PI Controller Research Articles

A Novel PI-Based Control Structure with Additional Feedback from Torsional Torque and Its Derivative for Damping Torsional Vibrations

This paper presents issues related to the damping of torsional vibrations in a system with elastic coupling. A novel PI-based control structure with additional feedback from torsional torque and its derivative is proposed. For the estimation of the required variables, the integral observer is proposed. Analytical expressions are presented to enable the selection of parameters of the control system. The relationship between the considered system and popular structures with a PI controller and one additional feedback from torsional torque and the derivative of torsional torque is pointed out. The proposed control structure is tested under simulation and experimental studies.

Developing a Risk Management Framework for Agricultural Water Systems Using Fuzzy Dynamic Bayesian Networks and Decision-Making Models

AbstractGiven the various natural and human-caused hazards that threaten the agricultural water distribution process from the main source to farms, establishing a framework to analyze these risks is crucial. This study aims to develop an intelligent risk management framework to help stakeholders devise long-term and sustainable solutions for managing agricultural water systems. First, we developed a Fuzzy Dynamic Bayesian Network (FDBN) model for multi-hazard risk assessment, taking into account the temporal causal interactions between parameters and incorporating fuzzy theory. Next, we defined several risk management scenarios across structural, non-structural, automated control, and integrated methods. These scenarios were implemented in the FDBN model to mitigate the risks associated with the system. Various economic, social, environmental, and technical criteria were considered, and scenarios were ranked using the WASPAS, TOPSIS, and MultiMoora methods. The Copeland approach was used to combine the ranking results. The results showed that automated scenarios, specifically Model Predictive Control (MPC) and Proportional-Integral (PI) controllers, could reduce the system's risk by 11.4% and 9.8%, respectively, and were ranked the highest. The findings of this study and the proposed framework can assist operators in the sustainable planning and management of water systems in light of anticipated threats.

Advanced controller design based on interval type-2 fuzzy neural network for elementary super-lift Luo converter

The elementary super-lift Luo converter is a third-order DC/DC step-up converter developed using the super-lift method that increases input voltage with low current and voltage ripples and a high voltage gain. On account of the nonlinearity and uncertainty incorporated with voltage, load changes, and switching operation of elementary super-lift Luo (ESLL) converter, it is a challenging task to design an efficient controller. To overcome these problems, this study proposes a new control approach based on interval type-2 fuzzy neural network (IT2FNN) to regulate a DC output current and voltage of ESLL converter. The parameters of IT2FNN were trained offline using a gradient descent algorithm. Mean square error (MSE), one of the most used statistical performance indicators, was used to evaluate the convergence accuracy performance of gradient descent algorithm. The DC output voltage regulation performance of IT2FNN was evaluated via a comparative simulation with interval type-2 fuzzy controller (IT2FC) and proportional + integral (PI) controller in MATLAB/Simulink environment in terms of settling time, average computational time, and recovery time under three different operational conditions: various reference voltages, input voltage, and load. Moreover, the DC output current regulation performance of IT2FNN was evaluated via a comparative simulation with IT2FC and PI controller in MATLAB/Simulink environment in terms of settling time and steady-state error under various reference current changes. The detailed simulation results obtained from comparative simulation validated the effectiveness of IT2FNN.

A Novel Feedforward Scheme for Enhancing Dynamic Performance of Vector-Controlled Dual Active Bridge Converter with Dual Phase Shift Modulation for Fast Battery Charging Systems

This paper proposes a novel feedforward control scheme to achieve a very smooth transition from Constant Current (CC) to Constant Voltage (CV) charging modes, the commonly used method for electric vehicle charging applications. Furthermore, a three-loop model-independent Linear Active Disturbance Rejection Control (LADRC)-based system is proposed, replacing the traditional two-loop Proportional-Integral (PI) control system. The extra loop performs a decoupled dq vector control of the inductor current, which is typically not used in single-phase Dual Active Bridge (DAB) systems. This additional loop not only facilitates the optimal determination of both internal and external phase shift angles of a Dual-Phase Shift (DPS) modulator but also lowers the peak input current of the converter, allowing for lower-rated switches. Numerical simulations using MATLAB/Simulink demonstrate the robustness of the proposed control strategy against both input voltage disturbances and load disturbances during the transition from CC to CV charging modes. Hence, the dynamic performance of the charging system is significantly improved with minimal controller effort.

A maiden application of an MGO-tuned cascade controller for the secondary frequency control in biogas-powered marine shipboard microgrid

The use of renewable energy sources in marine microgrids has become crucial to limiting the emissions from the shipping industry. Hence, sources like biogas, solar photo-voltaic, sea wave energy, etc. are being integrated rapidly into marine microgrids. In this study, a novel implementation of cascaded controllers like 1 + PI-TID and 1 + PD-PI is modelled for the secondary load frequency control of the shipboard system. The proposed controllers are then tuned with a newly developed metaheuristic algorithm i.e. mountain gazelle optimisation. Moreover, a comparative study is done with other newly developed algorithms. The performance of these controllers is checked in diverse scenarios. The proposed method gives a 23.79% and 54.07% increase stability for the conventional PI controllers. Furthermore, for studying cyber-attacks in the proposed system, various durations of communication delay are introduced and its stability is evaluated. Finally, sensitivity and uncertainty analysis is performed for the validation of the effectiveness of the proposed controller.

A Mixed Approach for Clock Synchronization in Distributed Data Acquisition Systems.

Proper timing synchronization is important when data from sensors are acquired by different devices. This paper proposes a simple but effective solution for System on Chip (SoC) architectures that integrates a general-purpose Field Programmable Gate Array (FPGA) with a CPU. The proposed approach relies on a network synchronization protocol implemented in software, such as Network Time Protocol (NTP) or Precision Time Protocol (PTP), and uses the FPGA to generate a clock reference that is maintained in step with the synchronized system clock. The clock generated by the FPGA is obtained from the FPGA oscillator via appropriate fractional clock division. Clock drift is avoided via a software program that periodically compares the FPGA and the system counters, respectively, and adjusts the fractional clock divider in order to slightly adjust the FPGA clock frequency using a Proportional Integral controller. A specific implementation is presented on the RedPitaya platform, generating a 1 MHz clock in step with the NTP synchronized system clock. The presented system has been used in a distributed data acquisition system for fast transient recording in the neutral beam test facility for the ITER nuclear fusion experiment.

Analysis of a Simplified Predictive Function Control Formulation Using First Order Transfer Function for Adaptive Cruise Control

This paper presents a formulation and analysis of a low computation Predictive Functional Control (PFC), which is a simplified version of the more advanced Model Predictive Control (MPC) for an Adaptive Cruise Control (ACC) system by using a representation of first order closed-loop transfer function. In this work, a non-linear mathematical model of vehicle longitudinal dynamics is considered as a control plant. Then, a simple Proportional Integral (PI) controller is employed as an inner loop to identify the first-order relationship between its actual and desired trajectory speed according to the reasonable time constant based on the logical response of pedals pressing. To directly control the whole plant, the PFC is formulated as an outer loop to track the desired speed together with the convergence rate based on a user preference while satisfying constraints related to acceleration and safe distancing. Since PFC is formulated based on the first-order transfer function, the prediction and tuning processes are straightforward and specific to this system. The simulation results confirm that the proposed controller managed to track the desired speed while maintaining a comfortable driving response. Besides, the controller also can retain safe distancing during the car following application, even in the presence of unmeasured disturbance. In summary, this framework can avoid the need to formulate an inverse non-linear model that is typically used when deploying a hierarchical control structure to compute the throttle and brake pedals pressing as it has been replaced with an inner loop PI controller. The performance also is comparable yet more conservative due to the simplification. These findings can become a good reference for designing and improving the ACC controller, as the framework can be easily generalized for any type of vehicle for future work.

Stability of step size control based on a posteriori error estimates

A posteriori error estimates based on residuals can be used for reliable error control of numerical methods. Here, we consider them in the context of ordinary differential equations and Runge-Kutta methods. In particular, we take the approach of Dedner & Giesselmann (2016) and investigate it when used to select the time step size. We focus on step size control stability when combined with explicit Runge-Kutta methods and demonstrate that a standard I controller is unstable while more advanced PI and PID controllers can be designed to be stable. We compare the stability properties of residual-based estimators and classical error estimators based on an embedded Runge-Kutta method both analytically and in numerical experiments.

TCP BBR-n interplay with modern AQM in Wireless-N/AC networks: Quest for the golden pair.

Effective congestion control on the internet has been a problem since its inception. Transmission Control Protocol (TCP), being the most widely used transport layer protocol tries to mitigate it using a variety of congestion control algorithms. Cubic, Reno, and Bottleneck Bandwidth and Round-trip propagation time (BBR) are the most deployed congestion controls. BBR v2 is leading the congestion control race with its superior performance in terms of better throughput and lower latency. Furthermore, Active Queue Management (AQM) algorithms try to mitigate the congestion control at the network layer through active buffer control to avoid bufferbloat. The most efficient congestion control occurs when TCP and AQM work together. Indeed, it is the TCP-AQM algorithm "Golden pair" that can result in the most efficient performance. This paper proposes such a novel pair based on our previously tested and published BBR-n (BBR new) with the most effective of the modern AQMs, that completely gels together to provide lower latency in wireless networks based on Wireless N/AC. Real-time experiments were performed using Flent on our physical testbed with BBR-n and modern AQMs such as Fair Queuing (FQ), Constrained Delay (CoDel), Proportional Integral controller Enhanced (PIE), Common Applications Kept Enhanced (Cake) and Flow Queuing Controlled Delay (FQ_CoDel). Various tests done on our physical testbed helped us identify CAKE as the most optimum AQM that fits with our proposed BBR-n while providing optimum throughput and lower latency in 802.11N/AC-based wireless networks.

Performance analysis of IMC-PID controller on PCT-14 air pressure control system

This research presents a comprehensive performance analysis of the IMC-PID controller implemented in the PCT-14 air pressure control system, a platform ideal for experimenting with various control methodologies. The study aims to compare the effectiveness of the IMC-PID controller with IMC-PIDF, Good Gain - PID and Skogestad's - PI controllers, focusing on response time characteristics based on the simulation of the PCT-14′s transfer function. Performance analysis is conducted by comparing system response characteristics to set point changes, including rise time, overshoot, settling time, peak time, and steady-state error. To validate the controller's effectiveness, performance index value such as Integrated Absolute Error (IAE) was calculated. The research findings indicate that the IMC-based PID controller outperforms the Good Gain – PID, Skogestad-PI, and IMC-PIDF controllers in handling set point changes.•Simulating the transfer function of the PCT-14 air pressure control system to examine the response time characteristics of the IMC-PID, IMC-PIDF, Good Gain - PID, and Skogestad's - PI controllers.•Conducting a performance analysis by comparing system response characteristics in response to changes in set point values.•Calculating performance index value, IAE to assess the controllers' effectiveness.

Design and implementation of Golden Eagle optimized cascaded PI and LQR controller for PFC SEPIC converter in EV charging

This research article focuses on designing and implementing a single ended primary inductor converter (SEPIC) for an AC-DC converter, followed by an electric vehicle (EV) charger. It improves the high-power factor at the AC supply with minimum harmonic distortion. The Golden Eagle optimization techniques are adapted to optimize the tuning of Proportional-Integral (PI) and Linear Quadratic Regulator (LQR) controller parameters for better converter performance. The optimization is designed based on the eagle knowledge of hunting methods at various angles of spiral trajectories in capturing the animal. The SEPIC converter is designed and derived from the state space model by state space averaging, and the reduced model is obtained through the moment matching method to reduce computational complexity. The Golden Eagle Optimize the parameters of KP and KI of the PI controller and weighing matrix Q of the linear quadratic controller. The fitness function of the proposed optimization is the sum of the Integral Absolute error (IAE) and Integral Square error (ISE). The proposed optimization is implemented using MATLAB/SIMULINK software, and the simulation outcomes demonstrate an improved settling time, fast recovery against input and output variations, Total Harmonic Distortion (THD) of 1.75 %, and enhanced stability.

Robust model-based control and stability analysis of PMSM drive with DC-link voltage and parameter variations

To ensure a stable operation of Permanent Magnet Synchronous Motor (PMSM) drive under both DC link voltage and parameter variations, a robust control technique based on a new dynamic model that includes both drive’s and motor’s specifications is proposed in this paper. In the proposed controller, the first component of the drive’s control law consists of the d-component error of the stator current, and the second one is shaped based on the error in the square value of q-component of the stator current. To further deal with the dynamic alterations of the drive system, compensators are designed to reduce the adverse effects of rotor angular frequency variations and the difference between electrical and load torque errors. Another compensator based on the drive’s output power error is also placed at the q-component of the proposed control law. Moreover, a general operation curve (GOC) for the stator current is introduced to further assess the operation of the PMSM. In the next step, a comprehensive stability analysis verifying the stable operation of both d- and q-components of the stator current is performed using two closed-loop descriptions of the proposed control strategy. Several simulation results in MATLAB/SIMULINK environment are provided to verify the validity of the proposed control technique under various dynamic scenarios. It is worth mentioning that comparative results show that the proposed control technique compared to conventional PI controller has enabled the PMSM speed and torque to reach its 50% and 95% of their desirable values with respectively 42.9% and 28.6% less time. Also, the PMSM speed and torque responses due to proposed control technique have 75% less undershoot compared to conventional PI controller.

Low Vibration Control Scheme for Permanent Magnet Motor Based on Resonance Controllers

For an electric locomotive traction motor, it is necessary to maintain relatively low vibration and noise due to the higher design standards. By using effective motor control strategies and implementing current harmonic suppression schemes, motor efficiency and vibration and noise suppression can be effectively improved. This study investigates the current harmonic suppression strategy for permanent magnet synchronous motors by (1) constructing a mathematical model of the permanent magnet motor to explore the sources of low-order harmonics currents such as fifth and seventh harmonics, as well as high-order harmonics at switch frequencies and their multiples, and analyzing the electromagnetic force characteristics generated by the current, and (2) establishing a vector control system for the permanent magnet motor. To suppress the fifth and seventh harmonic components in the current, a resonance controller is constructed, which utilizes the parallel connection of a resonator and PI controller to achieve low-order harmonic suppression. The factors affecting the effectiveness of the resonance controller’s suppression are also analyzed. The experiments are conducted, and the current harmonic suppression scheme constructed in this study can effectively reduce the harmonics in the current, thereby reducing motor vibration and noise.

Artificial intelligence-driven control for enhancing carbon dioxide-based wastewater pH regulation in tubular reactor

Alkaline wastewater treatment using carbon dioxide can reduce chemical costs and provide a safer alternative to traditional methods. However, complex gas-liquid reactions and narrow operating pH ranges present challenges. This research develops an artificial intelligence-driven control system for treating alkaline wastewater using carbon dioxide in a bench-scale tubular reactor. The proposed control system employs an inverse neural network to regulate the carbon dioxide gas based on the desired setpoint, along with a Smith predictor and a linear controller to compensate for natural delays, model mismatches, and pH disturbances. The inverse neural controller was trained using experimental data from a bench-scale reactor pH treatment of synthetic alkaline wastewater and verified on real influent from an electroplating wastewater treatment plant. The results show that the proposed method efficiently enforces the desired reactor outlet pH setpoint with up to 51.36% faster settling time than a proportional-integral controller while improving pH-adjusting efficiency by 72.24%.

Modeling and control of heating and heat circulation in direct air capture system

Direct air capture (DAC) is a critical technology for mitigating climate change. However, the high heat consumption of temperature vacuum swing adsorption (TVSA)-based DAC processes hinders its widespread deployment. This study focuses on developing a control strategy to optimize the energy efficiency of the TVSA heating phase. A novel adsorbent temperature estimation method, validated through experimental data, was integrated into a cascaded PI controller with a fuzzy gain scheduler (FGS). Experimental results demonstrate that the proposed control strategy effectively regulates the heating process, achieving a potential energy saving of up to 14%. This work contributes to enhancing the feasibility and sustainability of DAC technologies.

PARALLEL PLATFORM CONTROLLER BASED ON ADAPTIVE DIFFERENCE ALGORITHM – PART 2

There are two main approaches to motion control on parallel platforms: joint space control and workspace control. Joint space control is an easy-to-implement semi-closed-loop strategy, but its control effect could be better. The workspace control is to obtain the real-time position of the parallel platform through the forward solution and close the speed and position loop of the parallel platform in the workspace. This paper uses a Model Predictive Controller (MPC) to control the parallel platform with workspace control as the research goal. The loss function is constructed based on the swarm intelligence optimization idea, and the adaptive difference algorithm is used to optimize the parameters of MPC. This part uses MATLAB to perform simulation experiments to complete the S-shaped velocity trajectory planning algorithm. In addition, the control effect of MPC and position-loop PI controller in a robust disturbance environment is compared. Experiments show that MPC has the advantages of low energy consumption and high control accuracy.

AC loss optimization of high temperature superconducting magnetic energy storage considering energy management strategies in a hydrogen-battery system

Hydrogen-battery systems have great potential to be used in the propulsion system of electric ships. High temperature superconducting magnetic energy storage (HTS-SMES) has the advantages of high-power density, fast response, and high efficiency, which greatly reduce the dynamic power response of hydrogen-battery systems. Although a superconductor has zero-resistance during direct current operation, considerable alternating current (AC) loss is generated in superconducting coils during charging and discharging operations, which reduces the overall efficiency of the system and increases the quench risk of superconducting coils. Therefore, it is essential to analyze and optimize the AC loss of SMES during the design and optimization of a hydrogen-battery-SMES system. In this work, the AC losses of SMES in a hydrogen-battery-SMES system is studied under three energy management strategies, proportional-integral (PI) control, fuzzy logic, and the equivalent hydrogen consumption minimization strategy. The results show that the high fluctuation of load power can cause significant increases of AC losses in SMES. The fuzzy logic management method has the lowest AC loss among the three strategies. An orthogonal experimental method is presented to optimize the SMES structure. A control strategy based on a neural network is proposed to minimize the AC loss in SMES. Compared with PI control, the SMES AC loss can be reduced by up to 64.2 % using the proposed control scheme.

The comparison study of PI and Sliding Mode control techniques for Buck-Boost converters

This paper compares the performance of a Sliding Mode Control (SMC) and Proportional-Integral (PI) controller under different voltage and load conditions. The study's findings underscore the need to account for load variations in system designs and to continuously seek system optimization irrespective of the controller type chosen. PI controller demonstrates effectiveness under certain circumstances. However, significant drops in voltage and current during abrupt load changes are obtained. On the other hand, SMC enables superior adaptability, efficiently managing voltage transients and load variations without any electrical disturbances, thereby maintaining system stability. A comparative analysis further emphasizes the SMC's superior time response and robustness against reference voltage changes. Consequently, SMC is proven to be a preferable choice over the PI controller in systems experiencing frequent voltage and load variations. However, both controllers achieve the potential for further response time optimization and stability.

Revolutionizing PV grid integration: Metaheuristic optimization of fractional PI controllers in T-type neutral point piloted inverters for enhanced performance

This study investigates the efficacy of FOPI regulators as a substitute for conventional proportional integral (PI) controllers in grid-connected PV inverters. The research's objective is to improve the dynamic efficiency of these systems by integrating intelligent optimization techniques that utilize both PI and FOPI controllers and employing different metaheuristic optimization procedures. The study introduces four new optimization algorithms: the Tyrannosaurus Optimization Algorithm (TROA), the Nutcracker Optimization Algorithm (NOA), the Golden Eagle Optimizer (GEO), and the Jellyfish Search Optimizer (JSO). These are made to meet the needs of multi-objective optimization. The proposal suggests using two PI/FOPI regulators to regulate both voltage and amperage provided by the multilayer inverter. The proposal employs a T-type three-level inverter, known for its superior conversion efficiency over conventional inverters. Matlab-Simulink simulations demonstrate that the Nutcracker optimization algorithm (NOA) outperforms other metaheuristic techniques for optimizing dynamic behavior, such as overshoot, settling time, execution, and rising time. Comparisons with existing methods, such as the Manta Rays Foraging Optimization (MRFO) and Grey Wolf Optimizer (GWO), show that all four new algorithms consistently outperform these existing algorithms. The data suggest that using NOA improves stability, efficiency, and power factor while reducing the inverter's THD.

Control of thermal uniformity in microwave heating process by BPNN and adaptive particle swarm optimization

As China advances its green economy, innovative methods are being employed to enhance energy optimization and conservation within energy-intensive industries. Among these methods, microwave heating stands out due to its superior heating efficiency and lack of pollution. Nonetheless, uniform heating remains a challenge because the microwave absorption capacity of the heated medium varies with changes in heating time and temperature. To address this issue, an Adaptive Particle Swarm Optimization (APSO) neural network microwave system based on the Back Propagation Neural Network (BPNN) is proposed. This system leverages the fundamental principles of Particle Swarm Optimization (PSO), categorizing particle swarms into three types and iterating them through distinct processes to achieve optimal results. An APSO controller is designed based on system identification, adjusting the controller parameters according to the error between the real-time system output and the identification model. The feedback error is used as the fitness function in the PSO algorithm, continuously adjusting the weights and thresholds of the neural network. This intelligent control approach optimizes the microwave oven's input power to minimize the error between the actual temperature output and the identified temperature. The APSO controller is designed based on system identification, with the intelligently controlled microwave heating system adjusting its input power to minimize the error between the identified and actual temperature outputs. Unlike traditional Proportional Integral Differential (PID) and BPNN controllers, this approach calculates the output of the identified model and the error of the actual model, feeding this information back to the controller. The feedback error serves as the fitness function in the PSO algorithm, enabling continuous adjustment of the network's weights and thresholds to regulate the microwave equipment's output power, thereby ensuring the output temperature closely follows the preset temperature curve. Experimental results demonstrate that the actual output value of the system closely aligns with the original preset temperature curve, with a root mean square error of only 0.74. This designed identification and control system effectively achieves the desired outcome.