Controller Tuning Research Articles

Atomic Layer Deposition of Cu Catalysts on Gas Diffusion Electrodes for Electrochemical CO2 Reduction

Electrochemical CO2 conversion is gaining attention as an important process for carbon fixation through the use of renewable electricity to address the climate change crisis. Copper is a unique single-element catalyst that can induce C-C coupling reactions to generate high-value-added compounds. The triple phase boundary has been successfully controlled at the catalyst interface using gas diffusion electrodes (GDEs) over the past few years, resulting in high C-C coupling performance. Therefore, methods to engineer the catalyst interface on the 3D structure of GDE are of significant importance. Generally, the catalyst on the GDE is introduced through physical vaper deposition (PVD) using sputtering or E-beam evaporation, or through solution methods such as spray coating and drop-casting of nano-particles. However, the porous and tortuous 3D structure of the GDE often result in limited conformality and difficulty in obtaining a uniform and controlled infiltration of the catalyst into the support structure.In this study, we synthesized Cu catalysts directly onto GDE surfaces using plasma-enhanced atomic layer deposition (PE-ALD), which allowed for precise tuning of the catalyst size and loading at the nanoscale [1]. The synthesized catalysts were confirmed to be polycrystalline Cu nanoparticles using grazing incidence X-ray diffraction. To demonstrate the advantages of the conformal and tunable control of PE-ALD deposition, we deposited the same areal mass loading of Cu catalyst onto the GDE substrates using PVD, and compared the CO2 reduction activity in an H-cell environment. The PE-ALD Cu catalyst showed more than 3 times higher current density than the PVD catalyst at the same electrode potential, a low (~10%) Faradaic efficiency (FE) for the hydrogen evolution reaction (HER), and greater than 75% FE for C-C coupling to form C2+ products including ethylene and ethanol, which is comparable to the highest performances reported to date for metallic Cu catalysts in a H-cell environment. Furthermore, stable operation was observed for a duration of 15 hours, with minimal changes in activity and selectivity. In conclusion, the introduction of catalysts using PE-ALD on GDE electrodes represents a powerful new synthesis method that can increase both C-C coupling performance and selectivity compared to PVD deposition, due to its adjustable Cu nanoparticle size and high conformality. Reference 1) J. D. Lenef, S.Y. Lee, K. M. Fuelling, K. E. Rivera Cruz, A. Prajapati, D. O. D. Cornejo, T. H. Cho, K. Sun, E. Alvarado, T. S. Arthur, C. A. Roberts, C. Hahn, C. C. L. McCrory, N. P. Dasgupta, Nano Lett. 23, 10779-10787 (2023)

A Grid-Forming Converter with MVDC Supply and Integrated Step-Down Transformer: Modeling, Control Perspectives, and Control Hardware-in-the-Loop (C-HIL) Verification

A grid-forming voltage source converter with an integrated step-down transformer could be a promising solution for supplying low-voltage alternating current loads from a medium-voltage direct current supply. However, it may require a control system that gathers feedback signals from both the primary and secondary sides of the transformer, which in turn complicates the derivation of a standard form linear model. The absence of such a model complicates control tuning, as well as the assessment of dynamics and stability of the converter system. The objective of this paper is to address this gap in knowledge. For the case study, a conventional H-bridge converter with a step-down transformer and an αβ-frame dual-loop grid-forming controller is considered. Initially, comprehensive guidelines on deriving a standard form linear model for this converter system are presented. Then, the impact of controlling the VSC in a dq frame and the changes in the transformer vector group on the small-signal model of the VSC are analyzed. The aspects of control tuning are also discussed in detail, and the model’s accuracy and efficacy are validated both theoretically and through control hardware-in-the-loop (C-HIL) tests using a Typhoon HIL setup.

Modeling Fluid Flow in Ship Systems for Controller Tuning Using an Artificial Neural Network

Flow processes onboard ships are common in order to transport fluids like oil, gas, and water. These processes are controlled by PID controllers, acting on the regulation valves as actuators. In case of a malfunction or refitting, a PID controller needs to be re-adjusted for the optimal control of the process. To avoid experimenting on operational real systems, models are convenient alternatives. When real-time information is needed, digital twin (DT) concepts become highly valuable. The aim of this paper is to analyze and determine the optimal NARX model architecture in order to achieve a higher-accuracy model of a ship’s flow process. An artificial neural network (ANN) was used to model the process in MATLAB. The experiments were performed using a multi-start approach to prevent overtraining. To prove the thesis, statistical analysis of the experimental results was performed. Models were evaluated for generalization using mean squared error (MSE), best fit, and goodness of fit (GoF) measures on two independent datasets. The results indicate the correlation between the number of input delays and the performance of the model. A permuted k-fold cross-validation analysis was used to determine the optimal number of voltage and flow delays, thus defining the number of model inputs. Permutations of training, test, and validation datasets were applied to examine bias due to the data arrangement during training.

Investigation of H∞$$ {\mathcal{H}}_{\infty } $$‐Tuned Individual Pitch Control for Wind Turbines

ABSTRACTLarge wind turbines experience amplified asymmetrical loads at particular harmonics of the rotational frequency. Individual pitch control (IPC) has emerged as a potential controls solution to this problem. control, which facilitates robust, multivariable controller synthesis in the frequency domain, is a candidate approach to IPC design for several reasons. First, the objectives of asymmetrical load attenuation can be readily described in the frequency domain. Second, the IPC signals and loads in orthogonal directions are known to be coupled, which necessitates a multivariable controller approach. Third, synthesis is a method that can explicitly impose constraints on the robustness of the closed‐loop system. A downside of IPC is the significant increase in blade‐pitch travel incurred, which introduces additional loading on the blade‐pitch bearings over time. We investigate strategies to constrain the blade‐pitch travel in the controller tuning procedure. A comprehensive study is thus presented for a range of ‐synthesized IPCs to attenuate asymmetrical loads on large rotors at harmonics of the rotational frequency while reducing blade‐pitch travel. All developed controllers are validated and compared using a 25‐MW fixed‐bottom offshore wind turbine model via (a) linear analysis of the response of the closed‐loop system to input and output disturbances, (b) nonlinear analysis in terms of structural load power spectra and damage‐equivalent loads (DELs), and (c) the actuator duty cycle (ADC) of the blade‐pitch actuator.

Analytical design of modified Smith predictor for second-order stable time delay plants incorporating a zero

Simplified control tuning not only enhances engineers' proficiency in control theory and its application but also contributes to their skill development. This study focuses on the control of stable second-order plus time delay (SOPTD) plants incorporating a zero, by employing a modified Smith predictor structure. A forward path PID controller and a PID controller with a lead/lag filter in the feedback path contribute to the proposed control system. The PID controller determines the set-point tracking. The load disturbance rejection is influenced by both the PID and lead/lag filter. Each controller is simply designed using pole placement, while considering a tuning parameter to make a balance between performance and robustness. Filtering of the set-point signal is implemented to enhance its response. To assess the effectiveness of the proposed technique, the study involves examining a SOPTD plant and either a negative or a positive zero. Simulation and comparison analysis show that the proposed controller is appropriate for industrial usage since it quickly tracks the set-point and efficiently rejects disturbances, resulting in a smooth control signal. Furthermore, robustness tests reveal its satisfactory degree of robustness against model parameter uncertainties. In summary, the proposed approach is straightforward and outperforms recently published design methodologies in terms of both performance and robustness.

Comparative analysis of proportional, proportional–integral, and proportional–integral–derivative controllers for thermal regulation in a cylindrical cooling system with multiple O-Ring heat sources

This study is intended to determine the thermal management capability of different closed-loop controllers inside a cylindrical three-dimensional cooling system with multiple O-Ring type heat sources. The motivation for this research stems from the need for efficient thermal regulation in advanced cooling systems, which is critical for applications ranging from electronics cooling to industrial processes. The uniqueness of this study lies in its comprehensive evaluation of different controllers such as proportional (P), proportional–integral (PI), and proportional–integral–derivative within a geometrically complex cooling environment using a novel trial-and-error approach for tuning controller parameters. The observational domain is a hollow cylinder, and the four discrete heat sources are placed at regular intervals along the cylinder's longitudinal axis, with all the remaining walls insulated. At the center of the system is a temperature probe that measures and provides feedback to the control module to continuously compare it to a pre-specified setpoint temperature. The flowing fluid, air, enters through the semi-circular inlet at one end, whose velocity is controlled by a controller response, and is discharged through the semi-circular outlet at the other end at atmospheric conditions. This study uses the Galerkin finite element method, using proper initial and boundary conditions to solve the governing equations, namely, the Navier–Stokes and heat energy equations. We analyze the time-dependent behaviors of the cooling system by measuring controller responses such as overshoot, rise time, oscillation, steady-state error, and settling time. Additionally, the impacts of the Reynolds number (Re), Richardson number (Ri), and Grashof number (Gr) on the overall mean Nusselt number over time are observed to understand the influence of flow regulation due to the controllers' actions. This comprehensive analysis provides insights into the controllers' inlet flow control based on the set temperature at the probe's center location. A unique trial-and-error approach for selecting Kp and Ki values, which is crucial for controller analysis, is also presented. Based on the results, it can be inferred that increasing the Kp value from 0.003 to 0.010 ms−1 K−1 reduces the steady-state error from 40.8% to 13.97%. For a Ki value of 0.006 ms−2 K−1, the PI controller achieves a zero steady-state error with faster settling at 3.29 s, along with an overshoot of approximately 80.51%. Conversely, a lower Kd value of about 0.0001 mK−1 results in a reduction in the settling time and overshoot compared to the PI controller, while a higher Kd value ensures optimum stability with a higher settling time and a stable Nusselt number of 1.93. This trial-and-error approach to parameter tuning provides valuable insights into the design of controlled environments and the effective management of thermal conditions in various thermo-fluidic applications.

Addressing Launch and Deployment Uncertainties in UAVs with ESO-Based Attitude Control

This paper describes the design and implementation of a novel three-axis attitude control autopilot scheme for tube-launched, air-deployed UAVs. In early flight tests, various factors, such as model uncertainties during launch, aerodynamic uncertainties, geometric parameter changes during deployment, and significant uncertainties in booster rocket installation, exceeded the control capabilities of the attitude autopilot, causing flight instability. In order to address these issues, a numerical simulation model of the full launch process considering deviations was established based on early flight tests. A cascade attitude controller was then designed using an extended state observer (ESO), and the boundedness of control errors under unknown bounded disturbances was theoretically proven, providing requirements for the parameter tuning of the cascade controller. Comparative experiments and a second flight test both demonstrate that the ESO-based cascade attitude controller exhibits strong feedforward disturbance compensation under high-uncertainty conditions, effectively achieving stable control within the flight envelope.

Multichannel information transmission via a dual-frequency point space-time coding metasurface

Space-time coding (STC) digital metasurfaces enable dynamic control of nonlinear harmonics. To achieve efficient frequency tuning and harmonic control, we propose a one-bit column-controlled temporal coding dual-frequency point STC digital metasurface. Using a field-programmable gate array (FPGA) to control diode switching, the metasurface is encoded to generate multiple target reflected harmonics as independent information transmission channels. The amplitude intensities of these harmonics are defined as independent binary symbols for transmission. The proposed space-time coding digital metasurface can achieve a phase shift of 180° in its unit structure, with reflectivities above 90% at both operating frequencies. We fabricated the designed metasurface and tested its far-field scattering experimentally. When the incident electromagnetic wave frequency is 4.63 GHz, the predicted results are essentially consistent with the experimental findings.

Fault detection and synchronization control in hybrid DC/AC microgrids using grid-forming inverter DC-link controller

This paper introduces a DC-link fault detection and synchronization control strategy for grid-forming inverters in hybrid DC/AC microgrids, aiming to bolster system stability and reliability. The proposed control scheme seamlessly integrates DC-link fault detection with virtual synchronous generator (VSG) control, facilitating inertia emulation and synchronization without the need for a phase-locked loop (PLL). The DC bus controller is designed to swiftly identify faults triggered by power imbalances, such as load fluctuations or generator integration, and dynamically adjust the power reference for the VSG controller to ensure inertia emulation. The Ziegler-Nichols method is utilized for meticulous tuning of the DC-link controller gains, while the modified swing equation governs VSG inertia and synchronization. MATLAB/Simulink demonstrate the superior performance of the proposed method compared to traditional GFM control strategies. Specifically, the proposed controller improved the frequency nadir to 59.98 Hz compared to 59.75 Hz and showed an enhanced power step response of 7.9×10^5 W compared to 6.9×10^5 W for conventional control method. The proposed GFM control also eliminated the 10×10^6 W switching spike observed with conventional controllers. These findings highlight the potential of this fault-controller approach to advance the reliability and efficiency of hybrid DC/AC microgrids.

Pairing voltage-source converters with PI tuning controller based on PSO for grid-connected wind-solar cogeneration

This paper introduces a novel method to improve the efficiency of grid-connected wind-solar cogeneration systems. It involves the integration of Voltage-Source Converters (VSCs) with a Proportional-Integral (PI) tuning controller that has been optimized using Particle Swarm Optimization (PSO). Integrating renewable energy sources into power grids presents a set of challenges in terms of ensuring stability and optimizing power flow. VSCs are essential for effectively managing power flow between renewable sources and the grid. The PI controller is vital in maintaining voltage levels and ensuring stable operation. Conventional PI controller tuning methods commonly rely on heuristic approaches, which might only partially optimize performance across various operational conditions. In order to tackle this issue, PSO is utilized to fine-tune the parameters of the PI controller automatically. This results in a significant reduction in system deviations and a notable improvement in efficiency, even when faced with fluctuating wind and solar conditions. The simulation results conducted in MATLAB/Simulink confirm the effectiveness of the proposed approach in enhancing system stability and reducing response times. The PI controller optimized using PSO showcases exceptional adaptability to varying environmental and grid conditions, resulting in minimized power fluctuations and improved grid reliability. This study makes a valuable contribution to the field of renewable energy integration. It provides valuable insights into enhancing the performance of grid-connected wind-solar cogeneration systems through advanced control techniques and optimization algorithms such as PSO.

Copper-Catalyzed Tunable Oxygenative Rearrangement of Tetrahydrocarbazoles.

Herein, we report a general copper-catalyzed method for the tunable oxygenative rearrangement of tetrahydrocarbazoles to cyclopentyl-bearing spiroindolin-2-ones and spiroindolin-3-ones. The method demonstrates excellent chemoselectivity, regioselectivity, and product control simply by using the H2O and O2 as oxygen source, respectively. This open-flask method is safe and simple to operate, and no other chemical oxidants are required. Besides, inspired from the unique pathway of 1, 2-migration rearrangement, a highly controllable hydroxylation of indoles for the construction of C3a-hydroxyl iminium indolines was also developed. Mechanistic experiments suggest that a single-electron transfer-induced oxidation process is responsible for the tunable selectivity control.

Demonstration of Tunable Control over a Delayed-Release Vaccine Using Atomic Layer Deposition.

Many vaccines require multiple doses for full efficacy, posing a barrier for patient adherence and protection. One solution to achieve full vaccination may be attained with single-administration vaccines containing multiple controlled release doses. In this study, delayed-release vaccines were generated using atomic layer deposition (ALD) to coat antigen-containing powders with alumina. Using in vitro and in vivo methods, we show that increasing the coat thickness controls the kinetics of antigen release and antibody response, ranging from weeks to months. Our results establish an in vitro-in vivo correlation with a level of tunable control over the antigen release and antibody response times with the potential to impact future vaccine design.

Computational elements based on coupled VO2 oscillators via tunable thermal triggering

Computational technologies based on coupled oscillators are of great interest for energy efficient computing. A key to developing such technologies is the tunable control of the interaction among oscillators which today is accomplished by additional electronic components. Here we show that the synchronization of closely spaced vanadium dioxide (VO2) oscillators can be controlled via a simple thermal triggering element that itself is formed from VO2. The net energy consumed by the oscillators is lower during thermal coupling compared with the situation where they are oscillating independently. As the size of the oscillator shrinks from 6 μm to 200 nm both the energy efficiency and the oscillator frequency increases. Based on such oscillators with active tuning, we demonstrate AND, NAND, and NOR logic gates and various firing patterns that mimic the behavior of spiking neurons. Our findings demonstrate an innovative approach towards computational techniques based on networks of thermally coupled oscillators.

Performance Evaluation of Fractional Proportional–Integral–Derivative Controllers Tuned by Heuristic Algorithms for Nonlinear Interconnected Tanks

This article presents an in-depth analysis of three advanced strategies to tune fractional PID (FOPID) controllers for a nonlinear system of interconnected tanks, simulated using MATLAB. The study focuses on evaluating the performance characteristics of system responses controlled by FOPID controllers tuned through three heuristic algorithms: Ant Colony Optimization (ACO), Grey Wolf Optimizer (GWO), and Flower Pollination Algorithm (FPA). Each algorithm aims to minimize its respective cost function using various performance metrics. The nonlinear model was linearized around an equilibrium point using Taylor Series expansion and Laplace transforms to facilitate control. The FPA algorithm performed better with the lowest Integral Square Error (ISE) criterion value (297.83) and faster convergence in constant values and fractional orders. This comprehensive evaluation underscores the importance of selecting the appropriate tuning strategy and performance index, demonstrating that the FPA provides the most efficient and robust tuning for FOPID controllers in nonlinear systems. The results highlight the efficacy of meta-heuristic algorithms in optimizing complex control systems, providing valuable insights for future research and practical applications, thereby contributing to the advancement of control systems engineering.

CRISPR-GEM: A Novel Machine Learning Model for CRISPR Genetic Target Discovery and Evaluation.

CRISPR gene editing strategies are shaping cell therapies through precise and tunable control over gene expression. However, achieving reliable therapeutic effects with improved safety and efficacy requires informed target gene selection. This depends on a thorough understanding of the involvement of target genes in gene regulatory networks (GRNs) that regulate cell phenotype and function. Machine learning models have been previously used for GRN reconstruction using RNA-seq data, but current techniques are limited to single cell types and focus mainly on transcription factors. This restriction overlooks many potential CRISPR target genes, such as those encoding extracellular matrix components, growth factors, and signaling molecules, thus limiting the applicability of these models for CRISPR strategies. To address these limitations, we have developed CRISPR-GEM, a multi-layer perceptron (MLP)-based synthetic GRN constructed to accurately predict the downstream effects of CRISPR gene editing. First, input and output nodes are identified as differentially expressed genes between defined experimental and target cell/tissue types respectively. Then, MLP training learns regulatory relationships in a black-box approach allowing accurate prediction of output gene expression using only input gene expression. Finally, CRISPR-mimetic perturbations are made to each input gene individually and the resulting model predictions are compared to those for the target group to score and assess each input gene as a CRISPR candidate. The top scoring genes provided by CRISPR-GEM therefore best modulate experimental group GRNs to motivate transcriptomic shifts towards a target group phenotype. This machine learning model is the first of its kind for predicting optimal CRISPR target genes and serves as a powerful tool for enhanced CRISPR strategies across a range of cell therapies.

Improving load frequency controller tuning with rat swarm optimization and porpoising feature detection for enhanced power system stability

Load frequency control (LFC) plays a critical role in ensuring the reliable and stable operation of power plants and maintaining a quality power supply to consumers. In control engineering, an oscillatory behavior exhibited by a system in response to control actions is referred to as “Porpoising”. This article focused on investigating the causes of the porpoising phenomenon in the context of LFC. This paper introduces a novel methodology for enhancing the performance of load frequency controllers in power systems by employing rat swarm optimization (RSO) for tuning and detecting the porpoising feature to ensure stability. The study focuses on a single-area thermal power generating station (TPGS) subjected to a 1% load demand change, employing MATLAB simulations for analysis. The proposed RSO-based PID controller is compared against traditional methods such as the firefly algorithm (FFA) and Ziegler-Nichols (ZN) technique. Results indicate that the RSO-based PID controller exhibits superior performance, achieving zero frequency error, reduced negative peak overshoot, and faster settling time compared to other methods. Furthermore, the paper investigates the porpoising phenomenon in PID controllers, analyzing the location of poles in the s-plane, damping ratio, and control actions. The RSO-based PID controller demonstrates enhanced stability and resistance to porpoising, making it a promising solution for power system control. Future research will focus on real-time implementation and broader applications across different control systems.

A novel synthesis route with large-scale sublattice asymmetry in boron doped graphene on Ni(111)

One of the promising ways to functionalize graphene is incorporation of heteroatoms in carbon sp2 lattice, as it is proven to be an efficient and versatile method for controllably tuning chemistry of graphene. We present unique, contamination-free method for selectively doping graphene with B dopants, which are incorporated in layer from a reservoir created in the bulk of Ni(111) single crystal, during standard CVD growth process, leading to clean, versatile and efficient method for creating B-doped graphene. We combine experimental (STM, XPS) and theoretical (DFT, simulated STM) studies to understand structural and chemical properties of substitutional B dopants. Along with previously reported substitutional B in fcc sites, we have observed, for the first time, two more defects, namely substitutional B in top sites and interstitial B in octahedral subsurface sites. Extensive STM investigations confirm presence of low and high concentration regions of B dopants in as-prepared B-doped graphene, indicating non-uniform boron incorporation. Among two substitutional sites, no preference is observed in low-concentration B-doped regions, whereas in high B concentration regions, one of the sublattices is preferred for incorporation, along with alignment of defects. This generates an asymmetric sublattice doping in as-grown B-doped graphene, which is theoretically predicted to result in notable band gap.

Particle swarm optimization solution for roll-off control in radiofrequency ablation of liver tumors: Optimal search for PID controller tuning.

The study investigates the efficacy of a bioinspired Particle Swarm Optimization (PSO) approach for PID controller tuning in Radiofrequency Ablation (RFA) for liver tumors. Ex-vivo experiments were conducted, yielding a 9th order continuous-time transfer function. PSO was applied to optimize PID parameters, achieving outstanding simulation results: 0.605% overshoot, 0.314 seconds rise time, and 2.87 seconds settling time for a unit step input. Statistical analysis of 19 simulations revealed PID gains: Kp (mean: 5.86, variance: 4.22, standard deviation: 2.05), Ki (mean: 9.89, variance: 0.048, standard deviation: 0.22), Kd (mean: 0.57, variance: 0.021, standard deviation: 0.14) and ANOVA analysis for the 19 experiments yielded a p-value ≪ 0.05. The bioinspired PSO-based PID controller demonstrated remarkable potential in mitigating roll-off effects during RFA, reducing the risk of incomplete tumor ablation. These findings have significant implications for improving clinical outcomes in hepatocellular carcinoma management, including reduced recurrence rates and minimized collateral damage. The PSO-based PID tuning strategy offers a practical solution to enhance RFA effectiveness, contributing to the advancement of radiofrequency ablation techniques.

A dual-mode infrared thermal stealth structure compatible with radar bands

Currently, multi-spectral stealth technology has become crucial in countering advanced military and civilian detection methods. Metamaterials, known for their adaptable designs and superior wavefront manipulation capabilities, effectively meet the complex requirements of infrared and radar bands for material properties, thus being widely used in developing multi-band thermal stealth materials. In this study, we propose a tunable metamaterial structure based on phase-change material vanadium oxide. This structure operates in two modes facilitated by the temperature-induced phase transition of vanadium oxide, which modifies the effective cavity thickness and influences the infrared resonance of the coupled cavity-plasma system. This modulation enables tunable control of thermal radiation within the 3 μm–14 μm wavelength range, achieving effective infrared thermal camouflage across varying environmental backgrounds. Additionally, the structure achieves high radar absorption across the ultra-wideband radar frequencies of 3.65 GHz–18 GHz and 26.5 GHz–34 GHz, with average absorption of 0.89 and 0.85, respectively. Hence, the proposed metamaterial structure effectively provides infrared thermal stealth compatibility with radar bands in both low-temperature (270 K–310 K) and high-temperature (380 K–420 K) environments.

Reliable identification based intelligent PID tuning for long-period process control under different working conditions

BackgroundChemical process models change frequently with operating conditions. However, the industry mostly adopts an off-line calibration and online fixed value commissioning scheme, which can be difficult to maintain control quality. Therefore, a long-cycle online intelligent PID controller calibration scheme is necessary. MethodsFirstly, a high-order linear dynamic model is used as the identification model, and online recursive identification technology is utilized. Secondly, a comprehensive performance index is selected to ensure both stability and rapidity of dynamic transitions. Finally, the slow rate updated PID parameters are obtained during the process control, and the Levy Memory Particle Swarm Optimization (LMPSO) search is applied to harvest the optimal solution. Significant findingsThis paper has presented an intelligent PID tuning method based on reliable identification technology. The main contributions include: (i) By using Lyapunov theory, the boundedness of identification error using the proposed algorithm is guaranteed; (ii) The LMPSO algorithm is proposed to enhance global search capability and search efficiency; (iii) A novel optimization scheme is proposed for a full-process online closed-loop intelligent PID controller. The scheme aims to improve the industrial controller's performance without altering its structure.