Proportional Integral Derivative Controller Research Articles

PID-Based Temperature Stabilization for Silicon Photomultipliers Using Thermoelectric Cooling Devices

In this paper, we present a temperature stabilization system for Silicon Photomultipliers (SiPMs) using thermoelectric cooling devices controlled by a PID (Proportional-Integral-Derivative) controller. Maintaining a stable temperature for SiPMs and their associated front-end electronics is crucial for achieving consistent gain and noise minimization. Our system employs three CP1881-222 thermoelectric coolers per module and is managed by two ATMega1284p microcontrollers. One microcontroller handles the PID control routine, while the other functions as a TCP/IP client. The temperature is monitored using PT100 sensors connected via MAX31865 RTD converters, ensuring precise measurements. The PID algorithm efficiently corrects temperature deviations, maintaining an error margin of approximately ±0.2◦C. The system has been successfully deployed in the HASC subdetector of CERN’s NA62 experiment, where it maintains the SiPM temperature at 20.5◦C, enhancing the subdetector’s performance. The modular design of our PID controller system allows for adaptability to various experimental setups requiring precise temperature control.

An online error compensation strategy for hybrid robot based on grating feedback

Purpose This paper aims to enhance the machining accuracy of hybrid robots by treating the moving platform as the first joint of a serial robot for direct position measurement and integrating external grating sensors with motor encoders for real-time error compensation. Design/methodology/approach Initially, a spherical coordinate system is established using one linear and two circular grating sensors. This system enables direct acquisition of the moving platform’s position in the hybrid robot. Subsequently, during the coarse interpolation stage, the motor command for the next interpolation point is dynamically updated using error data from external grating sensors and motor encoders. Finally, fuzzy proportional integral derivative (PID) control is applied to maintain robot stability post-compensation. Findings Experiments were conducted on the TriMule-600 hybrid robot. The results indicate that the following errors of the five grating sensors are reduced by 94%, 93%, 80%, 75% and 88% respectively, after compensation. Using the fourth drive joint as an example, it was verified that fuzzy adaptive PID control performs better than traditional PID control. Practical implications The proposed online error compensation strategy significantly enhances the positional accuracy of the robot end, thereby improving the actual processing quality of the workpiece. Social implications This method presents a technique for achieving online error compensation in hybrid robots, which promotes the advancement of the manufacturing industry. Originality/value This paper proposes a cost-effective and practical method for online error compensation in hybrid robots using grating sensors, which contributes to the advancement of hybrid robot technology.

Neural network-based self-tuning control for hybrid electric vehicle engines

Combustion engine speed control faces significant uncertainties in parameters like mass equivalent coefficient and efficiency. To address these challenges, this research develops an adaptive, self-tuning artificial neural network (ANN) control design that aims to optimize engine speed despite load torque and model uncertainties. We integrate three controllers—linear quadratic regulator (LQR), proportional-integral-derivative (PID), and sliding mode controller (SMC)—with machine learning techniques to evaluate their performance in hybrid electric vehicle engines. The ANN-based self-tuning controller identifies the most effective method for maintaining idle engine speed with minimal deviation. The results indicate that the ANN can accurately predict settling times and enhance controller performance, leading to reduced settling time errors, improved fuel economy, and better emissions performance. The study highlights LQR's high sensitivity in the lower settling time range and reduced sensitivity in the higher range, the PID controller's varied sensitivity, and SMC's consistent sensitivity across both ranges. A well-trained ANN is essential for optimizing control parameters and maintaining system stability. The ANN was trained and validated using the nntool MATLAB toolbox, with tests conducted across lower and higher settling time ranges. Results showed that the PID controller's performance was overfitted by insufficient training in the lower range, while the LQR and SMC demonstrated potential for more efficient control in both ranges. Overall, the study underscores the importance of a proficiently trained ANN for achieving precise control in hybrid electric vehicle engines.

Design and performance investigation of a sliding-mode adaptive proportional–integral–derivative control for cable-breakage scenario

AbstractControlling a cable-driven parallel robot (CDPR) when a cable breaks is challenging. In this paper, a sliding mode adaptive PID control is designed to ensure a safe guidance of the load when a cable fails. Indeed, regardless when a cable breaks, this control makes it possible enchanting the guidance of the load inside the remaining wrench feasible workspace. In other words, it allows reducing the load oscillation and then increasing the safety of the recovery manoeuvre. Performances are evaluated through simulations by considering a spatial CDPR and comparing the results with a PID control.

A new optimal 3° of freedom fractional order proportion integral derivative controller with model predictive controller for frequency regulation in high penetrated renewable based interconnected system

The increasing use of renewable energy sources (RESs) in conventional power systems is making them more complex. RES, such as solar and wind energy, reduce system inertia, affecting power system (PS) stability and frequency aberrations. Thus, the modern PS faces new concerns that arise from integrating RESs and struggles to manage frequency and stability, which can cause power outages and blackouts and lower industry production. The intelligent control mechanism is critical to the PS's operation to ensure frequency under its complicated structure. This study suggests a new best 3-DoF fractional order proportional integral derivative-model predictive controller (3DoF-FOPIDN-MPC) for controlling frequency when there is a lot of load going on quickly. The 3DoF-FOPID controls the outer loop using the area control error (ACE) signal, while MPC controls the inner loop using the output signal from the 3DoF-FOPIDN and the area's total power output response. For optimal controller functionality, the salp swarm algorithm (SSA) extracts optimized controller settings. To validate the suggested controller, it is tested under various load-changing scenarios and compared to state-of-the-art controllers. For 1 % and 3 % load changes, the suggested controller settles in 1.1 s and 1.78 s, respectively.

Design and development of closed-loop controllers for trajectory tracking of a planar vibration-driven robot

Vibration-driven robots constitute an innovative paradigm for achieving locomotion, leveraging periodic vibrations to meticulously control the movement of an internal mass, thus affording them a high degree of precision while navigating surfaces with varying friction characteristics. This paper is dedicated to the refinement of trajectory tracking in planar vibration-driven robots, achieved through the meticulous design and implementation of a Proportional-Integral-Derivative (PID) controller and Sliding Mode Controller (SMC). The considered vibration-driven robot is propelled using two parallel reciprocating unbalanced masses which allows the robot to have various maneuvers in two dimensions. The movement of the robot is improved by employing bristles to make non-isotropic Coloumb’s friction on the surfaces. At first, the governing dynamic equations of the robot are derived by considering the stick-slip effect and using the Euler–Lagrange method. Moreover, a PID controller for accurate trajectory tracking within the robot’s natural coordinate system is designed and employed. The fine-tuning of the PID controller’s coefficients is accomplished through the application of the NSGA-II optimization method. Subsequently, a SMC strategy is introduced to enable the robot’s control in an absolute coordinate system. The paper culminates with the presentation, in-depth analysis, and evaluation of the simulation results, shedding light on the significant enhancements in performance and capabilities achieved by vibration-driven robots. In conclusion, the pivotal role of the NSGA II algorithm in optimizing controller parameters is emphasized, and although the PID controller excels in trajectory tracking, challenges with sudden acceleration changes are identified.

Experimental Validation of Truck Cab Suspension Model and Ride Comfort Improvement under Various Semi-Active Control Strategies

The semi-active cab suspension system for trucks is gaining increasing importance due to its economic advantages, low energy consumption, and significant enhancement of ride comfort. This paper investigates the effects of three control methods on improving ride comfort of semi-active cab suspension systems under random and bump road conditions: Proportional-Integral-Derivative (PID) control, fuzzy PID control, and Model Predictive Control (MPC). Initially, an accurate multi-degree-of-freedom truck cab suspension model was developed and validated through actual road tests. Based on this model, three control strategies were designed and implemented. Finally, the effectiveness of each control strategy was evaluated under various road conditions, including random and bump road scenarios. The results indicate that these control strategies can effectively reduce vibrations and impacts, significantly improving ride comfort. This improvement is crucial for alleviating driver fatigue and enhancing driving safety. Among them, the MPC control showed superior performance, reducing vibrations by at least 31% under both random and bump road conditions, outperforming PID and Fuzzy PID in terms of effectiveness and robustness.

TMS320F28379D microcontroller for speed control of permanent magnet direct current motor

<p><span>This paper aims to study the behavior of the <a name="_Hlk169245133"></a>proportional integral derivative (PID) and the fuzzy-based tuning PI-D controller for speed control of a permanent magnet direct current (PMDC) motor. The proposed method used a fuzzy-based tuning PI-D controller with a MATLAB/Simulink program to design and real-time implement a TMS320F28379D microcontroller for speed control of a PMDC motor. The performance of the study designed fuzzy-based tuning PI-D and PID controllers is compared and investigated. The fuzzy logic controller applies the controlling voltage based on motor speed errors. Finally, the result shows the fuzzy-based tuning PI-D controller approach has a minimum overshoot, and minimum transient and steady state parameters compared to the PID controller to control the speed of the motor. The PID controllers have poorer performance due to the non-linear features of the PMDC motor.</span></p>

Participation of wind-storage combined system in the secondary frequency regulation control strategy of power grid

Abstract The secondary frequency modulation of the unit, that is, the automatic generation control (AGC), is the process of increasing or decreasing the power of the power generation unit by the power plant unit according to the dispatching command issued by the power grid dispatching center, and then using the frequency modulator to restore the frequency. Based on the principle of AGC, this paper establishes a frequency dynamic response model of the secondary frequency modulation of the traditional unit and the wind storage participation system including load frequency control (LFC) and traditional proportional-integral-derivative (PID) controllers, determines the frequency modulation control strategy of wind storage, and uses the distribution mode based on Area Control Error (ACE) signal to assist in the secondary frequency modulation. In order to solve the parameter optimization problem of the classical PID controller when the wind-storage combined system participates in the secondary frequency modulation under the large load disturbance, a parameter optimization model of the controller with PID was established, and the ITAE evaluation index was finally selected as the fitness function by comparing different fitness evaluation indexes. To overcome the problem that the conventional Particle Swarm Optimization (PSO) is prone to local extremum, the inertia weights and learning factors are adjusted nonlinearly to balance and optimize the global and local optimization capabilities and improve the search efficiency, and the parameters of the PID controller are optimized by the improved PSO algorithm. The simulation results show that the proposed algorithm improves the secondary frequency modulation effect under the background of a high proportion of renewable energy connected to the power grid.

Speed Regulating of Wind Turbine Unit using Iterative Learning Control

Conventional control methods, such as Proportional Integral Derivative (PID) control, have been extensively utilized for speed regulation in renewable energy wind turbine units. However, the limitations and disadvantages of PID, including difficulties in handling nonlinearities and uncertainties inherent in wind turbine dynamics, necessitate the exploration of alternative approaches. This research paper proposes the adoption of Iterative Learning Control (ILC) as an intelligent controller for addressing the shortcomings of PID. By exploiting the repetitive nature of wind turbine operation, ILC offers the potential to enhance speed regulation performance by iteratively refining control actions based on past experiences for advancing renewable energy. Through simulation studies, the effectiveness of ILC in improving the transient response and tracking accuracy of wind turbine units is demonstrated.

Optimal nonlinear Fractional-Order Proportional-Integral-Derivative controller design using a novel hybrid atom search optimization for nonlinear Continuously stirred Tank reactor

Atom search optimization (ASO) algorithm derived from physics molecular dynamics. Lennard-Jones(L-J) and bond length potential of molecules are used to derive the model for optimization. In this paper, ASO is used for developing a nonlinear Fractional Order Proportional Integral Derivative controller (NL-FOPID) for Continuously Stirred Tank Reactor (CSTR). The convergence characteristics of ASO was improved by proposing a novel hybridization approach. The proposed hybridization approach called Hybrid ASO(HASO) guides the Atom search algorithm to optimally replace the atoms that goes out of the boundary of the search space. The designed algorithm is implemented to optimize various unimodal and multi model standard benchmark functions. From results obtained from this extensive simulation, it is indicated that proposed approach increased the convergence rate and also improved the optimization effort of conventional ASO. The proposed algorithm also tested with controller design for nonlinear CSTR. The NLPID and NL FOPID designed by HASO was better than conventional controllers found in the literature.

Model-driven design and validation of glucose supply control in a tubular photobioreactor operated under oxygen-balanced mixotrophy

Scaling up bioprocesses remains challenging due to the partially unpredictable and suboptimal behavior of microbes during scale transition. Mathematical modelling serves as a valuable tool in navigating these challenges. In this study, we developed and validated a kinetic model based on the tank-in-series approach for a novel bioprocess known as oxygen-balanced mixotrophy with the microalga Galdieria sulphuraria. This model aims to facilitate the design of an effective glucose feeding control strategy tailored for two-phase tubular photobioreactors (TPBRs). The residence time distribution of our reference reactor was investigated by means of a tracer experiment. Qualitative analysis of the results indicated that our system was optimally represented by simulations with a 100 stirred-tank reactors (STRs).The controlled variable, oxygen concentration in the gas phase, showed a downward trend in simulations with simplified conditions: fixed oxygen production and balanced glucose supply. The decrease was caused by dominant net heterotrophic activity and accumulation of CO2 in the gas phase. Different configurations of a proportional-integral-derivative (PID) controller were designed by means of the ultimate gain method and compared under these simplified conditions. The most effective PID controller was implemented and refined in simulations with outdoor weather conditions. The model was validated successfully by simulating over time in a lab-scale STR the spatial fluctuations happening in a TPBR. The empirical oxygen consumption and production were faster and steeper than in the model, probably due to inaccuracies in parameter estimation or biomass adaptation.

Optimizing PID control for enhanced stability of a 16‐DOF biped robot during ditch crossing

AbstractThe current research article discusses the design of a proportional–integral–derivative (PID) controller to obtain the optimal gait planning algorithm for a 16‐degrees‐of‐freedom biped robot while crossing the ditch. The gait planning algorithm integrates an initial posture, position, and desired trajectories of the robot's wrist, hip, and foot. A cubic polynomial trajectory is assigned for wrist, hip, and foot trajectories to generate the motion. The foot and wrist joint angles of the biped robot along the polynomial trajectory are obtained by using the inverse kinematics approach. Moreover, the dynamic balance margin was estimated by using the concept of the zero‐moment point. To enhance the smooth motion of the gait planner and reduce the error between two consecutive joint angles, the authors designed a PID controller for each joint of the biped robot. To design a PID controller, the dynamics of the biped robot are essential, and it was obtained using the Lagrange–Euler formulation. The gains, that is, KP, KD, and KI of the PID controller are tuned with nontraditional optimization algorithms, such as particle swarm optimization (PSO), differential evolution (DE), and compared with modified chaotic invasive weed optimization (MCIWO) algorithms. The result indicates that the MCIWO‐PID controller generates more dynamically balanced gaits when compared with the DE and PSO‐PID controllers.

Comparison of Proportional Integral Derivative Control and Model Predictive Control for Commercial Vehicles

Comparison of Proportional Integral Derivative Control and Model Predictive Control for Commercial Vehicles

A robust proportional filtered integral controller based on backpropagation neural network

This paper introduces a novel design of a proportional filtered-integral (P-FI) controller whose parameters are auto-tuned using the backpropagation neural networks (BPNN) algorithm. The proposed controller is designed to address the main challenges posed by a class of complex systems characterized by uncertain high-gain and pure integrator dynamics, including high-frequency noise amplification and poor robustness against parameter variations. Designing such a controller involves three main steps: First, a proportional–integral–derivative (PID) controller is designed, with its parameters auto-tuned online using the BPNN algorithm, resulting in the primary (BPNN-PID) controller. Second, the obtained parameters of the previous controller are utilized to compute those of a low-pass filter offline. This filter is then cascaded with the integral parts of a PID controller, forming a P-FI controller structure. This configuration introduces a phase lead within a specific frequency range without amplifying high-frequency noise, overcoming the primary disadvantage of the derivative term. Finally, the parameters of the resulting P-FI controller are again auto-tuned online using the BPNN algorithm, resulting in the final robust (BPNN-P-FI) controller. This novel controller structure and its parameters tuning procedure, based on a two-stage BPNN learning approach, constitute the main contributions of this paper. Simulation results demonstrate the superiority of the proposed controller in terms of time-domain performance, sensor noise attenuation, and closed-loop robustness compared to those obtained with the BPNN-PID and optimally tuned PID controllers.

Performance improvement of a DPC-FPID strategy with matrix converter using CDFIG in wind power system

This study focuses on optimizing energy extraction from Wind Power Generation Systems (WPGS) using a cascaded Doubly Fed Induction Generator (CDFIG) amidst variable wind speed operation. The aim of this study is to provide a new robust nonlinear control technique. The proposed method is founded on a fractional-order Proportional–Integral–Derivative controller (FPID). To well control the injection of both active and reactive power into the electrical grid and respond to the needs of consumers despite the changes in environmental conditions, a robust Direct Power Control (DPC) is employed with FPID (DPC-FPID) associated with Matrix Converter (MC). To extract the greatest aerodynamic power Maximum Power Point Tracking (MPPT), is employed. We compare the suggested method’s outcomes with traditional PID results in order to confirm and validate the findings. It can be argued that the DPC-FPID method exhibits superior performance compared to the DPC-PI method. In terms of Total Harmonic Distortion (THD), the outcomes indicate that the proposed technique utilizing FPID (1.20%) outperforms the conventional technique relying on PID (1.87%). The DPC-FPID approach gives a reduction in ripple values in comparison to the DPC-PI strategy in terms of ratios for the torque (6.25%), active power (14.28%), and reactive power (14.705%).

Innovative design for enhancing transient stability with an ATFOPID controller in hybrid power systems

In order to make multi-area power systems more efficient and stable, this study aims to create a new intelligent controller design based on adaptive property. The integrated power grid becomes more unstable as renewable energy sources become more prevalent, and system inertia decreases. Resultant, the penetration of renewable and load variance causes frequency changes to be more pronounced. Keeping frequency stable is essential for the electrical energy transmission system to work reliably and efficiently. This paper presents an adaptive design based on the tilt fractional order proportional integral derivative (TFOPID) controller by using an optimized property of bio inspired tunicate search algorithm (TSA). The gains of the TFOPID controller are continuously updated by utilizing the ancillary TPID controller, which is dependent on changes in area frequency. The suggested control system is implemented in the northern and southern regions, which consist of photovoltaic (PV), wind, redox flow battery (RFB), hydro, and thermal power plants. Therefore, this study contributes in the creation of more resilient and efficient power grids, which are critical for incorporating renewable energy sources and satisfying the increasing need for environmentally friendly power solutions.

Performance comparison between PID and Fuzzy logic controllers for the hardware implementation of traditional high voltage DC-DC boost converter

This research introduces a hardware implementation of DC-DC boost converter designed to elevate the DC voltage generated by renewable sources while effectively regulating it against line and load fluctuations for inverter application. The main objective is to boost the DC link voltage to the level of Vmax in the output AC voltage obtained from inverter circuits. This enables the inverters for transformer-less power conversion from DC to AC to reduce magnetic losses, size and weight of the inverter circuits used in the utility application. The proposed converter's topology and switching sequences play a crucial role in enhancing overall performance. Utilizing a Zero Current Switching (ZCS) technique, the converter efficiently recovers stored energy from the magnetics. The proposed converter attained the output voltage of 350 V at its current of 1A from the input voltage of 20 V at its current of 19 A. The ZCS technique and the topology of the converter enhances the efficiency to 92 %. The study employs traditional Proportional-Integral (PI) and Proportional-Integral-Derivative (PID) controllers for effective voltage regulation, analysing time domain specifications. Additionally, a Fuzzy logic controller is introduced as an alternative to PID controllers to compare their performance metrics, evaluating the optimization of the converter's transient and steady-state behaviours. The proposed converter is designed, simulated and their performance metrics are analysed using MATLAB for both with and without controllers. The step-time characteristics of the proposed converter with load resistance of RL = 500 Ω and an input voltage of Vi = 20 V has been determined and analysed. The PID system attained a rise time of 88.781 ms, an overshoot value of 9.341 %, and a steady-state error of 0.00043. The fuzzy system achieved a low-rise time of 10.624 ms, a low overshoot of 0.55 %, and a steady-state error of 0.0584. The hardware prototype of the proposed converter is implemented with a FPGA based PID and Fuzzy logic controllers for providing better voltage regulation and to improve the performance metrics of the converter. The simulation and experimental findings are contrasted, examined, and confirmed to ensure improved consistency in performance measures.

The control method of a quadrotor driven by bidirectional electronic speed controllers

In this paper, a dynamic quadrotor unmanned aircraft vehicle driven by bidirectional electronic speed controllers is proposed to enhance maneuverability and stability. Bidirectional electronic speed controllers are applied to achieve rapid deceleration of motors during flight. To match with bidirectional electronic speed controllers, fractional order Proportional-Integral-Derivative (PID) controllers are considered to attain better rapidity compared to PID controllers, and an innovative control allocation matrix with direction symbols is developed. The model, controllers, and allocation methods have been proven an effective scheme in simulations of attitude and position tracking.

Real-time simulation of ship overtake maneuvering at different water depths with a ship motion controller

This study aimed to predict ship trajectories in deep and shallow waters in real-time by properly accounting for the effects of hydrodynamic interactions on ship maneuvering. Ship maneuverability and motion control were investigated under hydrodynamic interactions arising from the effects of water depth, ship speed, and lateral distance between interacting ships on parallel courses. The Maneuvering Modeling Group (MMG) model was used, and the hydrodynamic interaction effect was estimated using an efficient three-dimensional potential-flow code that can run in real time. The influence of water depth on ship maneuverability was analyzed. The ship velocity response to the hydrodynamic interactions and maneuvering motion was simulated during the overtaking process of two ships at a range of water depths, lateral distances, and speeds. The findings suggest that, in shallow waters, changing the heading of the ship becomes more difficult, and the course deviation associated with the overtaking maneuver of two ships also increases. A conventional proportional-integral-derivative (PID) controller was employed for course-keeping. When the ratio of the water depth to ship draft was less than 2.0, the PID controller continued to perform effectively for the overtaking ship. For the overtaken ship, the effectiveness of the controller was diminished owing to the relatively low rudder effect at slow speeds.