Based Optimization (SPBO) and the Radial Basis Function Neural Network (RBFNN), hence it is named SPBO–RBFNN control scheme. The ASC/SL-QZS offers greater boost capability, uses fewer passive components, like inductors and capacitors, and reduces the voltage stress across main inverter switching devices. The expandability of this topology is another advantage. Extra cells can simply be cascaded at the network’s impedance if a higher boost rate is required by adding an inductor and three diodes. In the proposed control scheme, SPBO is developed for determining the total PV voltages. The input PV reference voltages and gain parameters of the SPBO are created as output for optimal tuning of the Proportional Integral (PI) controller. RBFNN is trained with offline process and it is used to extract the reference currents of the grid, and the output of RBFNN is provided with SPBO. It delivers the corresponding tuning parameters to accomplish the grid current. With this proper control, the input power is reduced and the current, voltage and frequency conditions of DC-link are regulated. Finally, the performance of the QZS-CMI is executed in MATLAB and the performance is compared with existing methods.

Output tracking control design with anti-disturbance rejection for modified repetitive nonlinear control systems

ABSTRACTThis paper intends to analyse the disturbance rejection and output tracking control for a class of nonlinear control systems with disturbances. In this connection, we present a method that combines a proportional-integral observer and nonlinear-equivalent-input-disturbance estimator for superior disturbance rejection performance. Specifically, the nonlinear-equivalent-input-disturbance estimator comprises of equivalent-input-disturbance estimator and nonlinear feedback term, which is employed to estimate and reject the disturbances from the nonlinear system. Notably, the proportional-integral loop in the proportional-integral observer reduces the estimation inaccuracy of the nonlinear-equivalent-input-disturbance. Then the estimated disturbance is intertwined into the repetitive control input to compensate it efficiently. In order to obtain the required results, the proposed control system is converted into a two-dimensional modified repetitive control system to describe the learning and control actions. In particular, the proposed controller enables to adjust the gains directly to improve the learning and control performance and as a result, the tracking accuracy increases. Using a general Lyapunov–Krasovskii functional, singular value decomposition technique and linear matrix inequalities approach, a design algorithm for establishing proportional-integral observer and feedback gains is developed for the system under consideration. Finally, simulation results are given to illustrate the developed method's validity and superiority.

An artificial neural network-based integrated tilt control system for narrow electric three-wheelers

The active tilt-controlled narrow electric three-wheelers offer an encouraging solution to pressing environmental and urban challenges, including air and noise pollution, parking shortage, and traffic congestion while improving their rollover stability. The previous research on active tilt control established a desired tilt angle neglecting track width which helps to bear unbalanced lateral acceleration that leads to rollover threat. This article proposes a dynamic rollover index based optimal tilting approach, which tilts the vehicle only when rollover index reaches beyond the desired optimal threshold values. Furthermore, the present work introduces an optimal active tilt control strategy, integrating tilt control system with an active steering system. The proposed integrated tilt control system optimizes an active steering gain with the objective to minimise the deviation in the desired trajectory. The proposed control system ensures the manoeuvrability and rollover stability at higher speed up to 100 km/hr with 22° steering angle even when vehicle reaches its tilt limit. Matlab/Simulink has been used in this study to thoroughly investigate the vehicle dynamics model and proposed integrated active tilt control system. The proposed control strategy is found to be more effective than the traditional steering direct tilt control approach. An artificial neural network that avoids the control system’s complexity and provides a faster response was also studied. The investigation showcases the efficacy of augmenting the dataset length and increasing neurons in the network’s hidden layers. The performance metrics of the trained model tested on the external data imply that the model holds significant potential for practical applications.

Enhanced MPPT-Based Fractional-Order PID for PV Systems Using Aquila Optimizer

This paper proposes a controller to track the maximum power point (MPP) of a photovoltaic (PV) system using a fractional-order proportional integral derivative (FOPID) controller. The employed MPPT is operated based on a dp/dv feedback approach. The designed FOPID-MPPT method includes a differentiator of order (μ) and integrator of order (λ), meaning it is an extension of the conventional PID controller. FOPID has more flexibility and achieves dynamical tuning, which leads to an efficient control system. The contribution of our paper lies is optimizing FOPID-MPPT parameters using Aquila optimizer (AO). The obtained results with the proposed AO-based FOPID-MPPT are contrasted with those acquired with moth flame optimizer (MFO). The performance of our FOPID-MPPT controller with the conventional technique perturb and observe (P&O) and the classical PID controller is analyzed. In addition, a robustness test is used to assess the performance of the FOPID-MPPT controller under load variations, providing valuable insights into its practical applicability and robustness. The simulation results clearly prove the superiority and high performance of the proposed control system to track the MPP of PV systems.

Grid-Forming Services From Hydrogen Electrolyzers

Hydrogen electrloyzers are power-to-gas storage devices that can facilitate large-scale integration of intermittent renewable sources into the future power systems. Due to their fast response and capability to operate in different loading conditions, they can be used as responsive loads providing support to AC grid during transients. This paper suggests taking one step further and using hydrogen electrolyzers to provide grid-forming services to the grid. As a result, the electrolyzer's role is elevated from supporting the grid (responsive load) to actively participating in forming voltage and frequency of the grid. The grid-forming capability of electrolyzer is linked to its hydrogen production constraints, which can potentially pose limitations on the grid-forming services. Besides the grid-forming mode, two additional operating modes, i.e., DC voltage mode and constant power mode, are proposed to ensure a safe operation of the electrolyzer in case of adversary interaction between grid-forming operation and hydrogen production constraints. This paper also studies the impacts of grid-forming services on the electrolyzer's physical features such as hydrogen stack temperature and efficiency. Comprehensive simulations are conducted on a low-inertia test network whose topology is inspired by a portion of the transmission grid in South Australia to confirm the effectiveness of the proposed concept under various operational conditions of the electrolyzer and upstream AC grid. Moreover, the practical feasibility of the proposed control system is experimentally validated by conducting hardware-in-the-loop tests.

Online Adaptive Neurochaotic Fuzzy Controller Design to Reduce the Seismic Response of Buildings Equipped with Active Tuned Mass Damper System

This paper presents a novel adaptive neurochaotic fuzzy control system based on type-2 fuzzy systems to reduce seismic responses in multistory structures with active tuned mass dampers under near-field and far-field earthquakes. In this proposed control system, the whole parameters of the plant are assumed to be completely unknown, the structural model is estimated using a multilayer perceptron neural network, and the system’s Jacobian is extracted. The online estimation model is used, and the controller parameters are adaptively trained using the extended Kalman filter and error back-propagation method. Subsequently, the control force is applied to the active tuned mass damper, and the control objectives are met. The adaptive controller does not require initial settings, and a fractional-order proportional-integral-derivative controller is added to maximize stability and robustness against seismic vibration. A simple adaptive controller optimized by a particle swarm is also presented as an innovation. Comparing the performance of the improved simple adaptive controller and adaptive neurochaotic fuzzy controller, the proposed controllers appear more efficient and accurate. However, the superiority of the novel adaptive neurochaotic fuzzy over the improved simple adaptive controller in reducing maximum displacement, acceleration, drift, and base shear while maintaining acceptable performance under parametric uncertainties and seismic conditions is substantial.

Assessing the seismic behavior of structures controlled with a novel elastoplastic-tuned mass damper inerter considering the effects of soil-structure interactions

In this paper, an elastoplastic-tuned mass damper inerter (PTMDI) is introduced, and its practical advantages under soil-structure interaction are investigated.For this purpose, a nine-story building frame equipped with PTMDI is modeled nonlinearly in the OpenSees software using the concentrated plasticity model. The Bald Eagle Search Optimization Algorithm calculates the PTMDI parameters for a structure with a fixed base or located on three soil types (i.e., soft, medium, and stiff soils).The objective function is the minimization of the Park-Ang damage index of the controlled structure. The results show that PTMDI has dramatically reduced the Park-Ang damage index in the controlled structure using the inertia-strengthening effect and the nonlinear behavior of the PTMDI system. The results show that the PTMDI has reduced the structural damage index of the controlled structures by more than 50% compared to the uncontrolled ones since the controlled system reduces the deformation of the members and drift story. Also, more energy is dissipated because the spring of the control system goes into the nonlinearity. It has also depicted that the PTMDI increases the performance level of the structure from the collapse state to the life safety state. Due to the inertia-enhancing effect of the PTMDI, the mass ratio of the proposed control system does not significantly affect the behavior of the structure. Therefore, the dynamic responses of the structure can be controlled with a small mass.