Tuning Of Controller Parameters Research Articles

Optimized Hierarchical Sliding Mode Control for the Swing-up and Stabilization of a Rotary Inverted Pendulum

This paper presents a study on controlling a rotary inverted pendulum (RIP) system using a hierarchical sliding mode control (HSMC) approach. The objective is to swing up and stabilize the pendulum at a desired position. The proposed HSMC controller addresses the underactuation challenge through a hierarchical structure of sliding surfaces. The particle swarm optimization (PSO) algorithm is used to optimize the controller parameters. Simulations were performed to evaluate the performance of the HSMC controller at different initial pendulum angles, demonstrating its effectiveness in achieving swing-up and stabilization. The integration of the PSO algorithm enhances the controller’s adaptability and robustness, emphasizing the benefits of combining optimization algorithms with controller parameter tuning for underactuated systems like the RIP.

Pole placement tuning of proportional integral derivative feedback controller for knee extension model

Functional electrical stimulation (FES) has shown potential in rehabilitative exercises for patients recovering from spinal cord injuries. In recent developments, conventional open-loop FES control techniques have evolved into closed-loop systems that employ feedback controllers for automation. However, closed-loop FES systems often face challenges due to muscle non-linear effects, such as fatigue, time delays, stiffness, and spasticity. Therefore, an accurate non-linear knee model is required during the design stage, and precise tuning of the feedback controller parameters is vital. A proportional– integral–derivative (PID) controller is commonly used as a feedback controller due to its simplicity and ease of implementation. However, most PID tuning methods are complex and time consuming. This paper investigates the viability of employing the pole placement technique for tuning a PID controller that regulates the non-linear knee extension model. The pole placement method aims to improve the control and adaptability of the PID controller in closed-loop FES systems, specifically by facilitating knee extension exercises. MATLAB Simulink was used to assess the effectiveness of this tuning approach. Results showed that the PID controller performed satisfactorily without non-linearities, but performance varied with the inclusion of specific non-linearities. The pole placement tuning method facilitated preliminary assessments of PID controller performance, preceding highly advanced optimization.

Combination of fuzzy control and reinforcement learning for wind turbine pitch control

Abstract The generation of the pitch control signal in a wind turbine (WT) is not straightforward due to the nonlinear dynamics of the system and the coupling of its internal variables; in addition, they are subjected to the uncertainty that comes from the random nature of the wind. Fuzzy logic has proved useful in applications with changing system parameters or where uncertainty is relevant as in this one, but the tuning of the fuzzy logic controller (FLC) parameters is neither straightforward nor an easy task. On the other hand, reinforcement learning (RL) allows systems to automatically learn, and this capability can be exploited to tune the FLC. In this work, a WT pitch control architecture that uses RL to tune the membership functions and scale the output of a fuzzy controller is proposed. The RL strategy calculates the fuzzy controller gains in order to reduce the output power error of the WT according to the wind speed. Different reward mechanisms based on the output power error have been considered. Simulation results with different wind profiles show that this architecture performs better (123.7 W) in terms of power errors than an FLC without RL (133.2 W) or a simpler PID (208.8 W). Even more, it provides a smooth response and outperforms other hybrid controllers such as RL-PID and radial basis function neural network control.

DFIG in Wind Energy Applications with High Order Sliding Mode Observer-based Fault-Tolerant Control Scheme using Sea Gull Optimization

This paper describes a new method for maximizing power extraction from a wind energy conversion system (WECS) by using a doubly fed induction generator (DFIG) that operates below nominal wind speed. To maximize the collected power of a wind turbine (WTG) exposed to actuator failure, a fault-tolerant high-order sliding mode observer (HOSMO) and Seagull Optimization Algorithm with a model predictive controller (MPC) technique is proposed. Evaluate both the real state and the sensor error simultaneously using a higher-order sliding-mode observer. Active fault tolerant controllers are designed to regulate wind turbine rotor speed and power in the presence of actuator defects and uncertainty. With the growing interest in employing wind turbines (WTGs) as the primary generators of electrical energy, fault tolerance has been seen as essential to improving efficiency and reliability. This research focuses on optimal fault-tolerant pitch control, which is used to modify the pitch angle of wind turbine blades in the event of sensor, actuator, and system failures. A Seagull Optimization Algorithm (SOA) is proposed to tune controller parameters to improve the performance of WT. The proposed method has achieved 92% of power tracking performance when compared to existing method.

Enhancing Electric Vehicle Performance with a Hybrid PI-Sliding Mode Controller for Battery Supercapacitor Integration

Nowadays, most of the works are based on electric vehicle usage for sustainable transportation using traditional energy storage device, such as battery. Usage of batteries in electric vehicles is having several disadvantages, for example, life span, temperature, and charge estimation. In this paper, a novel control scheme for battery and supercapacitor- (SC-) based hybrid energy storage system (HESS) using hybrid proportional and integral- (PI-) sliding mode control (SMC) for electric vehicle (EV) applications is introduced and implemented. This HESS with hybrid controller proves the usage of batteries in EVs to its fullest potential. The conventional control strategy for HESS follows two-loop voltage and current PI controllers with low-pass filter (LPF) and involves tuning of multiple control parameters with variations of source and load disturbances. Performance of the system is affected by tuning PI controller constants. A slow response time with linear PI controllers is long which is not advisable for starting and sudden jerk conditions of EVs. Moreover, the PI controller performance is affected by the system parameter variations during load changes. And these parameters are dynamic in nature due to nonideal conditions. In this paper, a hybrid PI-sliding mode controller (SMC) scheme is designed to control the bidirectional DC-DC converters to overcome the drawbacks of aforementioned issues. The combined PI-SMC controller reduces the tuning effort and reduces the effect of shift in operating point in controller performance. Linear modeling is done using small signal analysis for each subsystems. Permanent magnet synchronous machine (PMSM) is used as electric vehicle. The entire system and its controllers are simulated using MATLAB-Simulink, and detailed comparison is carried between conventional PI and proposed hybrid PI-SMC scheme to regulate the DC link voltage. The results are tabulated and show that the hybrid PI-SMC scheme outperforms in transient and steady-state conditions than the traditional PI controller. A scaled hardware prototype of 48 W set-up is developed using dSPACE-1104, and the experimental results have been carried out to verify the proposed system’s feasibility.

Fractional High-Order Differential Feedback Controller Based on Meta-Heuristic Algorithms for Automatic Voltage Regulator

In this study, optimally parameter tuning of fractional high-order differential feedback controller (FHODFC) for an automatic voltage regulator (AVR) system regulating the terminal voltage of the synchronous generator is presented. Recently developed meta-heuristic optimization techniques named honey badger algorithm (HBA) and gorilla troops optimizer (GTO) have been employed for the first time in order to optimally determine ten different parameters of the FHODFC. The performance of the proposed controllers, that is, HBA-FHODFC and GTO-FHODFC, is analyzed in terms of transient and steady state responses, and robustness against uncertainties and external disturbances. In addition, their performance is compared to a FHODFC of which parameters were tuned by using particle swarm optimization algorithm in a previous study. The results indicate that the proposed HBA-FHODFC and GTO-FHODFC controllers provide zero overshoot and better results in terms of settling time and objective function then the PSO tuned FHODFC. Therefore, the proposed controllers can be used as an effective method in the AVR system.

A Digital Proportional–Resonant Controller With Desired Tracking-Error Convergence

Proportional-resonant (PR) controllers were initially presented within a continuous-time control framework; hence, their digital implementations may be susceptible to resonant-frequency offset and modulation-signal computational delay. Consequently, engineers may need to tune the controller parameters by trial and error to design the control systems that achieve desired performance. In digital control, sinusoidal references of power converters are discrete-time sequences. Based on this, we present a digital design technique for the PR controllers and demonstrate the design process using a 13.2-kW grid-connected inverter prototype. The design ensures the sinusoidal reference's tracking-error sequence converges at the desired rate. The advised design technique's merits include: 1) The tracking-error convergence rate, phase margin, and cut-off frequency have clear mathematical relations, allowing engineers to uniquely determine the controller parameters by specifying any two indicators. 2) With the tuned controller parameters, the tracking-error sequence can converge at the desired rate. 3) The resultant controller can be partitioned into one proportional term and two resonant terms; the first two terms are construed as a standard PR controller digitally implemented via the zero-order-hold method, whereas the second resonant term guarantees the tracking-error sequence converging in the desired manner. A case study results confirm the digital PR controller's efficacy.

An inertial control method for large-scale wind farm based on hierarchical distributed hybrid deep-reinforcement learning

Wind farm (WF) is gradually required to provide inertial support during frequency events nowadays. Existing WF inertial control methods have exhibited limitations in parameter estimation and optimal control parameter tuning under variable operating conditions. Moreover, traditional model-based methods suffer from computational efficiency problems due to their reliance on repeated iterations or derivative computations in optimization process. Thus, this paper proposes an inertial control method for large-scale WF based on hierarchical distributed hybrid deep-reinforcement learning (HDH-DRL). Firstly, the control objectives for the inertial control are defined, and are decomposed on the basis of wind turbines (WTs) division. Next, this paper presents a HDH-DRL based inertial control framework consisting of two levels. The upper-level control is achieved by the hybrid multi-agent DRL (HMA-DRL) algorithm with hybrid action exploration mechanism and multi-agent coordination. The consensus based lower-level control aims to achieve the consensus convergence of the control through the locally distributed interaction among WTs. Finally, the inertial control processes of the model are exhibited; the influences of DRL algorithms on the control are discussed; the computational speed and solution accuracy of the proposed method are compared with the model-based methods; the multi-scenario applicability and performance in local communication failures of the model are analyzed. The results demonstrate that the proposed method significantly improves the active power response and frequency stability, achieving rapid consensus convergence with a power deviation below 0.3%, surpassing traditional control methods by at least 0.2%; also, it demonstrates superior performance across various scenarios, including transient and steady-state conditions, with frequency enhancements up to 0.30% and exceptional stability under different wind conditions and communication disruptions.

Design and Evaluation of a Bilateral Semi-Rigid Exoskeleton to Assist Hip Motion.

This study focused on designing and evaluating a bilateral semi-rigid hip exoskeleton. The exoskeleton assisted the hip joint, capitalizing on its proximity to the body's center of mass. Unlike its rigid counterparts, the semi-rigid design permitted greater freedom of movement. A temporal force-tracking controller allowed us to prescribe torque profiles during walking. We ensured high accuracy by tuning control parameters and series elasticity. The evaluation involved experiments with ten participants across ten force profile conditions with different end-timings and peak magnitudes. Our findings revealed a trend of greater reductions in metabolic cost with assistance provided at later timings in stride and at greater magnitudes. Compared to walking with the exoskeleton powered off, the largest reduction in metabolic cost was 9.1%. This was achieved when providing assistance using an end-timing at 44.6% of the stride cycle and a peak magnitude of 0.11 Nm kg-1. None of the tested conditions reduced the metabolic cost compared to walking without the exoskeleton, highlighting the necessity for further enhancements, such as a lighter and more form-fitting design. The optimal end-timing aligns with findings from other soft hip exosuit devices, indicating a comparable interaction with this prototype to that observed in entirely soft exosuit prototypes.

Artificial rabbits optimization algorithm based tuning of PID controller parameters for improving voltage profile in AVR system using IoT

The power system is mainly affected by transient situations caused by switching heavy loads. The system may become unstable when transient situations occur. The power system should be able to perform continuous operation to maintain its voltage within acceptable limits. To achieve more stability and increase its speed of response, an Automatic Voltage Regulator (AVR) system requires the inclusion of a controller. The AVR system in the generating station uses the PID controller to adjust the abnormal voltage caused by transient conditions. To maintain the nominal voltage level under all the load conditions in the system, a bio-inspired meta-heuristic algorithm called Artificial Rabbit Optimization (ARO) algorithm is proposed to tune the PID controller gain parameters and obtain the optimal gain, thereby the AVR system adjusts the generator terminal voltage to nominal levels. The ARO algorithm inspires natural survival techniques to improve the AVR performance by reducing errors. To maintain a stable voltage profile in a power system, this research mathematically models survival techniques using the Internet of Things (IoT) to obtain an optimal solution. As a result, all devices connected to the power network receive a stable voltage that ensures their voltage reliability. The effectiveness of the proposed algorithm for the AVR system is verified with the MATLAB R2022a model, and the statistics functions are implemented in the module of Pandas, Scipy and mathematical investigations done in Numpy. The proposed ARO algorithm achieves a better voltage profile with less than 12.63% maximum peak overshoot during the system's transient response. The proposed algorithm provides the fastest response and highest stability comparable to other optimisation algorithms.

Tangential velocity tracking-based cross-coupled control method with variable feedrate for biaxial parametric curve following

To address the inaccurate contour error calculation problem in traditional cross-coupled control (CCC) methods for biaxial motion systems, this paper presents a novel CCC method based on a recently developed tangential velocity tracking (TVT) strategy. It has the advantage that existing high-precision algorithms for searching the foot point can be directly integrated to obtain the excellent estimation accuracy of contour error. The cumbersome parameter tuning for position controllers of each axis is unnecessary. Moreover, a velocity interpolator for parametric curves is developed to extend the TVT strategy to the variable feedrate case. The stability of the TVT-based CCC system is proved using a quadratic Lyapunov function. Comparative experiments are carried out, and the results indicate that the estimation deviation of contour error in the TVT-based CCC method can be constrained within 1 μm. The maximum contour error is significantly reduced by 65.32% and 50.10% compared with the traditional CCC methods based on tangential and circular approximations, respectively.

A Small-Signal Stability Analysis Method Based on Minimum Characteristic Locus and Its Application in Controller Parameter Tuning

A small-signal stability analysis method based on minimum characteristic locus (MCL) was recently proposed by the authors. The method is found to be particularly useful in analyzing various oscillation modes in a unified way. The present work continues this line of research. After providing a brief review of the basic principle of MCL method, the paper proceeds to show the mathematical relationship between the stability margin obtained using an MCL method, and that obtained by traditional modal analysis method. Next, a sensitivity analysis procedure is proposed to study quantitatively the effects of control parameters on the stability of the system. Third, the properties of the so-called exciter mode are explained in detail, in particular, explicit formulas of exciter mode frequency and power system stabilizer (PSS) critical gain are derived. Finally, taking PSS4B as an example, a controller parameter tuning method is proposed. The effectiveness and practicability of the proposed method are verified by case studies of Kundur's four-machine-two-area system and China's Yunnan power grid.

Gray Wolf, Mikro Şebekede Pil Depolama Dahil Optimize Edilmiş Ekonomik Yük Dağıtımı

In last decades, grey wolf optimizer algorithm as a new meta-heuristic optimization technique plays major role in optimization of engineering problems such as load forecasting, controller parameter tuning and job scheduling. In this paper, grey wolf optimization (GWO) is used to optimize the microgrid system for effective dispatching of power to load with economic manner. The model of microgrid system components are developed and investigated in the MATLAB/Simulink platform. The vital objective of the proposed grey wolf algorithm is to minimize overall cost of the microgrid operation. The detailed investigation is carried out on power dispatch optimization and cost minimization for both modes, i.e. island and grid-connected modes of the microgrid system with considering the impact of running costs. From the analysis, the cost of the overall system is optimized effectively, and load sharing is done effectively by means of GWO.

Mechanical resonance suppression of the high-speed parallel robot based on fractional order disturbance observer

Considering the flexibility of the transmission shaft system, this paper presents an approach for mechanical resonance suppression of the high-speed parallel robot based on fractional order disturbance observer. First, in order to evaluate mechanical resonance, a decoupled mechatronic dynamics model is built. Based on the proposed decoupled dynamics model, the mechanical resonance under different influence factors is analyzed. Then, based on the three closed-loop control structure of servo system, a model-based parameter tuning method is adopted in the controller parameter tuning. Finally, inspired by the traditional disturbance observer, a fractional order disturbance observer is established by introducing a fractional order filter. Results show that due to the flexibility of the transmission shaft system, under the combined action of mechanical disturbance torque and electromagnetic driving torque, the joint transmission system will generate mechanical resonance phenomena. Therefore, this paper proposes a method for the mechanical resonance suppression of the high-speed parallel robot based on the fractional order disturbance observer. And the proposed method can be applied to other mechatronic device including flexible transmission shaft system.

Performance-Driven Safe Bayesian Optimization for Intelligent Tuning of High-Order Cascade Systems

Automatic parameter tuning of high-order cascade controllers suffers from sampling inefficiency and strong couplings. This work presents a performance-driven, systematic, and safe intelligent parameter-tuning framework for high-order cascade systems. To achieve data-efficient and noise-robust hyper-parameter calibration, an intelligent tuning framework based on Bayesian optimization is proposed to calibrate the control parameters from the buffer of performance-metric measurements. Furthermore, we improve the Bayesian-optimization-based framework in three aspects, involving effectiveness, security, and sampling efficiency. Firstly, a comprehensive control performance assessment combining the error-integral and statistical performance criteria is designed to evaluate the cost of a sampling point in terms of final precision, response rapidity, and vibration. Meanwhile, the security of sampling exploration is heightened by imposing composite hard parametric and soft performance-metric (maximum input, overshoot, etc.) constraints on the acquisition points. Additionally, a hierarchical optimization strategy is proposed to further boost sampling exploitation by alternatively tuning the control parameters of subsystems and refining the constraint on the sampling space. The proposed framework is applied to automatic parameter tuning of high-order dynamic-surface-based backstepping control combined with anti-disturbance rejection control. The comparative simulation results demonstrate that the proposed intelligent tuning possesses superiorities in sampling efficiency and security for parameter calibration problems.

Modeling and Deep Reinforcement Learning Based Control Parameter Tuning for Voltage Source Converter in a Renewable Energy Generation System

Modeling and Deep Reinforcement Learning Based Control Parameter Tuning for Voltage Source Converter in a Renewable Energy Generation System

Homogeneous Unit Sliding Mode Control

The methodology of the unit sliding mode control design (known since 1970s) for linear systems is revised based on the concept of the generalized homogeneity. The restriction about a consistency of the number of control inputs with the dimension of the sliding surface is eliminated. A simple procedure of control parameters tuning based on a known maximal magnitude of matched perturbations is developed. To deal with perturbations of unknown magnitude, a homogeneous sliding mode control with integral action is designed as well. The theoretical results are supported by numerical simulations.

Research on trajectory tracking control of ocean unmanned aerial vehicles based on disturbance observer and nonlinear sliding mode

This paper aims to solve the problem of real time tracking of unmanned aerial vehicle (UAV) with external disturbances, model uncertainty, and multiple control parameter tuning during wave glider (WG) navigation. Firstly, the dynamic model of UAV and WG is instituted; Secondly, an Expanded State Observer (ADOB) is proposed to estimate the total perturbations in real time, including model uncertainties and external wind perturbations; Moreover, the ADOB is integrated with a nonlinear integral sliding mode control to generate a nonlinear sliding mode controller (ASMC) with a compound disturbance observer for the trajectory tracking control strategy; Finally, the bandwidth parameterization theory is used to tune the parameters of the ASMC controller. Simulation and experimental results show that the UAV trajectory tracking landing control scheme proposed in this paper can effectively solve the real-time tracking problem of the UAV during WG navigation.

Advanced modeling and control of wind conversion systems based on hybrid generators using fractional order controllers

AbstractThe focus of this study is on modeling and management of a Wind Energy Conversion System (WECS) based on a Hybrid Excitation Synchronous Generator (HESG). Using a wind simulator, two controllers, CRONE and H∞, are evaluated for Maximum Power Point Tracking (MPPT) and optimal rotation speed. The results show the CRONE controller's higher tracking capability, and robustness tests investigate the influence of parametric uncertainty. Space harmonic effects, commutation effects, and turbine shaft flexibility are all addressed in the sophisticated models. The introduction of Fractional Order Proportional‐Integral (FOPI) control for MPPT is a game changer. Although fractional calculus‐based accuracy and robustness are uncommon in WECS, they show promise for emissions reduction and increased energy efficiency. This work validates a dependable control approach inside an isolated HESG‐based WECS's power‐maximizing range. Extensive investigation of the parameters impacting FOPI control efficiency yields useful insights for effective MPPT control. A variable‐speed wind turbine (WT) is linked to a HESG through a multiplier, with a controller controlling generator coil excitation voltages and a rectifier connecting the WECS to a load. A thorough 3 kW HESG electrical model that includes generator space harmonics and converter commutation effects is among the contributions. For HESG‐based WECS stability, frequency analysis finds important resonant and anti‐resonant frequencies. These issues are addressed by a FOPI control technique, which ensures system stability and performance. The necessity of exact frequency analysis in HESG‐based WECS is highlighted by a step‐by‐step controller parameter tuning approach.

Robust self‐adaptive fuzzy controller for load‐frequency control of islanded airport microgrids considering electric aircraft energy storage and demand response

AbstractThe rise of the renewable energy sources (RES) in microgrids has increased the impact of damping and low inertia on network stability. This gives rise to several issues in the power systems, including power fluctuations from variable RES, degradation of the frequency regulation, voltage surges, and oversupply from grid‐connected distributed generators. This paper presents a cascaded hybrid load‐frequency controller for an airport microgrid that addresses frequency fluctuations caused by load changes. It is combining a self‐adaptive fuzzy controller (SAF) and a classical two‐degree‐of‐freedom proportional integral derivative (PID) controller (2DOFPID) called CSAF‐2DOFPID. It is applied to the control section of a diesel generator, an electric aircraft energy storage system, and a demand response program. The controller parameters are optimized using a suggested hybrid differential artificial hummingbird algorithm (D‐AHA) to minimize frequency deviation. The proposed control framework is simulated on an airport microgrid comprising various components, and the impact of time delay on the controller's signals, system uncertainty is also examined. The simulation results demonstrate that the proposed self‐adaptive cascaded fuzzy controller outperforms other controllers such as PID, CF‐PID, and CF‐2DOFPID in reducing frequency fluctuations, and the D‐AHA algorithm achieves favourable results compared to other algorithms in the optimal tuning controller parameters.