Controller Tuning Approach Research Articles

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.

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.

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.

Advancements in the Theory and Practice of Flight Vehicle System Identification

System identification methods have played an essential role in the research and industry projects at the Institute of Flight System Dynamics of the Technical University of Munich. Besides the application of the established system identification approaches to multiple aircraft, novel methods have been developed at the institute to deal with the challenges associated with fixed- and rotary-wing aircraft system identification in the time and frequency domains. This paper provides an overview of these new developments, as well as practical application examples from the past and ongoing projects at the institute. After a brief introduction to the work at the institute, the paper describes the new advancements in the fields of time domain system identification and optimal input design by introducing optimal control methods. It continues with an alternative problem formulation for applying frequency domain system identification and parameter estimation methods to a novel flight control design and tuning approach. Additional topics from the past and ongoing research at the institute such as novel methods and practical findings in rotorcraft system identification and flight path reconstruction for different applications will be discussed and referenced.

Robust Fractional-Order Proportional-Integral Controller Tuning for Load Frequency Control of a Microgrid System with Communication Delay

The integration of renewable energy resources and uncertainties in power system models pose significant challenges to load frequency control (LFC). To tackle these challenges, controller tuning with robustness constraints provides an efficient solution. In this paper, we propose a novel approach for controller tuning in the LFC of microgrid systems with communication delay. Our approach converts the tuning task into an algorithm that solves two parametric equations subject to robustness constraints, resulting in high accuracy and computational efficiency. We use a perturbed scenario with uncertain microgrid model gains and communication delay to illustrate the tuning efficiency. Simulation examples, including comparisons with classical tuning methods, demonstrate the effectiveness of our proposed method.

A Novel Approach for Adaptive Partial Sliding Mode Controller Design and Tuning in Non-Minimum Phase Switch-Mode Power Supplies

In this article, a novel systematic approach is proposed for a partial sliding mode controller (SMC) design and tuning in non-minimum phase switch-mode power supplies (SMPS). To achieve a more simplified controller in comparison with the conventional SMCs, the partial SMC (PSMC) is introduced in this article, which just requires a part of the sliding surface for controller formulation. The accuracy of the developed PSMC is proved mathematically within the entire range of operation. Since the control parameters of the PSMC are not selected by trial and error, it can maintain the stability and robustness of the closed-loop system in a broad operational range. In this regard, and to develop a systematic approach for robust control of SMPS, a constant frequency equivalent SMC is designed using the converter nominal parameters. Then, the extracted controller is combined with an adaptive component to ensure asymptotical stability against load and line changes. Considering the Lyapunov stability criteria for nonlinear systems, it is proved that the presented SPMC can be used for output voltage regulation in both discontinuous and continuous operating modes with zero steady state error. To avoid the trial and error method during the controller tuning and parameters selection, the system characteristic equation is extracted using the Jacobian approach. Considering the roots of the characteristic equation and the stable range of the closed-loop system, the controller parameters are tuned. Furthermore, in addition to simulation, the developed approach is evaluated practically using the TMS3220F2810 digital signal processor. It is shown that the dynamic response of the proposed approach is faster than the standard double-loop SMC during load and line changes. Additionally, it is seen that the developed controller is robust against model changes in both continuous and discontinuous operations.

Practical considerations in reinforcement learning-based MPC for mobile robots*

In mobile robot applications, the trajectory tracking task hides several difficulties, including the choice of the setpoint and the search for an acceptable trade-off between performance and computational constraints. In this work, we discuss practical issues of a Reinforcement Learning (RL) based Model Predictive Control (MPC) tuning approach by focusing on a specific mobile robot application, where the objective is to maximize the velocity, while keeping the robot within the track bounds. Among others, we show that softening the latter constraints allows us to obtain a RL-tuned tracking controller with the same performance of an economic nonlinear MPC formulation, but requiring significantly less computational resources.

Settling Time Optimization in Wire Bonder Systems via Extremum-Seeking Control1

Adequate tuning of control laws is essential for high positioning accuracy, large system throughput, and reliability in high-end mechatronic and robotic systems. However, a population of such systems generally shows slight variations in dynamic responses due to, e.g., manufacturing tolerances, different disturbance situations, or position-dependent dynamics. Given the time-consuming nature of controller design, even by experienced control engineers, typically just one control law is designed for the whole system population based on worst-case bounds on variations in dynamic responses, resulting in a loss of individual system performance. The main contribution of this paper is the development of an automated controller tuning approach, based on extremum-seeking control, for settling time optimization via individual controller tuning. While other automated controller tuning methods exist, the developed approach allows inclusion of closed-loop stability and robustness constraints based solely on non-parametric frequency-response measurements of open-loop plant dynamics, and therewith directly optimizes transient system performance in a purely data-based manner. The proposed approach has been applied in simulation in an industrial case study for settling time optimization in point-to-point motions of a wire bonder system. In this case study, the effectiveness of the approach has been shown by achieving significant performance increases of 39.4% and 40.6% compared to controllers designed by experienced control engineers using manual loop-shaping techniques and a frequency-based auto-tuner, respectively, without needing manual tuning effort.

RETRACTED: Cuckoo Search Optimization based PI Controller Tuning for Hopper Tank System

The paper work focuses on soft computing and Conventional controller tuning approach to design of PI controller, for a nonlinear hopper tank liquid level control system process Industries. The automation industries provide conventional techniques where it is impossible to maintain its settling time which motivates to do the research in this field. The system processes the combination of a conical and cylindrical tank for providing Multi-region based mathematical modelling to obtain the first order with delay time (FOPDT) process transfer function model. The Ziegler Nichols, Cohen-coon, Tyreus Luben, CHR (Chien, Hrones, and Reswick), IMC (Internal Model Control), Direct Synthesis, FOPI(Fractional Order PI) Conventional tuning formulae and Cuckoo Search Optimization (CSO) algorithm are used to optimize the servo regulatory responses of PI controller. The integral and proportional gain of the PI controller is said to produce the fastest settling time and reduces the error using performance indices and achieves Liquid Level control in hopper tank. Comparison is made for the various conventional controller tuning methods with Cuckoo Search Optimization tuning responses and identified to CSO-PI method offers enhanced Optimized Performance of settling time which was about 105.58 s which is relatively less while comparing to Conventional PI controller tuning methods for a different region based system.

A new Sliding Mode Control tuning approach for second-order inverse-response plus variable dead time processes

This work addresses a new set of tuning rules for Sliding Mode Control (SMC) applied to second-order inverse-response plus variable dead time processes. Descriptions of the dynamics of inverse response systems and sliding-mode controllers are first provided. Then, we present the design process for the set of SMC tuning rules, using dimensional analysis, experimental design, optimization, and model reduction techniques for set-point tracking. The obtained expressions are simple, which eases the application of the proposed step-by-step systematic methodology. The performance and robustness of the proposed SMC tuning method are compared to other well-known controllers, and then applied to a Van de Vusse isothermal reactor, which is a complex nonlinear second-order inverse-response system that exhibits variable dead time. The performance achieved with the new SMC tuning method is good for set-point tracking, while exhibiting a good behavior for disturbance rejection, and can be applied considering a wide-range of parameters of the process’ transfer function.

Systematic Online Tuning of Multirotor UAVs for Accurate Trajectory Tracking Under Wind Disturbances and In-Flight Dynamics Changes

The demand for accurate and fast trajectory tracking for multirotor Unmanned Aerial Vehicles (UAVs) have grown recently due to advances in UAV avionics technology and application domains. In many applications, the multirotor UAV is required to accurately perform aggressive maneuvers in challenging scenarios like the presence of external wind disturbances or in-flight payload changes. In this paper, we propose a systematic controller tuning approach based on identification results obtained by a recently developed Deep Neural Networks with the Modified Relay Feedback Test (DNN-MRFT) algorithm. We formulate a linear equivalent representation suitable for DNN-MRFT using feedback linearization. This representation enables the analytical investigation of different controller structures and tuning settings, and captures the non-linearity trends of the system. With this approach, the trade-off between performance and robustness in design was made possible which is convenient for the design of controllers of UAVs operating in uncertain environments. We demonstrate that our approach is adaptive and robust through a set of experiments, where accurate trajectory tracking is maintained despite significant changes to the UAV aerodynamic characteristics and the application of wind disturbance. Due to the model-based system design, it was possible to obtain low discrepancy between simulation and experimental results which is beneficial for potential use of the proposed approach for real-time model-based planning and fault detection tasks. We obtained RMSE of 3.59 cm when tracking aggressive trajectories in the presence of strong wind, which is on par with state-of-the-art.

Event-triggered control optimal tuning through bio-inspired optimization in robotic manipulators

This paper proposes the tuning approach of the event-triggered controller (ETCTA) for the robotic system stabilization task where the reduction of the stabilization error and the data broadcasting of the control update are simultaneously considered. This approach is stated as a dynamic optimization problem, and the best controller parameters are obtained by using fourteen different bio-inspired optimization algorithms. The statistics results reveal that, among the tested bio-inspired optimization algorithms, the most reliable algorithm in the proposed tuning problem is the differential evolution variant DE/Best/1/Exp. The obtained result is validated both in numerical simulation as well as using a laboratory prototype. The simulation results indicate that the obtained control parameters can also deal with disturbances and reference changes not considered in the ETCTA’s optimization problem formulation without significantly worsening the control design objective. Experimental results disclose that the proposed event-triggered control tuning approach provides the best trade-off between the number of control signal updates and the position error among other tuning approaches, decreasing the data broadcasting of the control update by around 86.33% with a non-significant increase in the stabilization error of around 26.53%.

MMC control optimization approach to facilitate DC-side interoperability in MTDC networks

HVDC systems are expected to evolve from today's dominating point-to-point towards multi-terminal DC (MTDC) systems. Considering the European market structure, these MTDC networks are envisaged to be multi-vendor based. Due to proprietary reasons, the controls and tuning of the converters are not expected to be shared. Recent studies have shown interoperability based instabilities in multi-vendor based MTDC networks, which might lead to a shutdown of the network. To facilitate the envisioned development towards MTDC networks, the control interoperability between different converters in case of network extensions has to be ensured without having access to existing converters controls. This paper therefore presents an enhanced control tuning approach for Modular Multilevel Converters which are being connected to an existing DC network to ensure control interoperability. The existing network is seen as a black-box and only information accessible at the terminals of the new converter station is used. Using a frequency-sweep on the DC side, the equivalent MMC and network impedances at the terminals are derived. The proposed control optimization method incorporates the information gained from the impedance analysis and thereby ensures control stability. An example based on a three-terminal HVDC network demonstrates the validity of the concept.

Scalable and Data Privacy Conserving Controller Tuning for Large-Scale Power Networks

The increasing share of renewable generation leads to new challenges in reliable power system operation, such as the rising volatility of power generation, which leads to time-varying dynamics and behavior of the system. To counteract the changing dynamics, we propose to adapt the parameters of existing controllers to the changing conditions. Doing so, however, is challenging, as large power systems often involve multiple subsystem operators, which, for safety and privacy reasons, do not want to exchange detailed information about their subsystems. Furthermore, centralized tuning of structured controllers for large-scale systems, such as power networks, is often computationally very challenging. For this reason, we present a hierarchical decentralized approach for controller tuning, which increases data security and scalability. The proposed method is based on the exchange of structured reduced models of subsystems, which conserves data privacy and reduces computational complexity. For this purpose, suitable methods for model reduction and model matching are introduced. Furthermore, we demonstrate how increased renewable penetration leads to time-varying dynamics on the IEEE 68-bus power system, which underlines the importance of the problem. Then, we apply the proposed approach on simulation studies to show its effectiveness. As shown, similar system performance as with a centralized method can be obtained. Finally, we show the scalability of the approach on a large power system with more than 2500 states and about 1500 controller parameters.

Novel Asynchronous Activation of the Bio-Inspired Adaptive Tuning in the Speed Controller: Study Case in DC Motors

The fusion of bio-inspired algorithms into online controller tuning (adaptive controller tuning) is one of the main topics in Intelligent Control. One crucial issue is to reduce the times that the tuning process performs over time. In this work, a novel Asynchronous Adaptive Controller Tuning (AACT) approach is proposed to reduce the number of tuning process activations, and hence, this promotes resource savings in the overall computational cost for the tuning process. In this approach, an event function is designed to determine the control parameter update where the use of identification and predictive stages set the current control parameters. Furthermore, an elitist initialization in the differential evolution algorithm is also incorporated for solving the optimization problems at each stage. The speed regulation of the DC motor under disturbances is the study case in the ACCT approach. Comparative results with state-of-the-art bio-inspired algorithms in control tuning reveal in the AACT approach that the elitist initialization in differential evolution notably benefits the controller performance. Moreover, the comparative results with the Synchronous Adaptive Controller Tuning (SACT) approach show that the proposal reduces 61% the tuning process computation frequency with a similar speed regulation performance when disturbances appear.

Graphic RCRA‐PIDA tuning based on maximum sensitivity for automatic generation control of thermal and hydro power systems

The frequency control is the mandatory task in a modern power system because of load demand variation and the integration of renewable energy sources (RESs). The main objective of automatic generation control (AGC) scheme is to maintain the balance between generation and load demand. This study proposes a graphical robust characteristic ratio assignment (RCRA) approach for PIDA controller tuning based on maximum sensitivity ( M s ) for AGC. The basic feature of this graphical approach is that characteristic ratios ( γ i ) in RCRA scheme are calculated in terms of M s . The important part of this design is that the analytical expressions for coefficient gains of PIDA controller are derived in terms of γ i and system parameters. Here, the non-reheat steam-turbine and hydro-turbine based single and multi-area power systems are considered for validation of the proposed control scheme. The critical issue in controller design for a hydro-turbine based power system is the non-minimum phase behaviour. The robustness and performance of RCRA-PIDA controller are examined under system non-linearities, parametric uncertainty and disturbances. Further, the performance of proposed approach is evaluated in the presence of RESs. Finally, it is observed that the proposed control approach performs better in comparison to the recently published control schemes.

Overshoot Elimination for Control Systems with Parametric Uncertainty via a PID Controller

One of the key performance requirements for different control systems is non-overshooting step response, so that the controllable value should not overcome the reference value within a transient process. The problem of providing a non-overshooting step response was examined in this paper. Despite much scientific research being dedicated to the overshoot elimination problem, there are little to no results regarding parametric uncertainty for the discussed problem. Consideration of parametric uncertainty, particularly in the form of interval-given parameters, is essential, since in many physical processes, electronic devices and control systems parameter values can be obtained with acceptable error, and they can vary under different conditions. The main result of our research is the development of a proportional-integral-derivative (PID)-controller tuning approach for systems with interval-given parameters that provides a non-overshooting step response for such classes of control systems. This paper investigates analytical conditions and constraints for linear time invariant (LTI) systems in order to have no overshoot, enhances them with respect to parametric uncertainty, and formulates rules for tuning choices of parameters.

An Internal Model Control-Based Approach for Characterization and Controller Tuning of Turbocharged Gasoline Engines

This brief develops a method for system characterization and controller tuning for the boost control system of automotive turbocharged gasoline engines. The method utilizes the relay feedback technique for system characterization, followed by the internal model control (IMC) technique for controller tuning. The novelty is that instead of a high fidelity plant model, an ultrasimplified gain-integrator-delay model is employed in the IMC controller. As we show, our method leads to a time-efficient calibration process, assigns a desired closed-loop bandwidth exactly , and leads to good stability margins. A detailed and thorough analysis of the method, including its fundamental limitations, is provided. The method is experimentally applied to the boost control system of a turbocharged gasoline engine, where it is shown that the controller gains can be reliably computed at various engine operating points. The method identifies turbocharger speed as the best scheduling parameter for the controller gains, and the resulting controller is experimentally shown to yield good tracking performance.

Adaptive MIMO pole placement control for commissioning of a rotor system with active magnetic bearings

Active magnetic bearing (AMB) supported rotor systems require advanced control strategies to meet the increased performance requirements of more and more demanding applications. To meet the particular requirements for the performance under changing dynamics, an adaptive control structure is a task worth pursuing. This paper studies adaptive multi-input multi-output (MIMO) pole placement applied to the control commissioning of an AMB-supported rotor system. The control tuning approach is based on a rigid body model, and the parameter estimation is carried out with a recursive extended least squares (RELS) based algorithm for the MIMO system. The proposed approach is studied by simulations and validated with both a 2-degree-of-freedom (2-DOF) and a 4-DOF AMB system.

Analytical Tuning Rules of I-P Controller For Over Damped Processes with Large Zero Terms In Its Transfer Function Model

In this paper, the structure of the P-I controller has been revised to I-P controller structure, for the model of First-order plus time delay and model of Half-order plus time delay. P-I controller is the dominant controller used for most of the Industrial processes. However, high peak overshoot and sensitivity to controller gains are some of the disadvantages faced by P-I controller . The objective of this study is to overcome these disadvantages, I-P controller has been implemented. Simulation results obtained using MATLAB/simulink shows that the I-P controller tuning approach provides enhanced performance and reduction in the peak overshoot when compared with P-I controller tuning.