Showing papers in "Journal of Control, Automation and Electrical Systems in 2021"

New PI-PD Controller Design Strategy for Industrial Unstable and Integrating Processes with Dead Time and Inverse Response

Abstract: Industrial processes of unstable/integrating nature having a dead time and inverse response characteristics are challenging to control. For controlling such processes, double-loop control structures have proven to be more efficient than conventional PID controllers in a unity feedback configuration. Therefore, a new design method to obtain PI-PD controller settings is proposed for a set of unstable/integrating plant models with dead time and inverse response. The stabilizing proportional–derivative (PD) controller is designed using maximum sensitivity considerations and Routh–Hurwitz stability criteria. The PI controller settings are obtained by comparing the first and second derivatives of expected and actual closed-loop transfer functions about the origin of the s-plane. Adjustable parameters of the inner and outer loops are selected such that the desired value of maximum sensitivity is achieved. Simulation studies are conducted on some benchmark linear and nonlinear plant models used in literature. Robustness of the proposed design is analyzed with perturbed plant models, and quantitative performance measures are computed. It is found that the proposed design yields enhanced and robust closed-loop response than some contemporary works.

Renewable Energy-Based Load Frequency Stabilization of Interconnected Power Systems Using Quasi-Oppositional Dragonfly Algorithm

Abstract: It is already established that the renewable integration effects to the power system are nonzero and become more important with large penetrations. Thereby, the impacts of renewable energy sources (RESs) after integration are studied in this work to stabilize grid frequency of the studied test power system model. Initially, the two-area power system model is studied as the test system. The purpose is to show the tuning efficiency of non-conventional quasi-oppositional dragonfly algorithm (QODA) algorithm as compared to conventional way of tuning technique. It is showed that QODA algorithm is quite effective to find the optimal parameters of proportional–integral–derivative (PID) controller in load frequency control performance. Further, the three-area power system model integrated with RESs is studied. The work done here is to study the impacts of wind turbine generation, solar thermal power generation and solar photovoltaic on system frequency oscillations. The PID controller is employed as the supplementary control task, and its parameters are tuned by QODA algorithm. The integral of time absolute error is chosen as the objective function, and further performance indices are determined at the end of the execution of the program to examine the performance of the designed QODA-based PID controller. Following the integration of RESs, the impacts on frequency deviation through simulation results are also presented. The simulation results showed that the RESs are quite effective in regulating the power system frequency deviation understudied.

Optimal Fractional Order IMC-Based Series Cascade Control Strategy with Dead-Time Compensator for Unstable Processes

Abstract: In industrial unstable processes, disturbance rejection is more challenging task than setpoint tracking. So, cascade control structure is widely used in many chemical processes to reject disturbances. In this work, an advanced dead-time compensator-based series cascade control structure (SCCS) is suggested for unstable processes. The suggested SCCS has three controllers (named as primary, secondary and stabilizing controllers). Both primary and secondary controllers are designed using fractional order-based internal model control (IMC) approach. The stabilizing proportional–derivative controller is designed using maximum sensitivity considerations and Routh–Hurwitz stability criteria. Optimal values of the closed-loop time constants and fractional orders of IMC filters are obtained using constrained artificial bee colony (ABC) algorithm. This ABC algorithm uses a multi-objective function involving minimization of integral of absolute error, integral of time weighted absolute error and integral of squared error. Simulation studies are conducted using some benchmark plant models used in literature for illustrating the advantages of the proposed strategy compared to the state of the art. Moreover, robust stability of the proposed design is analysed and quantitative performance measures are also computed.

Aircraft Hybrid-Electric Propulsion: Development Trends, Challenges and Opportunities

Abstract: The present work is a survey on aircraft hybrid electric propulsion (HEP) that aims to present state-of-the-art technologies and future tendencies in the following areas: air transport market, hybrid demonstrators, HEP topologies applications, aircraft design, electrical systems for aircraft, energy storage, aircraft internal combustion engines, and management and control strategies. Several changes on aircraft propulsion will occur in the next 30 years, following the aircraft market demand and environmental regulations. Two commercial areas are in evolution, electrical urban air mobility (UAM) and hybrid-electric regional aircraft. The first one is expected to come into service in the next 10 years with small devices. The last one will gradually come into service, starting with small aircraft according to developments in energy storage, fuel cells, aircraft design and hybrid architectures integration. All-electric architecture seems to be more adapted to UAM. Turbo-electric hybrid architecture combined with distributed propulsion and boundary layer ingestion seems to have more success for regional aircraft, attaining environmental goals for 2030 and 2050. Computational models supported by powerful simulation tools will be a key to support research and aircraft HEP design in the coming years. Brazilian research in these challenging areas is in the beginning, and a multidisciplinary collaboration will be critical for success in the next few years.

Modified Salp Swarm Algorithm-Optimized Fractional-Order Adaptive Fuzzy PID Controller for Frequency Regulation of Hybrid Power System with Electric Vehicle

Abstract: A considerable no. of intermittent renewable sources such as PV generation and wind energy when integrated to the conventional grid technology causes serious issues in the power systems like frequency instability. So a more balancing controller is desired for a stable and reliable operation of the power system. Bidirectional power control of the EV aggregator is making itself a wise choice for distributed energy storage to scale down the frequency and power fluctuation. In this work, an intelligent load frequency controller using a fractional-order adaptive fuzzy PID controller with filter (FOAFPIDF) for hybrid power system with electric vehicle (EV) based on modified salp swarm algorithm (MSSA) technique is proposed. The effectiveness of MSSA technique is compared with original salp swarm algorithm as well as moth flame optimization , grey wolf optimization , particle swarm optimization and sine cosine algorithm techniques for benchmark test functions using statistical analysis. The effectiveness of the suggested load frequency control strategy by the use of electric vehicle as well as with other energy storing elements such as the superconducting magnetic energy storage component, flywheel energy storage system and ultra-capacitor along with their inherent rate constraint nonlinearity is validated by numerical simulations conducted on the studied test system. It is demonstrated that the proposed controller provides a better control action to suppress the frequency fluctuations as compared to PID controller. The robustness of the controller is also investigated against variation of system parameters and random load changes.

Performance Evaluation of Perturb and Observe Algorithm for MPPT with Buck–Boost Charge Controller in Photovoltaic Systems

Abstract: The ecological and economical concerns have given rise to the application of solar photovoltaic (PV) system in the community to meet the increased load demands on its own. Owing to the varying output of solar PV modules depending on the weather conditions, the efficiency of system is degraded and thus requires using maximum power point (MPP) tracking technique for utmost power extraction. The MPP methods can be realized both mechanically or electrically using suitable converter topology and appropriate tracking algorithm. However, the mechanical tracker in contrast to electrical circuit is immoderate and inefficient and conversely necessitates to exercise effective and promising power tracking algorithm. This research proposes to implement the perturb and observe (P and O) algorithm for MPPT in charge controller using buck–boost converter to attain desired and optimized results. The use of buck–boost DC–DC converter helps in stepping up/down the voltage level as per requirements under the control of P and O algorithm. Having optimized tracker designed, its performance has been tested at different levels of irradiance and temperature principally with load and battery. Further, the results obtained from simulated scenarios are compared with real-time experiments, which confirm the robustness and effectiveness of proposed MPPT method in solar PV system using P and O algorithm and buck–boost converter.

Optimal PI–PD Controller Design for Pure Integrating Processes with Time Delay

Abstract: Though Proportional-Integral-Derivative (PID) controllers are commonly being used for process control applications, it has been proven that they may give unacceptable closed loop responses for open loop unstable processes including integrating ones. Hence, this paper addresses to tuning of PI–PD controllers which is an extension of PID controllers and uses PD part in an inner feedback loop to convert the open loop unstable processes to a stable one so that PI controller in the forward path can be used to achieve a better closed loop response. PI–PD tuning parameters are determined from simple analytical rules which were obtained from minimization of the control system error based on IST3E criterion which is an integral performance index and has been proven to be resulting in very satisfactory closed loop responses. Derived tuning rules are in terms of the assumed process transfer function parameters, namely the gain and time delay. Effectiveness and superiority of obtained tuning rules have been shown by simulation examples.

Sliding Variable-based Online Adaptive Reinforcement Learning of Uncertain/Disturbed Nonlinear Mechanical Systems

Abstract: In this work, the trajectory tracking control scheme is the framework of optimal control and robust integral of the sign of the error (RISE); sliding mode control technique for an uncertain/disturbed nonlinear robot manipulator without holonomic constraint force is presented. The sliding variable combining with RISE enables to deal with external disturbance and reduced the order of closed systems. The adaptive reinforcement learning technique is proposed by tuning simultaneously the actor–critic network to approximate the control policy and the cost function, respectively. The convergence of weight as well as tracking control problem was determined by theoretical analysis. Finally, the numerical example is investigated to validate the effectiveness of proposed control scheme.

Real-Time Autonomous Vehicle Localization Based on Particle and Unscented Kalman Filters

Abstract: In this paper, a real-time Monte Carlo localization (RT_MCL) method for autonomous cars is proposed. Unlike the other localization approaches, the balanced treatment of both pose estimation accuracy and its real-time performance is the main contribution. The RT_MCL method is based on the fusion of lidar and radar measurement data for object detection, a pole-like landmarks probabilistic map and a tailored particle filter for pose estimation. The lidar and radar are fused using the unscented Kalman filter (UKF) to provide pole-like static-object pose estimations that are well suited to serve as landmarks for vehicle localization in urban environments. These pose estimations are then clustered using the grid-based density-based spatial clustering of applications with noise algorithm to represent each pole landmark in the form of a source-point model to reduce computational cost and memory requirements. A reference map that includes pole landmarks is generated offline and extracted from a 3-D lidar to be used by a carefully designed particle filter for accurate ego-car localization. The particle filter is initialized by the fused GPS + IMU measurements and used an ego-car motion model to predict the states of the particles. The data association between the estimated landmarks by the UKF and that in the reference map is performed using the iterative closest point algorithm. The RT_MCL is implemented using the high-performance language C++ and utilizes highly optimized math and optimization libraries for best real-time performance. Extensive simulation studies have been carried out to evaluate the performance of the RT_MCL in both longitudinal localization and lateral localization.

Autonomous Navigation System for a Delivery Drone

Abstract: The use of delivery services is an increasing trend worldwide, further enhanced by the COVID pandemic. In this context, drone delivery systems are of great interest as they may allow for faster and cheaper deliveries. This paper presented a navigation system that makes feasible the delivery of parcels with autonomous drones. The system generates a path between a start and a final point and controls the drone to follow this path based on its localization obtained through GPS, 9DoF IMU, and barometer. In the landing phase, information of poses estimated by a marker (ArUco) detection technique using a camera, ultrawideband (UWB) devices, and the drone’s software estimation are merged by utilizing an extended Kalman filter algorithm to improve the landing precision. A vector field-based method controls the drone to follow the desired path smoothly, reducing vibrations or harsh movements that could harm the transported parcel. Real experiments validate the delivery strategy and allow the evaluation of the performance of the adopted techniques. Preliminary results state the viability of our proposal for autonomous drone delivery.

Minimum-Time Trajectory Planning for a Differential Drive Mobile Robot Considering Non-slipping Constraints

Abstract: We propose a real-time minimum-time trajectory planning strategy with obstacle avoidance for a differential-drive mobile robot in the context of robot soccer. The method considers constraints important to maximize the system’s performance, such as the actuator limits and non-slipping conditions. We also present a novel friction model that regards the imbalance of normal forces on the wheels due to the acceleration of the robot. Theoretical guarantees on how to obtain a minimum-time velocity profile on a predetermined parametrized curve considering the modeled constraints are also presented. Then, we introduce a nonlinear, non-convex, local optimization using a version of the Resilient Propagation algorithm that minimizes the time of the curve while avoiding obstacles and respecting system constraints. Finally, employing a new proposed benchmark, we verified that the presented strategy allows the robot to traverse a cluttered field (with dimensions of 1.5 m $$\times $$ 1.3 m) in 2.8 s in 95% of the cases, while the optimization success rate was 85%. We also demonstrated the possibility of running the optimization in real-time, since it takes less than 13.8 ms in 95% of the cases.

Power Loss Minimization Using Optimal Placement and Sizing of Photovoltaic Distributed Generation Under Daily Load Consumption Profile with PSO and GA Algorithms

Abstract: The penetration of distributed generation (DG) in the distribution network has become a necessity and a significant solution to improve power grid quality, and solve power losses issue. To reach these targets, integrating these DGs in an optimal placement with an optimal sizing should be investigated and taken into consideration. This paper focuses on obtaining the optimal allocation and size of a photovoltaic (PV) distributed generation (PVDG) in order to reduce the total power losses and enhance voltage and frequency profiles of a modified IEEE 14 node distribution network (Hooshmand in J Appl Sci 8(16):2788–2800, 2008, https://doi.org/10.3923/jas.2008.2788.2800 ). An objective function is used in this paper aims to reduce grid power losses, and two optimization algorithms are applied to solve this function which are the particle swarm optimization (PSO) and the genetic algorithm (GA). Added to that, two scenarios are discussed in this paper in order to analyze the effects of variable PV penetration level, hourly load consumption profile variation and atmospheric condition change on the sizing optimization resolution. The obtained simulation results prove that the PSO algorithm has better performance compared to the GA in terms of speed convergence, power loss reduction and grid quality improvement (voltage and frequency profiles). Then, it shows that variable load consumption curve and weather condition change can affect not only the determination of the PVDG optimal position and capacity but also grid security.

Maintenance Planning of Electric Distribution Systems—A Review

Abstract: Electric distribution systems have the objective of supplying electricity with quality and reliability to the final consumers. In order to meet both criteria, efficient maintenance programs have a vital importance mainly due to the actual increase in the requirements for distribution service quality and in technologies related to electrical networks. In this sense, the number of options and criteria for developing effective programs makes the related decision-making process a complex task. This paper presents a comprehensive review on maintenance planning in electrical distribution systems covering different criteria such as economic and reliability. More specifically, this work summarizes contributions up to now through a holistic framework that comprises the premises of predictive, preventive and corrective maintenance. The work is organized by relevant aspects of researches in the field, as criteria, probability functions, constraints and methods that have been applied, within a comprehensive classification.

Nonlinear Neuro-Adaptive Control for MPPT Applied to Photovoltaic Systems

Abstract: This paper presents an intelligent control technique using a nonlinear neuro-adaptive method. This method is based on a nonlinear model describing the dynamics of the boost converter, the PV array and the load for maximum power point tracking (MPPT) under varying environmental conditions. The proposed approach consisted of a radial basis function-neuro observer for online estimation of unknown PV system parameters (i.e., irradiation and temperature) and an online trained neuronal controller that ensures a satisfactory MPPT, whatever be the position of the photovoltaic panel. The real-time implementation of the proposed controller is achieved using Arduino Mega board. The performance of the proposed MPPT method is analyzed under different operating conditions and compared to those provided by the P&O method. Simulation results using MATLAB/Simulink software coupled to experimental results demonstrate the feasibility and the robustness of the proposed controller.

Controlling Epidemic Diseases Based only on Social Distancing Level

Abstract: On March 11, 2020, the world health organization (WHO) characterized COVID-19 as a pandemic. When the COVID-19 outbreak began to spread, there was no vaccination and no treatment. To epidemic diseases without vaccines or other pharmaceutical intervention, the only way to control them is a sustained physical distancing. In this work, we propose a simple control law to keep the epidemic outbreak controlled. A sustained physical distancing level is adjusted to guarantee the fastest way to finish the outbreak with the number of hospitalized individuals below the desired value. This technique can reduce the economic problems of social distancing and keeps the health care system working. The proposed controller is a closed-loop approach that uses the number of hospitalized individuals as the feedback signal. It also does not need massive swab tests, which simplify the application of the technique. We do stability analyses of the proposed controller to prove the robustness to uncertainties in the parameters and unmodeled dynamics. We present a version of the proposed controller to operate using steps to reopen, which is relevant to help the decision-makers. The proposed controller is so simple that we can use a spreadsheet to calculate the physical distancing level. In the end, we present a set of numerical simulations to highlight the behavior of the number of hospitalized individuals during an epidemic disease when using the proposed control law. We simulate the proposed controller applied to the ideal case, considering uncertainties, unmodeled dynamics, a 10 days latent period, and different values of the desired number of hospitalized individuals. In all cases, the proposed controller ensures the number of hospitalized individuals lower than the upper limit of a predefined range.

Fixed-Time Adaptive Robust Synchronization with a State Observer of Chaotic Support Structures for Offshore Wind Turbines

Abstract: The chaotic support structures for offshore wind turbines are often subjected to a severe environment. A robust control scheme needs to be considered to maintain them in a safe operational limit. Robust sliding mode control (SMC) scheme can provide an excellent robust controller against this severe and challenging environment for these chaotic structures. This paper proposes a novel fixed-time adaptive sliding mode control scheme with a state observer to synchronize chaotic support structures for offshore wind turbines in the presence of matched parametric uncertainties. The proposed controller is a new integration of adaptive control concept, SMC method, fixed-time stability concept and a state observer. A fixed-time stability concept is used to provide stability for the system within a presented time regardless of initial conditions. The adaptive concept is utilized to provide an online estimator of the uncertain upper bound. Also, a nonlinear observer is employed to provide an online estimator of an unmeasured state in the controller. Lyapunov stability theorem is used to analyze fixed-time stability of the system based on SMC methodology. The simulation results demonstrate that the proposed controller is able to ensure fixed-time synchronization along with providing precise means to estimate the unmeasured state as well as uncertainty upper bound.

Fault Current of PV Inverters Under Grid-Connected Operation: A Review

Abstract: As well as many benefits, many conflicts arise with the large-scale connection of distributed generation (DG) in distribution networks. Leading the protection devices to malfunction and increasing the complexity of fault location refer to the main DG impacts under fault condition. These issues are even more challenging by considering a scenario with photovoltaic (PV) distributed generation since there is an expressive number of articles presenting divergent claims about the fault current value reached by PV inverters. The different values reported in the literature increase the uncertainty about the real fault contribution from PV inverters. Under such a scenario, a wide survey and a critical review are presented in this article in order to show such divergence and to present a more intuitive insight into fault currents from PV inverters.

Adaptive Control of Wind Turbine Generators for Power Capture Optimization by Using Integral Backstepping Approach During Partial-Load Operation

Abstract: The paper proposes an effective nonlinear integral backstepping approach strategy (IBS) through the use of a new adaptive power control algorithm for variable-speed wind turbine generators (VS-WTGs) to optimize the power extracted from the WTGs. The suggested approach is applied to one of the most frequently used maximum power control methods called as tip speed ratio (TSR) during partial-load operation considering the effects of the variations in wind velocity profile and unmodeled system dynamics such as external disturbances. The proposed method of control is known to scale-back the mechanical stress at the level of the GT shafts (generator and turbine). Furthermore, the IBS approach is relatively simple, which significantly reduces the online computational time and cost. Some numerical simulation results prove, under different wind speed models, that the proposed AC-TSR-IBS method works well on the level of the improved efficiency and the rapid system response compared to conventional TSR-PI method.

Controlling and Synchronization of Chaotic Systems Via Takagi–Sugeno Fuzzy Adaptive Feedback Control Techniques

Abstract: In this research article, we have addressed the T-S (Takagi–Sugeno) fuzzy modeling and controlling and adaptive synchronization of chaotic systems. Based on the T-S fuzzy model, the fuzzy logic for controlling and synchronization for chaotic systems are designed via linear matrix inequality (LMI). We have illustrated the new chaotic Chen system. Lyapunov exponents and bifurcation diagrams of new Chen system are obtained to justify the chaos in system. Analytical and computational studies of new Chen systems with triangular fuzzy membership function have been performed by using LMI toolbox. Numerical simulation illustrates the controlling chaos as well as adaptive synchronization for the identical systems. Feedback gain matrices and Lyapunov positive definite matrix for the synchronization of identical new Chen systems are obtained.

Development of Optimal Feature Selection and Deep Learning Toward Hungry Stomach Detection Using Audio Signals

Abstract: The process of transmitting signals to the body regarding the hungry stomach is referred to as the migrating motor complex (MMC) process. The intestines and stomach are considered for sensing the unavailability of food in the body. Hence, the receptors present in the stomach wall generate the electrical activity waves and activate the hunger. In general, audio signal processing algorithms include signal analysis, property extraction, and behavior prediction, identifying the pattern available in the signal, and predicting how a specific signal is correlated to various identical signals. The major challenge here is to consider the audio signals that are produced from the stomach for identifying the growling sound that well describes the hungry. The main intent of this paper is to develop an intelligent model using the deep learning concept for recognizing the hungry stomach by using the synthetically collected audio signals through mobile phones. This makes society reaching the hungry stomach by way of intelligent technology. The proposed detection model covers different phases for automated hungry stomach detection. The data acquisition is performed by gathering information using mobile phones. Further, the pre-processing of the signals is done by the median filtering and the smoothening methods. In order to perform the proper classification, the spectral features like spectral centroid, spectral roll-off, spectral skewness, spectral kurtosis, spectral slope, spectral crest factor, and spectral flux, and cepstral domain features like mel-frequency cepstral coefficients (MFCCs), linear prediction cepstral coefficients (LPCCs), Perceptual linear prediction (PLP) cepstral coefficients, Greenwood function cepstral coefficients (GFCC), and gammatone cepstral coefficients (GTCCs) are extracted. Further, the optimal feature selection is done by the improved meta-heuristic algorithm called best and worst position updated deer hunting optimization algorithm (BWP-DHOA). An improved deep learning model called optimized recurrent neural network (RNN) is used for classifying the optimal features of the audio signal into growling sound and burp sound. Finally, the performance comparison over the existing models proves the efficiency and reliability of the proposed model.

Analysis of Hybrid Fuzzy Logic Control Based PID Through the Filter for Frequency Regulation of Electrical Power System with Real-Time Simulation

Abstract: In the present study, a teaching learning based optimization (TLBO) method tuned hybrid fuzzy logic control based PID through the Filter (FPIDN) is proposed for frequency regulation of the electrical power system problem. The studied electrical power system contains the non-linearity parameters like governor dead band, time delay, and generation rate constraint. The performance of TLBO is analyzed by applying a genetic algorithm (GA) method. It is observed that TLBO outperforms compared with GA. Next, an investigation has been carried out by placing static synchronous series compensator in the tie-line. It is demonstrated that TLBO outperforms GA technique in controller design problem. To confirm the capability of the recommended method, experimental commendation with the hardware-in-the-loop real-time validation based on OPAL-RT has been implemented. Finally, the performance of TLBO tuned FPIDN controller is equated with some recently suggested frequency control problem in a standard two-area system. From the simulation outcome, it is established that the suggested plan offers better compared with existing methods.

A Path-Following Controller for Guiding a Single Robot or a Multi-robot System

Abstract: A solution to the problem of path-following by a team of terrestrial mobile robots is proposed in this paper. The proposal, in this case, is a formation controller dealing with three robots navigating in a coordinate way (as a formation). Based on the controller proposed to guide the formation to follow a prescribed path, an extension to the case of path-following with a single terrestrial mobile robot is also proposed. When regarding a formation of mobile robots, the proposed solution consists in applying a path-following controller to the center of mass of the formation, dealt with as a single virtual robot, and trajectory-tracking controllers to the individual robots in the formation. The advantage of such approach is that it allows planning the motion of the desired formation without specifying how each robot should move. The movement is specified for the formation as a whole, using a representation called cluster-space, and the movement of the individual robots is derived from the specification of the formation movement directly, using transformations from the cluster-space to the space of the robots and vice versa. In the sequel, the path-following controller designed for the virtual robot is analyzed in detail, now dealing with the possibility of being also used as a path-following controller applied to a single real robot. Theoretical stability analysis is presented, as well as some experimental results, whose analysis allows claiming that the proposed controller is suitable to guide either a single robot or a multi-robot formation when following a path.

Negative Imaginary Theory-Based Proportional Resonant Controller for Voltage Control of Three-Phase Islanded Microgrid

Abstract: This paper demonstrates the design of robust proportional resonant (PR) controller using negative imaginary (NI) theorem for voltage control of three-phase islanded microgrid (MG) application. While operating MG as the islanded mode, different types of random and unknown load dynamics affect the MG. These loads eventually deteriorate the proper execution of MG-inducing disturbances in voltage and current. Therefore, to improve the performance of the three-phase MG, a simple, second-order controller is designed with the combination of NI theory and PR (NI–PR) controller. This controller is capable of providing higher level of damping as well as excellent stability properties. The stability and effectiveness of this controller are examined through imposing uncertainties, in terms of several load dynamics as well as different fault conditions. The comparison with respect to linear quadratic regulator and model predictive controller also ascertains the robustness of the designed controller. The NI–PR controller and the system are simulated in MATLAB/SIMULINK platform.

A Control Approach to Web Speed and Tension Regulation of Web Transport Systems Based on Dynamic Surface Control

Abstract: This paper proposes an approach to deal with control problems of unmodeled components of the web transport system. It is commonly challenging to construct model-based controllers to guarantee tracking quality due to the unknown terms in the mathematical model. Hence, our main contribution is to design a robust controller for the uncertain model of the web transport system based on Dynamic Surface Control (DSC) technique to ensure high accuracy in the tracking process of the web speed and tension. The proposed controller is designed in the face of the mathematical model of the web transport system that is adversely affected by bounded uncertainties. The stability of the controlled system is proved using the Lyapunov standard. The simulation results show the validity and the effectiveness of the proposed control law when the system is lack of system model’s information.

Finite-State Predictive Current Control of a Standalone DFIG-Based Wind Power Generation Systems: Simulation and Experimental Analysis

Abstract: This paper presents an improved stator voltage magnitude and frequency control for standalone doubly fed induction generators (DFIGs) based wind power generation systems (WPGSs). The proposed technique uses a simple finite-state predictive current control (FS-PCC). In this control method, the switching vector for the IGBT is selected to minimize the error between the reference value and the predicted value of the rotor current. Moreover, the discrete-time models of (DFIG) are needed to predict the future value of the rotor current for all possible voltage vectors generated by the rotor-side converter (RSC). Since the classic control methods in the literature use inner control loops and are based on pulse width modulation (PWM), this method does not require complex modulation stages and omits the current control loops, which reduces the control requirements. The main objective in a standalone DFIG system is to keep the stator voltage has constant in amplitude and frequency and equal to the reference value, regardless of the changes in rotor speed or load. The proposed control strategy was implemented through a 3 kW DFIG prototype platform-based dSPACE 1104 card. The simulation and experimental results show that the proposed FS-PCC offers excellent reference tracking with less total harmonic distortion (THD) in stator voltages and rotor currents.

Comparison of SMC and PID Controllers for Pneumatically Powered Knee Orthosis

Abstract: The study aims in the design and implementation of suitable controllers for the knee joint of a pneumatically actuated orthosis, which is intended for rehabilitation and assistive purposes. Pneumatically powered orthosis, when compared with electrically driven orthosis, is lightweight in structure and also cost-effective. The knee and hip joints of the orthosis should follow the desired angle trajectory so that subject can move and stabilize efficiently. The role of controllers is crucial for the effective functioning of the limb to achieve the desired angle and velocity. Controllers like SMC and PID are integrated in the driving mechanism of the limb, and the characteristics are determined. The gain constants of proportional–integral–derivative controller (PID) are tuned manually to get the optimal response. Sliding mode control (SMC), basically a nonlinear control method is implemented for better performance. The prototype of below hip orthosis is fabricated, and the kinematic equations of the system are determined which is used to choose the optimum trajectory for the knee joints. The dynamics of the system are determined using Lagrange Euler Method, and the actuator torque required for both the joints is calculated. The performance of both controllers is compared, and SMC controller proved to be efficient in tracking the different types of reference inputs of the pneumatic assistive limb.

Scheduling Problems for a Class of Hybrid FMS Using T-TPN and Beam Search

Abstract: This paper is about scheduling problems for a class of flexible manufacturing systems (FMS) that have some operations with total precedence constraints and other operations with full routing flexibility (namely hybrid FMS). The objective is to find a control sequence from an initial state to a reference one in minimal time. For that, a systematic multi-level formalism is introduced to model the hybrid FMS based on the hierarchical structuration of the operations. Transition-timed Petri nets (T-TPN) that behave under earliest firing policy are used for that purpose. Then a new cost function is introduced to estimate the residual time to the reference. This estimation is proved to be a lower bound of the true duration. A modified Beam Search algorithm is proposed that uses the cost function to selectively explore the Petri net (PN) state space. Computational experiments illustrate the efficiency of the approach in comparison with other existing methods.

Power Quality Monitor Allocation Based on Singular Value Decomposition and Genetic Algorithm

Abstract: This paper proposed a novel method for allocating power quality meters with the main objective of ensuring a completely observable power distribution system. The method was designed to establish the quantity, location and type of measurement to be performed (voltage and/or current) for a given distribution system by employing genetic algorithms and the principles of state estimation based on the singular value decomposition. Moreover, a limitation in the number of current measuring channels was inserted in the mathematical formulation as an alternative for reducing costs. The method has been validated by running a three-phase harmonic state estimation using the IEEE 34 and 37 bus distribution test feeders. The results demonstrated the effectiveness of the method for designing power quality monitoring systems in distribution grids, ensuring full observability for state estimation purposes.

Optimal Location and Compensation Using D-STATCOM: A Hybrid Hunting Algorithm

Abstract: This paper intends to propose a power quality design model for the distributed system through nonlinear functions, and hence the prerequisite of power quality enhancements can be precisely quantified. As the model is adaptable, it needs a robust optimization algorithm for estimating the optimal location and compensation of the D-STATCOM. Hence, this paper develops a hybrid meta-heuristic optimization algorithm based on the prey targeting behavior of whales. The proposed hybrid whale optimization, Whale with Grey Wolf Optimization (WG), is used for determining the optimal placing and sizing of D-STATCOM by solving the power quality model. The solutions will be reactive power-encoded with two bound constraints to address both the localizing and sizing problems. Besides, along with the renowned literature, we determine the Mean Voltage Stability Index. The updating algorithm of the whale optimization will be hybridized with the hunting behavior of grey wolves so that the location and sizing of D-STATCOM can be estimated precisely. The proposed WG algorithm compares its performance over other conventional methods such as GA, ABC, PSO, GWO, and WOA in terms of convergence analysis, cost analysis, and total loss and determines the effectiveness of the proposed power quality model.