Controller Tuning Research Articles

Widely Tunable Repetition-Rate Control of Optical Pulse Trains Using a Fixed Dispersive Medium

Widely Tunable Repetition-Rate Control of Optical Pulse Trains Using a Fixed Dispersive Medium

Analog real time tunable and configurable fractional order PID controller realization

Fractional-order controllers are modeled using irrational continuous-time transfer functions. Implementing these controllers in analogue form can be challenging, particularly if their parameters need to be adjusted in real-time. This paper presents an analogue implementation of a fractional PID controller that can be adjusted and configured in real-time. The controller can be configured as PIαDf, PIDβ, PIα, PDβ, Iα, Dβ. The signal within the proposed controller’s circuit stays analog and is not converted into discrete values. A prototype of the controller is tested. The results of the frequency and time domain validations indicated similarity between the theoretical, simulated and experimental measurements.

Efficient Impedance Controller Tuning of Robot–Environment Interaction With Improved Transient and Steady-State Performance

Efficient Impedance Controller Tuning of Robot–Environment Interaction With Improved Transient and Steady-State Performance

Comparative Investigation of DSP-Based Speed Control of PMSM Using Proportional Integral and Takagi-Sugeno Fuzzy Logic Controller

This paper presents the simulation and experimental results of speed control of a three-phase Permanent Magnet Synchronous Motor (PMSM). The control techniques employed in this work include a Proportional Integral (PI) controller and a Takagi-Sugeno Fuzzy Logic Controller (TS-FLC). While theory and simulation help in predicting the PI controller settings for real drive implementation, further adjustment of the PI controller coefficients is still needed for optimal performance. However, the complexity of PI controller tuning can be overcome by employing a fuzzy logic controller, which is less affected by variations in model parameters and load torque. Two variants of the proposed TS-FLC are implemented: a simple algorithm with 9 rules and a standard algorithm with 49 rules. The drive system utilizes Field Oriented Control (FOC). The control program is developed using the C/C++ programming language and executed on the Texas Instruments TMS320LF28335 Digital Signal Processor (DSP), known for its intelligent motor control capabilities. The drive system evaluation was performed through simulations and experimentation using the MCK28335 professional development kit provided by Technosoft Company. The simulation and experimental results of the proposed controllers are compared under various conditions, including speed reversal and load torque variations.

Enhanced Randomized Harris Hawk Optimization of PI controller for power flow control in the microgrid with the PV-wind-battery system

Microgrids, characterized by their ability to work individually or in combination with the main power system, play a pivotal role in addressing the growing demand for reliable and sustainable energy solutions. This work concentrates on the integration of sustainable energy sources, specifically photovoltaic (PV), and wind generation and a battery storage system within a microgrid framework. Additionally, a power flow control strategy is implemented to enhance the dynamic behaviour and stability of the microgrid. The proportional-integral (PI) controller is a fundamental component in regulating the microgrid’s power flow, ensuring optimal performance under varying operating conditions. However, tuning the PI controller parameters is a difficult task because of the dynamic and nonlinear nature of renewable energy sources. In this work, the application of the Enhanced Randomized Harris Hawk Optimization (ERHHO) to fine-tune the PI controller is proposed, using the algorithm’s ability to mimic the hunting behaviour of hawks in finding optimal solutions. The PV-Wind-Battery microgrid system is modelled, and the proposed algorithm is employed to optimize the PI controller parameters for efficient energy management. The ERHHO algorithm’s exploration-exploitation balance is harnessed to navigate the complex solution space and converge to optimal PI controller settings, thereby enhancing the microgrid’s stability and performance. The study evaluates the effectiveness of the proposed ERHHO-based PI controller tuning through comprehensive simulations. Performance metrics such as transient response, overshoot, settling time, and steady-state error are analysed to validate the robustness and efficiency of the proposed method. Compared to its nearest optimization algorithm, with the proposed algorithm rise time is reduced by 50%, overshoot is reduced by 75%, settling time is reduced by 66%, and finally, a percentage of reduction of steady-state error is 45%. The outcomes of this research contribute to the advancement of microgrid control strategies, offering a novel approach to PI controller tuning in the context of diverse renewable energy sources. The integration of the Harris Hawk Optimization algorithm provides a promising avenue for enhancing the operational efficiency and reliability of microgrids, paving the way for sustainable and resilient energy systems in the aspect of growing energy landscapes.

Model Parametrisation and Rule Selection for Problem-tailored PID Autotuning

When employing measured input/output data to determine the parameters of a process model and then exploiting that model to tune a controller, the used parametrisation procedure can exert a significant (and often overlooked) influence on the obtained results. In this paper we propose a methodology to select the best combination of model parametrisation procedure and (PID) controller tuning rule depending on the nature of the control problem to address, characterised by conveniently defined quality indices.

Rapid identification of bacterial isolates using microfluidic adaptive channels and multiplexed fluorescence microscopy.

We demonstrate the rapid capture, enrichment, and identification of bacterial pathogens using Adaptive Channel Bacterial Capture (ACBC) devices. Using controlled tuning of device backpressure in polydimethylsiloxane (PDMS) devices, we enable the controlled formation of capture regions capable of trapping bacteria from low cell density samples with near 100% capture efficiency. The technical demands to prepare such devices are much lower compared to conventional methods for bacterial trapping and can be achieved with simple benchtop fabrication methods. We demonstrate the capture and identification of seven species of bacteria with bacterial concentrations lower than 1000 cells per mL, including common Gram-negative and Gram-positive pathogens such as Escherichia coli and Staphylococcus aureus. We further demonstrate that species identification of the trapped bacteria can be undertaken in the order of one-hour using multiplexed 16S rRNA-FISH with identification accuracies of 70-98% with unsupervised classification methods across 7 species of bacteria. Finally, by using the bacterial capture capabilities of the ACBC chip with an ultra-rapid antimicrobial susceptibility testing method employing fluorescence imaging and convolutional neural network (CNN) classification, we demonstrate that we can use the ACBC chip as an imaging flow cytometer that can predict the antibiotic susceptibility of E. coli cells after identification.

Multiobjective Robust PI Synthesis in Plants with Uncertain Poles

The use of multiobjective optimization procedures for control tuning has been constantly explored, presenting satisfactory results, and generating significant contributions to control engineering. In this way, the field of robust control can benefit significantly from the integration of multiobjective optimization design procedures for robust Proportional-Integral-Derivative (RPID) controller synthesis, enabling innovative solutions to complex control challenges. Due to this fact, this paper explores an approach to utilize multiobjective optimization algorithms in a simplified procedure, considering uncertain systems in the form of interval plants with uncertain poles, the Kharitonov theorem is used to obtain a region of stability as search space for the multiobjective algorithm, being the optimization process performed over a nominal system model. Two numerical cases and a flexible actuator model were used to demonstrate the procedure for RPI synthesis, resulting in robustly stable controllers with optimized performance over error and control signal effort measures.

PID/FOPID Tuning Rule Based on a Second Order Inverse Response Process Model with Robustness Considerations

This paper deals with the design of a proportional-integral-derivative (PID) and fractional-order proportional-integral-derivative (FOPID) controller tuning rule for second-order controlled processes with inverse response. The design of the rules takes into account specific levels of robustness and performance for both load disturbance rejection and set-point tracking tasks. In the first step, the optimal parameters for both controllers are obtained from optimizations that seek to minimize the error in both control modes while maintaining the robustness constraint. Subsequently, fitting functions to the optimal parameters are sought to capture their behavior with the possibility of finding optimal parameters with the tuning rule designed between certain ranges of the model parameters. For FOPID and PID controllers, the rule IRM-RoT (Inverse Response Model Robust Tuning) have been developed. The performance of the controllers was compared using examples, and the results showed that fractional order controllers provide a novel solution for inverse response dynamics in process control.

Analysis and design of PI-based controllers for the axes of European Solar Telescope

European Solar Telescope (EST) represents the largest European research infrastructure projected in the field of solar astrophysics. It is a 4-meter class solar telescope currently in the design phase. Within this phase, an essential objective is to analyze the control strategy of the telescope axes to achieve an accurate sun trajectory tracking. This paper presents a cascade control scheme based on proportional-integral (PI) controllers specifically designed for the elevation and azimuth axes of EST. The control strategy, which employs nested position-velocity loops, is devised to adeptly track the sun’s trajectory while mitigating wind impact. The controller tuning adheres to criteria of relative stability margins and bandwidth constraints using the state-space representation derived from Finite Element Model of the EST structure. An end-to-end dynamics model has been proposed to evaluate the performance of the control strategy during a ten-minute simulation. Results evidenced the convenience of the proposed controllers, highlighting wind as the main source of the control error (0.78 arcsec and 0.14 arcsec in azimuth and elevation axes, respectively) compared to the sun tracking error. Future control strategy enhancements should target the reduction of wind-induced effects to meet stringent image stability requirements.

ETF dead-time compensation based multiloop control approach for multivariable processes

Due to significant interaction dynamics and time delays in multivariable processes, designing a well-tuned PI controller is always challenging. When tuning multi-loop PI controllers, effective open-loop transfer function should be used instead of the diagonal transfer function element. The complexity of the effective open-loop transfer function (EOTF) increase rapidly for high-dimensional multivariable processes. Therefore, in this manuscript, it is proposed to use equivalent transfer functions (ETFs) under perfect control approximation. Subsequently, a dead-time compensation (DTC) scheme is used for ETFs and PI controllers are tuned. DTC can facilitate eliminating the impact due to time delay from the multivariable process, making the task of tuning PI controllers relatively less complex. The proposed multi-loop control topology minimizes the controller effort, and a good controller design for a multivariable process can be achieved. The PI controller parameters for dead-time compensated ETFs are calculated using simple IMC PI tuning rules, where the filter factor is taken based on the paired element relative gain value. To examine the effectiveness of the proposed controller tuning approach, the proposed control strategy was applied to a wide range of multivariable processes. Compared to state-of-the-art control strategies, the proposed control strategy provides superior closed-loop performance indices, even when there is a significant plant-model mismatch.

Genetic PI Controller Tuning to Emulate a Pole Assignment Design

Tuning of proportional-integral (PI) and proportional-integral-derivative (PID) controllers continues to be a current topic, as the control needs in industry are broad and diverse. Although PID controllers have been the predominant controller type for several decades, there are still opportunities to improve the performance of both the installed base and newly deployed controllers. One of the main challenges is a reliable and easy tuning that can be performed by an operator on site. From a computer science point of view, the problem of tuning PID controllers qualifies as a nondeterministic polynomial-time hard problem (NP-hard). Genetic algorithms are a heuristic approach to approximate this type of problem, and the continued growth of computational capacity makes them increasingly more viable. In this work, we present a PID tuning architecture using a genetic algorithm, which incorporates in its fitness function an emulation of pole assignment and implicit cancellation of the additive dynamics of zeros in a closed loop, in addition to reducing discretization losses. The tuning is performed with a paradigm different from the one usually applied in the literature. Rather than simply minimizing the error between the process variable and the setpoint, the error between the process variable and an ideal response curve associated with a desired pole assignment is minimized. This approach provides better control over closed-loop performance.

Iterative Control Tuning: With Application to MIMO Precision Motion Systems

Iterative Control Tuning: With Application to MIMO Precision Motion Systems

FUZZY FRACTIONAL ORDER SLIDING MODE CONTROL FOR OPTIMAL ENERGY CONSUMPTION-TRANSIENT RESPONSE TRADE-OFF IN ROBOTIC SYSTEMS

This work presents a new fuzzy logic parameter tuning-based fractional order sliding mode control (F-FOSMC) strategy for the trajectory tracking of robotic systems. The primary objective is to achieve an optimal control compromise seeking to minimize energy consumption for required motions, all while increasing both accuracy and rapidity of the transient response, addressing therefore a critical challenge in the field of robotics. Our approach integrates a sophisticated fuzzy logic fractional order mechanism to dynamically fine-tune the noninteger derivation parameter of the FOSMC, offering adaptability to varying operational conditions. This intelligent fuzzy logic controller tuning optimizes the FOSMC performance, demonstrates a remarkable capacity to handle uncertainties and disturbances intrinsic to robotic applications and allows to optimize the trade-off between the energy consumption and transient response efficiency throughout the robot’s motion. The effectiveness of the proposed study is thoroughly examined via simulations conducted on a 3 DOF manipulator robotic system. Results showcase that F-FOSMC significantly reduces control energy consumption while preserving rapid and efficient transient response characteristics.

Methyl-β-cyclodextrin asymmetrically extracts phospholipid from bilayers, granting tunable control over differential stress in lipid vesicles.

Lipid asymmetry - that is, a nonuniform lipid distribution between the leaflets of a bilayer - is a ubiquitous feature of biomembranes and is implicated in several cellular phenomena. Differential tension - that is, unequal lateral monolayer tensions comparing the leaflets of a bilayer- is closely associated with lipid asymmetry underlying these varied roles. Because differential tension is not directly measurable in combination with the fact that common methods to adjust this quantity grant only semi-quantitative control over it, a detailed understanding of lipid asymmetry and differential tension are impeded. To overcome these challenges, we leveraged reversible complexation of phospholipid by methyl-β-cyclodextrin (mbCD) to tune the direction and magnitude of lipid asymmetry in synthetic vesicles. Lipid asymmetry generated in our study induced (i) vesicle shape changes and (ii) gel-liquid phase coexistence in 1-component vesicles. By applying mass-action considerations to interpret our findings, we discuss how this approach provides access to phospholipid thermodynamic potentials in bilayers containing lipid asymmetry (which are coupled to the differential tension of a bilayer). Because lipid asymmetry yielded by our approach is (i) tunable and (ii) maintained over minute to hour timescales, we anticipate that this approach will be a valuable addition to the experimental toolbox for systematic investigation into the biophysical role(s) of lipid asymmetry (and differential tension).

Origami-inspired metamaterial with compression–twist coupling effect for low-frequency vibration isolation

Through unique folding methods and material selection, origami structures enable optimum structural design within a restricted space and provide strong force support. In aerospace engineering, origami-inspired structures are used in designing satellites, probes and other space devices for lightweight, compact and adjustable design requirements. A compression-twist coupled metamaterial inspired by Kresling origami structure consisting of beams and plates is presented in this study, and the vibration response of the structure is investigated. The bandgaps of the structure along longitudinal and transverse directions are studied separately due to the application of the polarization factor. The vibrational modes of the structure are further investigated to elucidate the mechanism of bandgap generation. Parametric studies make the optimal design of bandgap in graded structures possible. Finally, experiments are carried out to verify the correctness of the structural dynamic response. This research provides a new perspective on the design of origami-inspired metamaterial structures in terms of dynamics control and bandgap tuning.

Surrogate indirect adaptive controller tuning based on polynomial response surface method and bioinspired optimization: Application to the brushless direct current motor controller

The increment of autonomous systems has stimulated the research of new controller tuning techniques to face the unpredictable disturbances and parametric uncertainties inherent in any autonomous system that affect its performance. The indirect adaptive controller tuning approach based on the general dynamic model (IACTA-GDM) and bioinspired optimization is one of the most successful elections facing parametric uncertainties and disturbances, which are intricate to handle by other controller tuning techniques. However, this controller tuning approach is limited by the complexity of the dynamic model due to the computational burden, restricting its application to relatively small systems or systems with slow responses where the tuning is updated at large time intervals. The present work proposes a novel surrogate indirect adaptive controller tuning approach based on the response surface method (SIACTA-RSM) to address computational burden limitations. The proposal is tested on the speed regulation controller of a brushless direct current motor, with the aim of reducing the speed regulation error and the control system’s power consumption. The closed-loop system performance and the required computational time obtained by the proposed SIACTA-RSM are compared to the ones of a well-established IACTA-GDM. The descriptive and inferential statistics, as well as graphical comparisons, show that the system performance obtained by the SIACTA-RSM proposal is as competitive as the IACTA-GDM approach, keeping a mean difference among the results by up to 3.18% while reducing the computational burden of IACTA-GDM by up to 90%. These outcomes show that the SIACTA-RSM proposal is a reliable alternative to overcome the computational burden limitations that affect the IACTA-GDM approach while maintaining competitive performance.

An Adaptive PID Control System for the Attitude and Altitude Control of a Quadcopter

Abstract In adaptive model-based control systems, determining the appropriate controller gain is a complex and time-consuming task due to noise and external disturbances. Changes in the controller parameters were assumed to be dependent on the quadcopter mass, which was the process variable. A nonlinear model of the plant was used to identify the mass, employing the weighted recursive least squares (WRLS) method for online identification. The identification and control processes involved filtration using differential filters, which provided appropriate derivatives of signals. Proportional integral derivative (PID) controller tuning was performed using the Gauss–Newton optimisation procedure on the plant. Differential filters played a crucial role in all the developed control systems by significantly reducing measurement noise. The results showed that the performance of classical PID controllers can be improved by using differential filters and gain scheduling. The control and identification algorithms were implemented in an National Instruments (NI) myRIO-1900 controller. The nonlinear model of the plant was built based on Newton’s equations.

Highly Accurate Pneumatically Tunable Optofluidic Distributed Feedback Dye Lasers.

Optofluidic dye lasers integrated into microfluidic chips are promising miniature coherent light sources for biosensing. However, achieving the accurate and efficient tuning of lasers remains challenging. This study introduces a novel pneumatically tunable optofluidic distributed feedback (DFB) dye laser in a multilayer microfluidic chip. The dye laser device integrates microfluidic channels, grating structures, and vacuum chambers. A second-order DFB grating configuration is utilized to ensure single-mode lasing. The application of vacuum pressure to the chambers stretches the soft grating layer, enabling the sensitive tuning of the lasing wavelength at a high resolution of 0.25 nm within a 7.84 nm range. The precise control of pressure and laser tuning is achieved through an electronic regulator. Additionally, the integrated microfluidic channels and optimized waveguide structure facilitate efficient dye excitation, resulting in a low pump threshold of 164 nJ/pulse. This pneumatically tunable optofluidic DFB laser, with its high-resolution wavelength tuning range, offers new possibilities for the development of integrated portable devices for biosensing and spectroscopy.

Comparing actor-critic deep reinforcement learning controllers for enhanced performance on a ball-and-plate system

The optimization of controller parameters remains an ongoing challenge in the field of control system applications. This study introduces a novel approach involving the creation of custom actor-critic deep reinforcement learning (DRL) based PID controllers. These controllers are designed with the goal of achieving adaptive tuning, precise trajectory tracking, and stability in a ball-and-plate system. To achieve this objective, multiple actor-critic reinforcement learning agents were developed using different learning algorithms: Soft actor critic (SAC), deep deterministic policy gradient (DDPG), and twin delayed deep deterministic policy gradient (TD3). These agents incorporate multilayer-perceptron (MLP) policy learning algorithms in both the actor and critic network architectures, employing non-linear activation functions. This enables them to fine-tune PID control parameters within an infinite search space. Additionally, a custom reward function derived from the system’s environment was integrated into the learning process. The performance of the proposed methods was compared against a benchmark method, specifically, an existing deep reinforcement learning controllers reported in the literature. The evaluation of these controllers and other approaches was based on error metrics and time response analysis. Results demonstrate that the proposed controller denoted as SAC-PID(5) excelled in trajectory tracking and outperformed other methods. It exhibited minimal predictive errors and the shortest time responses in the majority of experiments. This highlights the significance of designing a customized SAC agents with appropriate network architecture, which positively impacts the learning process for intelligent tuning of controllers for classical control systems.