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The Photovoltaic (PV) system is an eco-friendly renewable energy system that is integrated with a DC-DC buck-boost converter to generate electrical energy as per the variations in solar irradiance and outdoor temperature. This article proposes a novel Adaptive Fractional Order PID (A-FOPID) compensator with self-adjusting fractional orders to extract maximum power from a stand-alone PV system as ambient conditions change. The reference voltage is generated using a feed-forward neural network. The conventional FOPID compensator, which operates on the output voltage error of the interleaved buck-boost converter, is employed as the baseline maximum-power-point-tracking (MPPT) controller. The baseline controller is retrofitted with an online state-error-driven adaptation law that dynamically modifies the fractional orders of the controller’s integral and differential operators. The adaptation law is formulated as a nonlinear hyperbolic scaling function of the system’s state error and error-derivative variables. This augmentation supplements the controller’s adaptability, enabling it to manipulate flexibly the tightness of the applied control effort as the operating conditions change. The efficacy of the proposed control law is analyzed by carrying out customized simulations in the MATLAB Simulink environment. The simulation results show that the proposed MPPT control scheme yields a mean improvement of 25.4% in tracking accuracy and 11.3% in transient response speed under varying environmental conditions.

This paper synthesizes an innovative nonlinear-type self-adaptive Linear Quadratic Regulator (LQR) to enhance the robustness of under-actuated systems against bounded exogenous disturbances by using the relative-rate and state-error feedback. The control procedure is realized by augmenting the generic LQR with an online reconfiguration block that acts as a superior regulator to dynamically adjust the state-weighting-factors associated with the quadratic-performance index. The Riccati Equation uses the modified state-weighting-factors to re-compute the LQR problem and delivers the time-varying state-feedback gains. The said weighting-factors are adaptively modulated via a conventional state-error-magnitude-driven adaptation law that is retrofitted with auxiliary relative-rate-driven reconfiguration blocks. These blocks utilize error acceleration to deduce the response speed and thus re-adjust the variation rate of the weights in real time. This arrangement reinforces the controller’s adaptability to flexibly manipulate the tightness of the applied control effort. The proposed scheme is analyzed by conducting real-time hardware experiments on the QNET Rotary Pendulum. The experimental outcomes validate the superior position-regulation and disturbance-rejection behavior of the proposed scheme.

Monkeypox is a rare zoonotic DNA virus with lineage from the Poxviridae family, Chordopoxvirinae subfamily, and Orthopoxvirus genus. (1) With a previous history of controlled and contained occasional outbreaks of the virus, currently a widely erupted outbreak of monkeypox with progressively rising numbers has been reported since May 2022 in multiple countries of the western hemisphere that are not historically endemic for this infection, particularly the UK and EU countries. The global cessation of smallpox vaccination has been hypothesized to cause the rise in monkeypox infections in recent years. (5) (7) Monkeypox like any other viral infection commences with prodromal symptoms; a maculopapular rash with centrifugal distribution usually follows. (16) (9) Polymerase Chain Reaction (PCR) confirms the diagnosis. (16) Transmission in humans is possible through infected animals or humans. (5) (27) In the ongoing 2022 outbreak, monkeypox virus has been undergoing novel mutations at an alarming rate. (56) Treatment options for monkeypox is an area that still requires extensive research, the utility of certain antiviral medications in treating monkeypox infection is currently being explored but is still controversial and debatable.

This article presents a novel self-adaptive linear-quadratic-regulator (LQR) architecture to improve the robustness of self-stabilizing electromechanical systems against exogenous disturbances. The main contribution of this article is to formulate a nonlinear-type artificial-immune adaptation mechanism that dynamically adjusts the state-weighting-factors of LQR’s quadratic-performance-index online. The Riccati-equation solver uses these updated state-weighting-factors to yield time-varying state-feedback gains. This hierarchical control procedure uses immunological computations to indirectly alter the LQR gains, which helps in flexibly reconfiguring the control trajectory under disturbances. The performance of the proposed immune-adaptive LQR is benchmarked against a conventional adaptive LQR and a fixed-gain LQR by conducting software simulations on the nominal model of the QNET rotary pendulum system. Credible real-time experiments are also conducted on the QNET rotary pendulum’s hardware setup to analyze each controller’s efficacy in the physical environment. The simulation and experimental results validate the superior disturbance-rejection capability of the proposed controller under every testing scenario.

Elevators are surfaces of flight control, typically at the rear of an aircraft to control the pitch of the plane, the angle of attack and the wing lift. The most critical actuation device is longitudinal aircraft control, and its failures will result in a catastrophic aircraft crash. This paper proposes a Highly Redundant Fault Tolerant Control (HRFTC) policy for the aircraft to accommodate faults in the critical sensors and actuators. Modified Triple Modular Redundancy (MTMR) has been proposed for the sensors and Dual Redundancy (DR) has been proposed for the actuators. The working of control laws, pilot order, signal conditioning, and failure are elaborated. Furthermore, the PID controller is used for the adjustment of the position of the elevator by comparing it with a set point. The results show that when a fault occurs, the system detects it successfully and tolerates it quickly without disturbing the flight of aircraft. The study is significant for the avionics industry for manufacturing highly reliable machines for human and environmental safety.

Today’s world is now more interested in the robustness and automation of machines, robots, and vehicles for accurate performance in the events of external disturbances and uncertainties in the system parameters. For that purpose, a Proportional–Integral–Derivative (PID) control has found many applications in every field of industry and academics. Many methods can be used for tuning of PID, however, these methods suffer from continual variations of parameters and exterior disturbances. Therefore, a PID controller is needed that is robust to external disturbances and possess auto-tuning feature according to varying process conditions. The main motivation of this paper is to present a PID controller that is robust, continuously auto-tuned by considering uncertainties of the plant, optimize the response by minimizing the overshoot in reference tracking, and achieve stability in minimum settling time by rejecting disturbance. The earlier models can only do two tasks, tuning and optimization but this paper is proposing a contribution to such models by rejecting external disturbances and making sure the smooth running of the system. In this paper, we have done the simulation work to demonstrate the desired behaviour of the system and evaluate the effectiveness of the proposed model and a brief analysis is done by discussing the comparison between the previous work in literature and the proposed work.

This article methodically constructs a novel adaptive self-tuning state-space controller that enhances the robustness of under-actuated systems against bounded exogenous disturbances. The generic Linear-Quadratic-Regulator (LQR) is employed as the baseline controller. The main contribution of this article is the formulation of a hierarchical online gain-adjustment mechanism that adaptively modulates the weighting-factors of LQR’s quadratic-performance-index by using pre-calibrated continuous hyperbolic scaling functions. The hyperbolic scaling functions are driven by the magnitude of system’s state-error variables. This augmentation dynamically updates the solution of the Matrix-Riccati-Equation which modifies the state-feedback gains after every sampling interval. The efficacy of the proposed adaptive controller is validated by conducting hardware-in-the-loop experiments on QNET Rotary Pendulum setup. The experimental outcomes show that the proposed adaptive control scheme yields stronger damping against oscillations and faster error-convergence rate, while maintaining the controller’s asymptotic-stability, under the influence of parametric uncertainties.

This article systematically formulates an optimized cross-coupled single-input fuzzy-logic control mechanism that aims to enhance the tracking and contouring control performance of an X-Y positioni...

This paper presents the development of an indirect adaptive state-feedback controller to improve the disturbance-rejection capability of under-actuated multivariable systems. The ubiquitous Linear-Quadratic-Regulator (LQR) is employed as the baseline state-feedback controller. Despite its optimality, the LQR lacks robustness against parametric uncertainties. Hence, the main contribution of this paper is to devise and retrofit the LQR with a stable online gain-adjustment mechanism that dynamically adjusts the state weighting-coefficients of LQR's quadratic cost-function via state-error dependent nonlinear-scaling functions. An original self-mutating phase-based adaptive modulation scheme is systematically formulated in this paper to self-adjust the state weighting-coefficients. The scheme employs pre-calibrated secant-hyperbolic-functions whose waveforms are dynamically reconfigured online based on the variations in magnitude and polarity of state-error variables. This augmentation dynamically alters the solution of the Riccati-Equation which modifies the state-feedback gains online. The proposed adaptation flexibly manipulates the system's control effort as the response converges to or diverges from the reference. The efficacy of proposed adaptive controller is validated by conducting hardware-in-the-loop experiments to vertically stabilize the QNET 2.0 Rotary Pendulum system. As compared to the standard LQR, the proposed adaptive controller renders rapid transits in system's response with improved damping against oscillations, while maintaining its asymptotic-stability, under bounded exogenous disturbances.

This article is centered on the development of a robust position control and disturbance compensation strategy for a mechatronic aero-pendulum using the soft computing paradigm. The pendulum arm is rotated about its pivot via the thrust generated by two coaxial contra-rotating motorized propellers installed at its free end. The tracking error in arm’s angular position is fed to a multi-loop feedback controller. The proportional–integral–derivative (PID) controller, in the outer loop, stabilizes the arm at the reference position. The reference current control signals generated by the PID position controller are fed to two PI controllers, in the inner loop, that are responsible for regulating the current consumption of each motorized propeller. Initially, the fixed PID controller gains are evaluated by selecting the optimal value of the system’s closed-loop pole using the particle swarm optimization (PSO) algorithm. However, to mitigate the inefficacies of fixed gain controller and further enhance the system’s robustness against bounded exogenous disturbances and damping against oscillations, the closed-loop pole is dynamically adjusted via fuzzy inference system, after every sampling interval. The fuzzy membership functions are calibrated offline via PSO algorithm. The superior time optimal control behavior rendered by the proposed controller is validated by comparing its performance with fixed gain controller via credible real-time experiments.

The main contribution of this paper is to formulate a robust-adaptive and stable state-space speed control strategy for DC motors. The linear-quadratic-integral (LQI) controller is utilized as the baseline controller for optimal speed-regulation, accurate reference-tracking and elimination of steady-state fluctuations in the motor’s response. To reject the influence of modelling errors, the LQI controller is augmented with a Lyapunov-based model reference adaptation system (MRAS) that adaptively modulates the controller gains while maintaining the asymptotic stability of the controller. To further enhance the system’s robustness against parametric uncertainties, the adaptation gains of MRAS online gain-adjustment law are dynamically adjusted, after every sampling interval, using smooth hyperbolic functions of motor’s speed-error. This modification significantly improves the system’s response-speed and damping against oscillations, while ensuring its stability under all operating conditions. It dynamically re-configures the control-input trajectory to enhance the system’s immunity against the detrimental effects of random faults occurring in practical motorized systems such as bounded impulsive-disturbances, modelling errors, and abrupt load–torque variations. The efficacy of the proposed control strategy is validated by conducting credible hardware-in-the-loop experiments on QNET 2.0 DC Motor Board. The experimental results successfully validate the superior tracking accuracy and disturbance-rejection capability of the proposed control strategy as compared to other controller variants benchmarked in this article.

This article presents an intelligently optimised self-tuning fractional-order control scheme to improve the attitude-stabilisation of an inverted pendulum. Primarily, the scheme employs two Fractio...

This paper establishes the Firefly Algorithm (FA) to be a special variant of the conventional Accelerated Particle Swarm Optimization (APSO) technique by suggesting modifications in the movement criteria of the fireflies in case of the simple FA. In order to find the significance of this implication, a well known optimization problem known as the Economic Dispatch problem is computed for multiple test cases. Economic dispatch problem aims to minimize the total fuel cost of a multi-generator power system. In addition, the valve point effect loading is considered for the thermal cost equation in order to make the objective function more non-linear and non-convex in nature. The modified and enhanced FA not only improves its performance as compared to the conventional FA, but also presents enhanced FA as a special case of the APSO algorithm by giving the similar performance parameters as that of APSO. Moreover, a comprehensive statistical analysis based on the results of the independent t-test is presented in order to statistically compare the performance of APSO, FA and enhanced FA. The independent t-test results statistically prove enhanced FA to be a special variant of APSO technique by comparing the mean and the variance of the two algorithms for a particular sample size.

This paper presents an experimental comparison of four different hierarchical self-tuning regulatory control procedures in enhancing the robustness of the under-actuated systems against bounded exogenous disturbances. The proposed hierarchical control procedure augments the ubiquitous Linear-Quadratic-Regulator (LQR) with an online reconfiguration block that acts as a superior regulator to dynamically adjust the critical weighting-factors of LQR’s quadratic-performance-index (QPI). The Algebraic-Riccati-Equation (ARE) uses these updated weighting-factors to re-compute the optimal control problem, after every sampling interval, to deliver time-varying state-feedback gains. This article experimentally compares four state-of-the-art rule-based online adaptation mechanisms that dynamically restructure the constituent blocks of the ARE. The proposed hierarchical control procedures are synthesized by self-adjusting the (i) controller’s degree-of-stability, (ii) the control-weighting-factor of QPI, (iii) the state-weighting-factors of QPI as a function of “state-error-phases”, and (iv) the state-weighting-factors of QPI as a function of “state-error-magnitudes”. Each adaptation mechanism is formulated via pre-calibrated hyperbolic scaling functions that are driven by state-error-variations. The implications of each mechanism on the controller’s behaviour are analyzed in real-time by conducting credible hardware-in-the-loop experiments on the QNET Rotary-Pendulum setup. The rotary pendulum is chosen as the benchmark platform owing to its under-actuated configuration and kinematic instability. The experimental outcomes indicate that the latter self-adaptive controller demonstrates superior adaptability and disturbances-rejection capability throughout the operating regime.

This paper suggests a hybrid Firefly-APSO algorithm which uses the global best position criteria of the Accelerated Particle Swarm Optimization technique in order to update the position of the fireflies in the search space of the given objective function. To validate the significance of the suggested hybrid firefly algorithm as compared to its counterpart, a well known optimization problem known as the Economic Dispatch problem is solved using both techniques. The main objective of the dispatch problem is to minimize the total thermal generation cost by determining the optimal power contribution of each thermal energy source while meeting the system constraints. In order to make the economic dispatch problem more practical, the valve point effect loading is considered which makes the quadratic cost function of the thermal generation highly non-linear and non-convex by adding an additional sinusoidal term in the thermal cost equation. The dispatch problem is computed for multiple test cases having different number of thermal generators and dynamic load demand using both algorithms. The suggested hybrid technique surpasses the conventional firefly algorithm by reducing the overall mean generation cost of the considered network while meeting the generation constraints of the system.

This paper presents a modified and novel form of the conventional short-term hydrothermal scheduling problem by incorporating the effects of adding the photovoltaic energy source to the conventional grid. A photovoltaic energy source is intermittent in nature, therefore, to determine the optimal power contribution of the photovoltaic source towards the economic dispatch problem, a detailed strategy is presented in this paper. The proposed design method includes the forecasting of the photovoltaic system’s parameters using the Auto-Regressive Integrated Moving Average (ARIMA) model. The analytical model is developed based on the fractional integral polynomials for studying the characteristics of the single photovoltaic module. The optimization of power allocation in the system consisting of conventional and non-conventional energy sources is highly non-linear and classical deterministic methods can not be guaranteed to determine the optimal solution for economic power dispatch. The global optima of such non-linear and non-convex problems can be determined using swarm-based intelligence techniques. This paper presents accelerated particle swarm optimization and the firefly algorithm to determine a solution for short-term non-linear scheduling problems. The multiple test cases have been presented to demonstrate the effectiveness of the proposed solution over classical methods. The overall generation cost of the selected hybrid system is reduced using the proposed methods while meeting the generation constraints of each energy source. Moreover, due to the stochastic nature of the meta-heuristic techniques, a comprehensive statistical comparison, based on the independent T-test results, is also presented to highlight the algorithm which performs better for selected scheduling problems. It has been demonstrated that accelerated particle swarm optimization gives lower mean generation cost of the system whereas the execution time of the firefly algorithm is better compared to its counterpart.

This paper addresses the recent advancements and trends in the field of electroencephalography (EEG) using inverse problem solutions. Using the EEG data of the brain to gather the informati...

This paper presents an adaptive intelligent cascade control strategy to maintain the dynamic stability of a ball-riding robot (BRR). The four-wheeled mechanism beneath the robot body balances it on a spherical wheel. The BRR is modeled as a combination of two decoupled inverted pendulums. Therefore, two independent controllers are used to control its pitch and roll rotations. An incremental proportional–integral–derivative (PID) is implemented in the inner loop of the cascade to maintain the vertical balance. A generic PD controller is used in the outer loop to keep the station by controlling its spatial position. The controller parameters are automatically tuned via a fuzzy adaptation mechanism. The centers of fuzzy output membership functions are dynamically updated via an extended Kalman filter (EKF). The proposed controller quickly responds to changes in system’s state and effectively rejects the exogenous disturbances. The results of real-time experiments are presented to validate the effecti...

This paper presents a robust adaptive nonlinear proportional--integral (ANPI) scheme to control the speed of a direct-current (DC) motor. Unlike proportional--integral--derivative (PID) controllers, PI controllers have a simpler structure and they deliver effective control effort. However, due to inadequate controller gains, they are often unable to simultaneously improve the transient as well as the steady-state response of the system. A nonlinear PI (NPI) controller alleviates these issues and delivers a good response. In this research, the proportional and integral gains of the NPI controller are dynamically modulated via a nonlinear sigmoidal function (SiF) of the error dynamics of the motor's angular speed. The variation rates of these functions are manually tuned via trial-and-error method. These rates are also dynamically updated via an extended Kalman filter (EKF)-based adaptation mechanism. The performances of a linear PI controller, an NPI controller having fixed variation rates, and an NPI controller equipped with the EKF-based self-regulated SiFs are tested and compared in real time. The experimental results are analyzed to validate the effectiveness of the proposed ANPI controller in optimizing DC motor speed control.

Upstream oil & gas heavy oil steam-flood operation, typically involves a high volume of year-round process equipment changes outside of scheduled shutdowns. This coupled with high workforce transition rate, presents a challenge in establishing and sustaining an effective MOC program. This work is focused on improving the MOC for surface facilities in a high activity brownfield operation. The objectives of this effort included taking the MOC program beyond regulatory requirements and to a proactive level. At high level, elements of the MOC improvement efforts included: Using Risk Based Process Safety (RBPS) approach to provide extra rigor to eligible "high focus" changes.Optimizing the MOC workflow for low risk high volume and "typical" changes.Influencing workforce behaviors to support proactive process safety (PS) culture.

Various risk assessment methodologies are used across the oil & gas industry to identify potential risks, for example Integrated Hazard Identification (IHAZID), Hazard and Operability (HAZOP) and Layers of Protection Analysis (LOPA) etc. Many times results of such detailed assessments reside with local SME functions (e.g. engineering, health environment and safety (HES) or process safety (PS) etc.) and are not widely communicated, understood and used by line functions e.g. operations, maintenance and different levels of organization to make risk based decisions. A "Facility Risk Profile" process has been developed and implemented for Chevron's San Joaquin Valley Business Unit (SJVBU) in California. The Facility Risk Profile is a methodology to systematically use information from various risk identification processes, to analyze and communicate results to various functions and levels of organization to allow risk based decision making. This methodology is especially useful for upstream oil & gas organizations where multiple geographically separated facilities are owned and operated by sub-organizations. Collection, comparison and communication of various types of risk information across sub-organizations would allow common ownership and resource prioritization. In summary this methodology comprises of: Common enterprise-wide risk assessment values and language that allows for the organization to effectively communicate assessed risks. Gathering, analyzing and grouping data from existing risk identification processes (e.g. iHAZID and HAZOP) to identify high consequence scenarios across multiple sub-organizations and facilities. Use gathered data to facilitate discussions of cross functional workgroups (e.g. drilling, pipeline, and abandonment etc.) and capture any perceived catastrophic process safety (PS) risks "not already identified". Facilitated sessions with leadership to prioritize high consequence risks across various sub-facilities and identify the top ten process safety risks for the business unit (BU). Use the top 10 BU PS risks as a BU risk profile to align annual Business Planning to prioritize resources and activities. Conduct this exercise at a regular frequency (annual or every other year) depending on the complexity of facilities, workforce turnover rate and activity levels (could be indicated by annual capital and operating $$ spend rates). Observed benefits of proposed Facility Risk Profile exercise include communication and ownership of high consequence risks across the BU leadership and refreshing the level of awareness to high consequence PS risks across the organization.

Petroleum play in North Pakistan comprises of naturally fractured reservoirs (NFR) both in clastics and carbonates. These fractures play key role in the determination of reservoir quality and production behavior of the formations and is a subject of interest for E & P companies particularly working in Kohat Plateau. In TAL Block, naturally fractured Lockhart formation has very low matrix porosity and fractures provide the main source for storage and fluid flow in it. It has also been observed that all the fracture sets in carbonate reservoirs do not contribute to flow. Flow variations have been observed with different fracture trends identified from open-hole logs based on their connectivity, fracture apertures and secondary porosity. This leads to the development of an integrated approach using existing information from open hole logs, image logs, formation tester and production logs to optimize the reservoir modeling, history matching and selection of future perforation intervals. In this paper, data from six wells located in different structures in TAL Block has been utilized to study the behavior of naturally fractured carbonate reservoir. During the study, it was observed that in some wells, the top part of Lockhart formation was contributing more to production while in others, the bottom part was more productive. These flow variations could be associated with different fracture trends, their connectivity, fracture apertures and secondary porosity. This problem was addressed by sub-dividing Lockhart in to different layers/facies based on fracture evaluation (types, density, secondary porosity, nodularity and rock texture), open hole logs, formation tester results and production logs interpretation.

This paper presents the synthesis of a linear-quadratic-regulator (LQR) augmented with a nonlinear-integral controller to optimize the speed regulation of a permanent magnet DC (PMDC) motor. The LQR utilizes the full state-feedback and minimizes the quadratic performance index to deliver optimal speed control decisions. However, the LQR lacks the ability to eliminate the steady-state error and damp the oscillations occurring during transients. For this purpose, the LQR is combined with an integral controller. In order to synergize the controller combination, the integral gain is dynamically varied via smooth hyperbolic secant function of error-dynamics in motor speed; such that, the contribution rendered by the integral-action increases as the response converges towards the reference speed and vice-versa. The results of real-time experiments are presented and analyzed to validate the effectiveness of the proposed controller.

This paper presents a robust adaptation mechanism for the orders of a fractional-order proportional-integral-derivative (PID) controller in order to optimally regulate the speed of a permanent magnet direct current motor. The fractional-number powers (order) of the integration and differentiation operator in the proposed controller are self-tuned in real-time using hyperbolic secant functions of error dynamics of motor's angular-speed. The proportional, integral and derivative gains of the fractional-order controller are meta-heuristically tuned via particle-swarm-optimization algorithm, and are kept fixed throughout the experimentation. The QNET DC Motor Control Trainer is used as a benchmark to experimentally compare the proposed control scheme with a fixed-gain generic PID controller as well as a fixed-gain and fixed-order fractional-order PID controller. The results of the ‘hardware-in-the-loop’ tests are presented to validate the robustness of the proposed speed controller.