Minimum Overshoot Research Articles

Detailed modelling and LQG\LTR control of a 2-DOF radial active magnetic bearing for rigid rotor

The paper presents detailed modelling of rotor, electro-magnetic actuators, and accompanying electronics of an in-house-developed active magnetic bearing (AMB) test rig in a unified manner. The work is the extension of our previous work on a 1-degree-of-freedom (DOF) cantilever beam while in here, the challenging problem of levitating and spinning a 2 DOF rotor on a pair of opposing EMs is investigated. Classical three-term PID controllers are designed as a benchmark controllers, in frequency domain to stabilize the 2-DOF open-loop unstable system, and the performance is compared with the modern state-space control strategies, namely Linear Quadratic Gaussian (LQG) and combined LQG-Loop Transfer Recovery (LQG\LTR) compensators. The tuned robust controllers are deployed on a custom-designed hardware using Simulink Real-Time software. Dual axes controllers performed reasonably well when subjected to static and dynamic tests both in simulation and in experimentation. The key time domain performance objectives such as reference tracking, disturbance rejection, and minimal control efforts as well as frequency domain stability margins are accomplished. The validity of the developed model is thus confirmed through successful synthesis and implementation of both PID and LQG\LTR controllers, wherein the robust controller (LQG\LTR) outperformed the benchmark controller in terms of performance, i.e. minimal overshoot, superior disturbance rejection capabilities, and improved loop gain characteristics. The AMB actuator also showed reasonable dynamic behaviour when the levitated rotor was spun at 1000 r/min. The dynamic performance was evaluated through a two-dimensional rotor orbit plot. The study goes beyond theory and underscores practical design considerations and demonstrates real-time controller implementation.

Sigmoid PID based adaptive safe experimentation dynamics algorithm of portable duodopa pump for Parkinson’s disease patients

This paper emphasizes on the development of an appropriate closed-loop control strategy for traditional portable duodopa pump (PDP); thereby ensuring an automated drug infusion without wearing off. In particular, a sigmoid proportional integral derivative (SPID) controller is adopted to control the blood plasma level of dopamine. The parameters of SPID controller are tuned using the adaptive safe experimentation dynamics (ASED) algorithm. The efficiency of the suggested SPID-ASED is evaluated by concerning the convergence plot of the cost function, the amount of dopamine in the blood plasma (BP) of the patient, the statistical analysis of cost function, norm of error and norm of input, and time responses specifications. The simulation results show that the proposed SPID-ASED outperforms the standard PID controller in terms of better control accuracy with minimum overshoot and settling time.

OPTIMAL DESIGN PSS-PID CONTROL ON SINGLE MACHINE INFINITE BUS USING ANT COLONY OPTIMIZATION

Optimization of the controller in a generator can improve system performance. The right parameter optimization is needed to get the optimal performance from the controller. The application of the artificial intelligence method as a parameter optimization method is proposed in this study. By using the smart method based on Ant Colony, the optimal PSS-PID parameters are obtained. With optimal tuning, the system gets optimal Single Machine Infinite Bus (SMIB) system frequency and rotor angle response, indicated by the minimum overshot system response. The SMIB system's stability will be tested. A case study of adding and reducing loads is used, with the proposed control method PSS-PID being optimized using Ant Colony. Based on the analysis using the proposed PSS-PID control, we get the minimum overshoot for the frequency response and rotor angle of the SMIB system. When the load changes at 20 seconds, using the PSS-PID control scheme, the minimum overshoot is -4.316e-06 to 7.598e-05 pu with a settling time of 22.01s. For the rotor angle overshoot response, using the PSS-PID control scheme, the minimum overshoot is -0.01113 to -0.009669 pu with a settling time of 22.36s.

Simulation of electro-mechanical friction clutch control using proportional derivative plus conditional integral control scheme for automotive application

This paper explains simulation works carried out to evaluate the performance of proportional-integral-derivative-based (PID-based) controls in controlling the electro-mechanical friction clutch (EMFC) for engagement and disengagement. The EMFC model is developed in Matlab/Simulink comprising DC motor's model and power screw mechanism's model. Four controls; proportional-integral (PI), proportional-integral-derivative (PID), proportional (P) and proportional derivative (PD), are applied on the model using 6 mm as the set point for the power screw's position. Among them, PD control performs the best with 0% overshoot, 0.041 mm steady state error and about 1.3 seconds settling time. Next, the PD control is updated with a conditional integral controller (PDPCI), resulting in approximately zero steady state error and only 0.68% overshoot, while settling time stays at 1.3 seconds. Subsequently, both PD and PDPCI controls are simulated to achieve EMFC's full engagement and disengagement. The final results show that PDPCI control performs the best with minimum overshoot.

Simulation of electro-mechanical friction clutch control using proportional derivative plus conditional integral control scheme for automotive application

This paper explains simulation works carried out to evaluate the performance of proportional-integral-derivative-based (PID-based) controls in controlling the electro-mechanical friction clutch (EMFC) for engagement and disengagement. The EMFC model is developed in Matlab/Simulink comprising DC motor's model and power screw mechanism's model. Four controls; proportional-integral (PI), proportional-integral-derivative (PID), proportional (P) and proportional derivative (PD), are applied on the model using 6 mm as the set point for the power screw's position. Among them, PD control performs the best with 0% overshoot, 0.041 mm steady state error and about 1.3 seconds settling time. Next, the PD control is updated with a conditional integral controller (PDPCI), resulting in approximately zero steady state error and only 0.68% overshoot, while settling time stays at 1.3 seconds. Subsequently, both PD and PDPCI controls are simulated to achieve EMFC's full engagement and disengagement. The final results show that PDPCI control performs the best with minimum overshoot.

Hybrid Optimization Technique for Enhancing the Stability of Inverted Pendulum System

The inverted pendulum system (IPS) is considered the milestone of many robotic-based industries. In this paper, a new variant of variable structure adaptive fuzzy (VSAF) is used with new reduced linear quadratic regulator (RLQR) and feedforward gain for enhancing the stability of IPS. The optimal determining of VSAF parameters as well as Q and R matrices of RLQR are obtained by using a modified grey wolf optimizer with adaptive constants property via particle swarm optimization technique (GWO/PSO-AC). A comparison between the hybrid GWO/PSO-AC and classical GWO/PSO based on multi-objective function is provided to justify the superiority of the proposed technique. The IPS equipped with the hybrid GWO/PSO-AC-based controllers has minimum settling time, rise time, undershoot, and overshoot results for the two system outputs (cart position and pendulum angle). The system is subjected to robustness tests to ensure that the system can cope with small as well as significant disturbances.

A novel super-cooling enhancement method for a two-stage thermoelectric cooler using integrated triangular-square current pulses

In this work, a novel concept of continuous cooling has been proposed to enhance the super-cooling performance of a two-stage thermoelectric cooler, in which integrated triangular-square pulses are used with their modes separately controlled in the upper and lower stages. The 3-dimensionl modeling of the thermoelectric cooler is developed to simulate the super-cooling performance. The non-dominated Sorting Genetic Algorithm II (NSGA-II) is adopted to perform the multi-parameters optimization of pulses forms, in which twelve influencing parameters including pulse amplitude and width, several time constants such as the interval time and input time of pulses are selected as the searching variables, and maximum effective cooling zone with minimum overshoot temperature is set as the multi-objective function. The optimal designs are proved to be better in super-cooling performance for both constant and variable leg cross-sectional area, owing to their well mutual cooperation of the upper and lower stage pulses. Compared to the conventional design with completely identical imposed current form, the effective cooling zone can be improved up to 72.41% by the optimal design when the cooling load is fixed at 0 kW m-2, and the corresponding temperature overshoot can be decreased by 56.48% from 20.29 K to 8.83 K.

Centralized Fuzzy Logic Based Optimization of PI Controllers for VSC Control in MTDC Network

Advancements in the field of power electronics led to global changes in the electrical energy generation, transmission, and distribution. The voltage source converter (VSC) based HVDC system is the future of the power system due to the advantages it offers in terms of renewable power generation, transmission, and integration. Currently, the two-terminal VSC–HVDC systems have been successfully commissioned. Multi-terminal VSC–HVDC system is the developing trend for higher power reliability, large scale integration, and smart operation. The performance of a VSC based multi-terminal direct current (MTDC) system greatly depends upon the tuning of the controller. Standard practice is to tune the PI controller using hit and trial method or based on the operator’s experience. However, the tuning process becomes complex when multiple grids are involved. Thus, for MTDC systems, the manual tuning of the controller does not yield the desired results. This paper presents a centralized fuzzy logic-based optimization technique for VSC control of the MTDC system to obtain the optimized parameters for the PI controllers. The optimized parameters ensure a better system performance in terms of fast settling time, better slew rate, minimum undershoot, and minimum overshoot response. The proposed technique is tested on a three-terminal MTDC network in SIMULINK / MATLAB.

Dynamic Modeling and Control of a Coupled Reforming/Combustor System for the Production of H2 via Hydrocarbon-Based Fuels

The present work aims to provide insights into the dynamic operation of a coupled reformer/combustion unit that can utilize a variety of saturated hydrocarbons (HCs) with 1–4 C atoms towards H2 production (along with CO2). Within this concept, a preselected HC-based feedstock enters a steam reforming reactor for the production of H2 via a series of catalytic reactions, whereas a sequential postprocessing unit (water gas shift reactor) is then utilized to increase H2 purity and minimize CO. The core unit of the overall system is the combustor that is coupled with the reformer reactor and continuously provides heat (a) for sustaining the prevailing endothermic reforming reactions and (b) for the process feed streams. The dynamic model as it is initially developed, consists of ordinary differential equations that capture the main physicochemical phenomena taking place at each subsystem (energy and mass balances) and is compared against available thermodynamic data (temperature and concentration). Further on, a distributed control scheme based on PID (Proportional–Integral–Derivative) controllers (each one tuned via Ziegler–Nichols/Z-N methodology) is applied and a set of case studies is formulated. The aim of the control scheme is to maintain the selected process-controlled variables within their predefined set-points, despite the emergence of sudden disturbances. It was revealed that the accurately tuned controllers lead to (a) a quick start-up operation, (b) minimum overshoot (especially regarding the sensitive reactor temperature), (c) zero offset from the desired operating set-points, and (d) quick settling during disturbance emergence.

Balancing a Segway robot using LQR controller based on genetic and bacteria foraging optimization algorithms

A two-wheeled single seat Segway robot is a special kind of wheeled mobile robot, using it as a human transporter system needs applying a robust control system to overcome its inherent unstable problem. The mathematical model of the system dynamics is derived and then state space formulation for the system is presented to enable design state feedback controller scheme. In this research, an optimal control system based on linear quadratic regulator (LQR) technique is proposed to stabilize the mobile robot. The LQR controller is designed to control the position and yaw rotation of the two-wheeled vehicle. The proposed balancing robot system is validated by simulating the LQR using Matlab software. Two tuning methods, genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) are used to obtain optimal values for controller parameters. A comparison between the performance of both controllers GA-LQR and BFO-LQR is achieved based on the standard control criteria which includes rise time, maximum overshoot, settling time and control input of the system. Simulation results suggest that the BFOA-LQR controller can be adopted to balance the Segway robot with minimal overshoot and oscillation frequency.

Deadbeat PQ Control for Islanded Smart Grids

The dynamic performance of smart (micro)grids depends on the proper selection of the controller gains and power-sharing parameters. This manuscript describes the control design to achieve a deadbeat desirable performance in terms of: i) Zero steady-state error. ii) Minimum rise time. iii) Minimum settling time. iv) Less than 2% overshoot/undershoot. This paper considers an Islanded microgrid system composed of two distributed generation (DG) units. Each DG unit includes three-phase pulse width modulation (PWM) inverter. The proposed controllers are proportional- integral (PI) type. The Controllers gains of the inverters and the Phase Locked Loop (PLL) parameters are designed to guarantee deadbeat dynamic performance in terms of minimal overshoot and system stability. The Particle Swarm Optimization (PSO) is used to tune the controller parameters of the current, PQ loops, and the PLL. The proposed controllers are compared with the traditional (Ziegler and Nichols), auto-tuned, and interior-point methods to shows the excellence of the proposed technique. Results authenticate and endorse the effectiveness of the proposed controllers and PLL design technique to achieve the desired deadbeat response of the study microgrid system.

Design of Satellite Attitude Control Systems using Adaptive Neural Networks

This paper investigates the performance of adaptive neural networks through simulations for satellite systems involving three-axis attitude control algorithms. PID tuning is the method employed traditionally. An optimally tuned, to minimizes the deviation from set point. It also responds quickly to the disturbances with some minimal overshoot. However, the disadvantage of poor performance has been observed in these controllers when manual tuning is used which in itself a monotonous process is. The PID controller using Ziegler-Nichols has more transient responses of satellite such as Overshoot, Settling time, and Steady state errors. For overcome this technique, the proposed analysis implemented an Adaptive Neural Network with PID tuning. The paper aims to combine two feedback methods by using neural networks. These methods are feed- forward and error feedback adaptive control. The research work is expected to reveal the inside working of these neural network controllers for state and error feedback input states. An error driven adaptive control systems is produced, when the neural networks acquire the knowledge of slopes and gains regarding the error feedback, while, with state feedback the system will keep trying to approximate a stable approach in order to stabilize the attitude of the satellite.

Implementation of a neural network MPC for heat exchanger network temperature control

Heat exchanger network (HEN) control is considered to be a difficult task due to its nonlinear behavior, complexity, presence of disturbances and noise. The optimal operation of HEN implies the implementation of a robust control system able to overcome all these issues. This work presents an advanced technique based on an artificial neural network model predictive control (NNMPC) and compares the controlling performance to three widely used controllers: two PID and a linear MPC. The goal is to control an outlet oil stream temperature of a HEN containing four heat exchangers in counter-current. A lumped parameter model was used to describe the HEN system. The data generated were used to train a neural network in the Matlab environment using the Levenberg-Marquardt algorithm. Closed-loop negative and positive step responses were simulated to investigate set point tracking and disturbance rejection. The ability to handle constraints was evidenced in MPC, as well as faster settling time and minimum overshoot.

Design of Smart Fuzzy Logic Controller in Quad Buck-Boost DC DC Converter with Constant Input and Output Current

In this paper, the design of a fuzzy logic controller based quad buck-boost converter with constant input and output current is presented. In contrast with the ordinary step-down-up converters the advanced converter with same duty cycle, a large limit of voltage conversion ratio could be acquired. A proposed converter is designed and studied with an intelligent controller i.e. fuzzy logic controller/FLC and differentiated with the ordinary Proportional-Integral /PIC controller. Finally, the analysis of the presented converter by employing MATLAB/Simulink, simulation results of the presented converter is recorded to substantiate the effectualness and rationality of the output voltage control of Quad buck-boost converter in both step-down-up mode and its comparative analysis is presented where the fuzzy logic has a minimum overshoot and settling time compared to regular PI controller.

Enhanced Nature-Inspired Meta-Heuristic Algorithm for Microgrid Performance Improvement

In this article, a new selection technique based on Enhanced Nature-Inspired Meta-Heuristic (ENIMH) optimization algorithm is presented to improve the Microgrid (MG) dynamic performance. Interconnected microgrids have the ability to provide a clean and sustainable energy during normal and emergency operating conditions. The concerned microgrid includes hybrid renewable energy sources (RES) and energy storages systems (ESS). MG achieves a reduced dependency on the electric grid and provides flexible and adaptive energy supply. This paper develops a new selection technique based on ENIMH optimization that distinguishes the degrees of resemblance between the best individual and other individuals of current population. This technique proposes a binary coding of individuals, and is compared to conventional techniques; it allows each individual to occupy a section of the modified roulette wheel selection for the calculated degree of resemblance. This enhanced optimization technique tunes the dynamic PID parameters of microgrid closed loop system. The designed strategy is dependably to locate the arrangement of enhanced parameters to minimize the system frequency fluctuations in the microgrid and to provide the improved dynamic performance by being sensitive to variations for closed loop response under various power and load conditions. The proposed technique has been demonstrated using Matlab/Simulink simulation on the underlined microgrid, where the achieved results confirm the effectiveness of proposed selection method for the reproduction of best individuals to show the improved performance. The proposed technique achieved satisfactory performance for PID-controllers, and provided a good closed loop performance, minimum overshoot and minimum fitness index, in comparison with other well-established methods. The results emphasize that ENIMH optimization algorithm has the exploration and exploitation capability of population best individuals to accomplish the best solutions.

Enhanced Composite Nonlinear Control Technique using Adaptive Control for Nonlinear Delayed Systems

Background and Introduction: The proposed control law is designed to provide fast reference tracking with minimal overshoot and to minimize the effect of unknown nonlinearities and external disturbances. Methods: In this work, an enhanced composite nonlinear feedback technique using adaptive control is developed for a nonlinear delayed system subjected to input saturation and exogenous disturbances. It ensures that the plant response is not affected by adverse effect of actuator saturation, unknown time delay and unknown nonlinearities/ disturbances. The analysis of stability is done by Lyapunov-Krasovskii functional that guarantees asymptotical stability. Results: The proposed control law is validated by its implementation on exothermic chemical reactor. MATLAB figures are provided to compare the results. Conclusion: The simulation results of the proposed controller are compared with the conventional composite nonlinear feedback control which illustrates the efficiency of the proposed controller.

DESIGN OF OPTIMAL PID CONTROLLER FOR THREE PHASE INDUCTION MOTOR BASED ON ANT COLONY OPTIMIZATION

Speed control of an induction motor is an important part of the operation of an induction motor. One method of regulating motor speed is the addition of a PID controller. PID parameters must be tuned properly to get the optimal speed. In this study, the PID controller tuning method uses an artificial intelligence method based on Ant Colony Optimization (ACO). ACO algorithm in an intelligent algorithm that is inspired by the behavior of ants looking for food sources in groups with traces of feromone left behind. In this study, food sources are represented as optimal parameters of PID. From the computational results obtained optimal parameters respectively, P (Proportional) 0.5359, I (Integral) 0.1173, D (Derivative) 0.0427. ACO computing found the optimal parameters in the 21st iteration with a minimum fitness function of 11.8914. Case studies are used with two variations of the speed of the induction motor input. With optimal tuning, the performance of the induction motor is increasing, marked by a minimum overshoot of 1.08 pu and a speed variation of both overshoots of 1,201 pu, whereas without control 1.49 pu and 1.28 pu, as well as with PID trial control of 1.22 pu and 1.23 pu respectively. The benefits of this research can be used as a reference for the operation of induction motors, by tuning the Ant Colony intelligent method for the PID controller in real-time with the addition of microcontroller components.

Minimal overshoot V-F control for islanded microgrids

Islanded microgrids' dynamics are greatly affected by the controllers' gains and power-sharing parameters. This paper considers an islanded microgrid containing two distributed generation (DG) units. Each DG has its pulse width modulated (PWM) inverter for three phases. Each inverter has two control loops in cascade. The inner loop is a current loop and the outer loop is a voltage loop. The controller of each loop is designed to provide good dynamic performance in terms of minimal percentage overshoot and overall system stability. The particle swarm optimisation (PSO) is used to design the current and voltage controllers to achieve the desired performance. The proposed controllers are compared with the conventional technique (Ziegler and Nichols), which shows the excellence of the proposed technique. Different test scenarios as voltage and current tracking and load parameters variation are used to examine the proposed design.

Minimal overshoot V-F control for islanded microgrids

Islanded microgrids' dynamics are greatly affected by the controllers' gains and power-sharing parameters. This paper considers an islanded microgrid containing two distributed generation (DG) units. Each DG has its pulse width modulated (PWM) inverter for three phases. Each inverter has two control loops in cascade. The inner loop is a current loop and the outer loop is a voltage loop. The controller of each loop is designed to provide good dynamic performance in terms of minimal percentage overshoot and overall system stability. The particle swarm optimisation (PSO) is used to design the current and voltage controllers to achieve the desired performance. The proposed controllers are compared with the conventional technique (Ziegler and Nichols), which shows the excellence of the proposed technique. Different test scenarios as voltage and current tracking and load parameters variation are used to examine the proposed design.

OPTIMAL PARAMETER TUNING FOR PID CONTROLLER USING ACCELERATED GREY WOLF OPTIMIZATION IN SMART SENSOR ENVIRONMENTS

System lifetime is the crucial problem of wireless sensor networks (WSNs), and exploiting environmental energy provides a potential solution. Boost convertor can be employed in WSN to achieve energy efficiency. In order to achieve better performance, PID controller is combined with boost convertor. However, tuning the PID controller is crucial task whenever the input voltage fluctuates. In this work, a novel algorithm namely accelerated grey wolf optimisation (AGWO) is proposed to improve the convergence speed and to eradicate the local optima stagnation. AGWO algorithm utilises a balanced intensification and diversification techniques to eradicate the local optima struck. The observed results conveys that AGWO achieves minimum percentage overshoot (9%), settling time (0.894), rise time (0.50) and peak time (0.57) which is better compared to other comparative algorithms. Additionally, it has been observed that AGWO is able to achieve comparatively better success performance in a complex environment.