Self-tuning Control Research Articles

Modelling and simulation of MR damper characterization and uncertainty assessment in marine engine vibration isolation with FLPID control

ABSTRACT This paper meticulously examines the magneto-rheological (MR) damper to exploring its application in a semi-active isolator system. The MR damper’s distinctive capability to regulate damping force through an externally applied magnetic field is investigated via extensive experimental testing. Sinusoidal displacement inputs with varying electromagnet currents reveal a direct correlation between higher current levels and augmented MR damper forces. Validation of the experimental results is achieved through the Viscous Plus Dahl Model, showcasing its accuracy in capturing the damper’s hysteresis behavior. The study extends its scope to practical implementation by integrating the MR damper into a marine diesel engine system. A fuzzy logic-based self-tuning PID controller collaborates with the MR damper to establish a semi-active vibration isolator. The integrated model, encompassing primary and secondary masses, the engine, and the isolator with passive and semi-active systems, is rigorously analyzed. Experimental validation and theoretical simulations, aligned with data gathered from experiments, affirm the model’s fidelity in representing the MR damper. Simulation outcomes underscore the significant enhancement in vibration reduction characteristics of the semi-active isolator system compared to its passive counterpart, particularly evident in the improved performance of the 1200 kg engine under diverse operating conditions.

Enhancing Urban Mobility with Self-Tuning Fuzzy Logic Controllers for Power-Assisted Bicycles in Smart Cities.

In smart cities, bicycle-sharing systems have become an essential component of the transportation services available in major urban centers around the globe. Due to environmental sustainability, research on the power-assisted control of electric bikes has attracted much attention. Recently, fuzzy logic controllers (FLCs) have been successfully applied to such systems. However, most existing FLC approaches have a fixed fuzzy rule base and cannot adapt to environmental changes, such as different riders and roads. In this paper, a modified FLC, named self-tuning FLC (STFLC), is proposed for power-assisted bicycles. In addition to a typical FLC, the presented scheme adds a rule-tuning module to dynamically adjust the rule base during fuzzy inference processes. Simulation and experimental results indicate that the presented self-tuning module leads to comfortable and safe riding as compared with other approaches. The technique established in this paper is thought to have the potential for broader application in public bicycle-sharing systems utilized by a diverse range of riders.

A Hybrid Fuzzy Predictive DVR Model for Voltage Estimation Using Intelligent Learning

A self-constructing intelligence-based learning law is proposed for the prediction of power quality mitigation through a dynamic voltage restorer (DVR). To tackle voltage-based power quality concerns in the distribution network, a fuzzy neural-based hybridization control approach using an evolving Takagi–Sugeno–Kang (e-TSK) and radial basis function (RBFN) is developed. The estimated value of fundamental weight components is achieved by the eTSK predictive model. The evaluated weight values are used to update the parameters for the generation of the reference load voltage components. The RBFN is used to achieve the desired task of dc and ac link voltage regulation. The self-tuned control algorithm is developed based on an intelligence technique that avoids the trapping of local optima, and early convergence, and reduces the voltage oscillation to obtain an optimal solution. A meta-heuristic optimization approach of grey wolf optimization (GWO) and grey wolf-cuckoo search (GWOCS) is selected to optimize the parameters of eTKS and RBFN to find the best-fitted predictive model under the dynamic state. The hybridization of the model with GWO and GWOCS is implemented for improving the training and generalization capability. The performance of VSC-based DVR is validated using the d-SPACE Micro lab box under the various power quality scenario.

Self-tuning PID feedback control method for magnetic suspension active vibration isolation system with parameters uncertainty

A self-tuning PID (Proportion-Integral-Derivative) control method based on the adaptive mechanism was proposed for the magnetic suspension active vibration isolation system with the characteristics of nonlinear, time-varying, and model parameter uncertainty. The controller identified the controlled object parameters online and solved the controller parameters in real time. To test the effectiveness of the proposed method, a six degree-of-freedom (DOF) magnetic suspension active vibration isolation system was designed and built. The six DOF dynamic model of the vibration isolation system with uncertain model parameters was derived for control. Experiments were carried out and compared with the control effects of the tradition PID control and cascade PID control algorithm. The experimental results show that the proposed self-tuning PID controller has more outstanding practicality and effectiveness in solving the model parameter uncertainty problem compared with the existing studies at home and abroad, and it also proves that the control method has a good isolation effect on the wide band disturbances.

Risk-Quantified Autonomous Maintenance of Robotaxis Using Self-Tuning Model Predictive Control

Risk-Quantified Autonomous Maintenance of Robotaxis Using Self-Tuning Model Predictive Control

Adaptive PI control of temperature with natural ventilation in greenhouses using a bat algorithm variant

This paper presents a self-tuning PI controller to control the temperature of a greenhouse using natural ventilation. The PI controller parameters are adapted according to the changing dynamics of the process, identified with a simplified greenhouse temperature model based on first principles. The time-varying model parameters are estimated online using the random scaling-based bat algorithm. The model is linearized to obtain a first-order transfer function which facilitates the design of the PI controller using well-known tuning methods. Simulated results with real greenhouse data show that the proposed solution could be applied to keep controllers tuned throughout different agri-seasons.

Parallel Self-Tuning Controllers for Speed Servo System Based on Predictive Model-Mismatch Compensation

Parallel Self-Tuning Controllers for Speed Servo System Based on Predictive Model-Mismatch Compensation

A Self-Tuning ANFIS DC Link and ANN-LM Controller Based DVR for Power Quality Enhancement

A Self-Tuning ANFIS DC Link and ANN-LM Controller Based DVR for Power Quality Enhancement

Assessment of Novel Self-tuned Emotional Learning-based Intelligent Controller for Bidirectional Cascaded Quasi-Z-Source DC–DC Converter Control

The self-tuned emotional learning-based intelligent controllers (STELBICs) are suggested to regulate a bidirectional cascaded quasi-Z-source DC-to-DC converters (BC-qZSCs). The bidirectional converters of DC-to-DC with a huge voltage conversion range are required in current times for voltage matching in a variety of applications, particularly electric automobiles. The BC-qZSC may raise or buck the voltage, and electricity flows in both directions. The switch’s duty cycle can be changed to keep the voltage steady. To produce a consistent output voltage, a good controller design is necessary. To enhance the performance of BC-qZSC in both operating modes, this study introduces a unique STELBIC. The proposed novel controller’s performance is evaluated by comparing it with an artificial neural networks-controlled converter and a traditional Proportional Integral-controlled converter. In both boost and buck modes of operation, the controller’s performance is evaluated in terms of voltage quality, including steady-state error, settling time, and voltage ripple. Matlab/Simulink is used to analyze the entire system.

Self-Tuning Controller Using Shifting Method

This paper presents a newly implemented self-tuning PID controller that uses a relay feedback identification using a recently designed relay shifting method to determine the mathematical model of the process and subsequently adjust the controller parameters. The controller is applicable to proportional and integrating systems and is even applicable to systems with transport delays if steady-state oscillation can be achieved in the relay control of the system. After briefly introducing the relay shifting method, the current paper describes the hardware (HW) and software (SW) of the proposed controller in detail. The relay feedback identification and control of a laboratory setup by an automatically tuned controller is demonstrated on a real laboratory device called “Hot air tunnel”. The evaluation of the experiment and the characteristics of the controller are presented at the end of the paper. The advantage of the relay method is that it is not as computationally intensive as other identification methods. It can thus be implemented on more energy-efficient microcontrollers, which is very important nowadays.

A new adaptive MPPT technique using an improved INC algorithm supported by fuzzy self-tuning controller for a grid-linked photovoltaic system.

Solar energy, a prominent renewable resource, relies on photovoltaic systems (PVS) to capture energy efficiently. The challenge lies in maximizing power generation, which fluctuates due to changing environmental conditions like irradiance and temperature. Maximum Power Point Tracking (MPPT) techniques have been developed to optimize PVS output. Among these, the incremental conductance (INC) method is widely recognized. However, adapting INC to varying environmental conditions remains a challenge. This study introduces an innovative approach to adaptive MPPT for grid-connected PVS, enhancing classical INC by integrating a PID controller updated through a fuzzy self-tuning controller (INC-FST). INC-FST dynamically regulates the boost converter signal, connecting the PVS's DC output to the grid-connected inverter. A comprehensive evaluation, comparing the proposed adaptive MPPT technique (INC-FST) with conventional MPPT methods such as INC, Perturb & Observe (P&O), and INC Fuzzy Logic (INC-FL), was conducted. Metrics assessed include current, voltage, efficiency, power, and DC bus voltage under different climate scenarios. The proposed MPPT-INC-FST algorithm demonstrated superior efficiency, achieving 99.80%, 99.76%, and 99.73% for three distinct climate scenarios. Furthermore, the comparative analysis highlighted its precision in terms of control indices, minimizing overshoot, reducing rise time, and maximizing PVS power output.

A Novel Data-Driven Self-Tuning SVC Additional Fractional-Order Sliding Mode Controller for Transient Voltage Stability With Wind Generations

This paper proposes a novel data-driven self-tuning additional sliding mode controller for power system transient voltage stability enhancement considering wind integration. We first develop a new additional fractional-order sliding mode controller (FOSMC) for the static var compensator (SVC). The tuning of FOSMC parameter settings is then reformulated as a Markov decision process (MDP) and solved by the deep reinforcement learning (DRL) algorithm. In addition, a data-driven estimation method is proposed for the identification of an equivalent transfer function that is further used to calculate reward during the training process, yielding the model-free training. After that, the well-trained agent allows us to tune the controller parameters considering system uncertainties and achieve robustness against various operating conditions. Comparative results with other state-of-art methods demonstrate that the proposed method can effectively suppress the chattering issue of the sliding mode controller and ensure transient voltage stability under different operating conditions.

Adaptive and intelligent control of a dual-arm space robot for target manipulation during the post-capture phase

Dual-arm space robots provide a promising solution for efficiently and flexibly completing complex on-orbit servicing tasks. However, their effectiveness is often hindered by strong dynamic coupling between the robotic arms and the satellite base in the microgravity environment. In this paper, both robotic arms are used as task arms, and a system model that considers the closed-loop constraint formed after a dual-arm space robot captures a target is established. On this basis, two types of control strategies, namely self-tuning control strategy (STC) and radial basis function neural network control strategy (RBF) are proposed for trajectory tracking of the target transferred or detumbled by the robot arms in the presence of system uncertainty or external disturbances. The control methods are verified and compared against the traditional computed torque control strategy (CTC) in terms of control accuracy and system robustness through simulation. The results demonstrate the excellent robustness of both RBF and STC strategies. RBF outperforms the other strategies in terms of control accuracy, while STC proves to be the most energy-efficient.

Embodying Multifunctional Mechano-Intelligence in and Through Phononic Metastructures Harnessing Physical Reservoir Computing.

Recent advances in autonomous systems have prompted a strong demand for the next generation of adaptive structures and materials to possess built-in intelligence in their mechanical domain, the so-called mechano-intelligence (MI). Previous MI attempts mainly focused on specific case studies and lacked a systematic foundation in effectively and efficiently constructing and integrating different intelligent functions. Here, a new approach is uncovered to create multifunctional MI in adaptive structures using physical reservoir computing (PRC). That is, to concurrently embody computing power and the key elements of intelligence, namely perception, decision-making, and commanding, directly in the mechanical domain, advancing from conventional reliance on add-on computers and massive electronics. As an exemplar platform, a mechanically intelligent phononic metastructure is developed by harnessing its high-degree-of-freedom nonlinear dynamics as PRC power. Through analyses and experiments, multiple intelligent structural functions are demonstrated ranging from self-tuning wave controls to wave-based logic gates. This research provides the much-needed basis for creating future smart structures and materials that greatly surpass the state of the art-such as lower power consumption, more direct interactions, and better survivability in harsh environments or under cyberattacks. Moreover, it enables the addition of new functions and autonomy to systems without overburdening the onboard computers.

Pressure control for pneumatic pressing roller in automated fiber placement

In automated fiber placement (AFP), the contact pressure between the roller and the mold is an important process parameter. Too small or too large contact pressure will result in unacceptable laying defects. This paper focuses on the control method of contact pressure in AFP. Firstly, the dynamic model of the pressing foot mechanism is established, and a mathematical relationship of input air pressure and output force is developed. Then, a self-tuning PI control algorithm is used to implement servo control of the roller output force based on the Slowly Time-Varying Recursive Identification Algorithm. Also, using Abaqus/CAE, the mapping relationship between the contact area and measurements from displacement sensors is established to predict the contact area in real time. Finally, the contact pressure control method is established by combining the servo control method of contact force and the prediction method of contact area. Experimental results show that the constant pressure control method can effectively stabilize the contact pressure, with a pressure fluctuation of less than 4% under the experimental conditions set in this paper.

Self-Tuning PID Controller for Quadcopter using Fuzzy Logic

Tracking has become a necessary feature of a drone. This is due to the demand for drones, especially quadcopters, to be used for activities such as surveillance, monitoring, and filming. It is crucial to ensure the quadcopters perform the tracking with stable flight. Despite the advantages of having VTOL ability and great maneuverability, quadcopters require an effective controller to overcome their under-actuation and instability behavior. Even though a PID controller is commonly used and promising with its simple mechanism, it requires very proper tuning to ensure the stability of the system is not affected. In this paper, a simple Fuzzy algorithm is proposed to be incorporated into a PID controller to form a self-tuning Fuzzy PID controller. The Fuzzy logic controller works as the self-adjuster to the PID parameters. A mathematical model of the DJI Tello quadcopter is derived with position and attitude control loops that are designed to track a variety of trajectories with stable flight. The proposed method uses a simple architecture where the ranges of PID parameters are used as scaling factors for Fuzzy controller outputs. The results of the simulations show the tracking error performance metrics, which are IAE, ISE, and RMSE, are smaller compared to the values of the PID controller. Beyond its impact on quadcopter control, the proposed self-tuning approach holds promise for broader applications in nonlinear systems.

Perancangan Self Tuning Dua Derajat Kebebasan dengan Metode Fuzzy Logic pada Pengendalian Kecepatan Motor Sinkron Magnet Permanen Menggunakan Metode Vector Control

Abstract — This paper proposes self tuning Proportional-Integral/Proportional gain controller parameter in a Two Degree of Freedom (2DoF) using fuzzy logic method for speed control of permanent magnet synchronous motor. The self tuning method can automatically determine the PI-P parameters using a fuzzy logic control system. Speed control of the 3-phase permanent magnet synchronous motor is carried out by changing the 3-phase system (a-b-c) to a 2-phase system (direct-quadrature) using the vector control method. SVPWM method is used to modulate six switches on a 3 phase inverter. The results obtained using MATLAB Simulink show that by using a self-tuning fuzzy logic control system, high motor performance results are obtained, where the speed response of the permanent magnet synchronous motor shows that the step response and disturbance response are close to the ideal response.
 Keywords— Permanent Magnet Synchronous Motor, Self Tuning, Two Degrees of Freedom, Fuzzy Logic, Vector Control.

Compliant Grasping Control for a Tactile Self-Sensing Soft Gripper.

Soft grippers with good passive compliance can effectively adapt to the shape of a target object and have better safe grasping performance than rigid grippers. However, for soft or fragile objects, passive compliance is insufficient to prevent grippers from crushing the target. Thus, to complete nondestructive grasping tasks, precision force sensing and control are immensely important for soft grippers. In this article, we proposed an online learning self-tuning nonlinearity impedance controller for a tactile self-sensing two-finger soft gripper so that its grasping force can be controlled accurately. For the soft gripper, its grasping force is sensed by a liquid lens-based optical tactile sensing unit that contains a self-sensing fingertip and a liquid lens module and has many advantages of a rapid response time (about 0.04 s), stable output, good sensitivity (>0.4985 V/N), resolution (0.03 N), linearity (R2 > 0.96), and low cost (power consumption: 5 mW, preparation cost <CNY 100). The proposed force controller for the soft gripper was designed based on the Hammerstein nonlinear model, and due to adaptive laws designed by an adaptive theory and the full online sequential extreme learning machine, respectively, its parameters can be adjusted online. The simulation and experiment results demonstrate that the proposed force controller exhibits good force control performance and robustness in a nonlinear contact environment. Moreover, because of its simple control structure and good online learning ability, the proposed controller has the advantages of being real time and easy to realize, which shows its potential applications in many grasping tasks, such as collecting biological samples and sorting industrial products.

Servo and Regulatory Response of an Industrial Fluid Catalytic Cracking (FCC) Unit under Fuzzy Logic Supervisory Control

A self-tuning hierarchical controller in which a Fuzzy logic controller supervises the control actions of a conventional PID has been proposed, implemented and presented in this paper. The controller has been applied to a control study of Fluid Catalytic Cracking unit (FCCU) riser temperature, and regenerator temperature respectively. Comparison between the performance of the proposed Fuzzy-PID controller and the conventional PID was made in simulation studies of regulatory and servo performances of the two controller types. Six performance measures: Percent overshoot (OS), settling time (ST), integral absolute error (IAE), integral square error (ISE), integral time absolute error (ITAE) and integral time square error (ITSE) were employed as the tools for performance comparison between the conventional PID and the Fuzzy-PID controller. For the tracking of riser temperature with a set point at 524oC, the performance indicators under PID control gave the following results overshoot (14.5%); settling time (40 seconds) Integral absolute error (8.246), integral square error (3.3); integral time absolute error(1762);integral time square error (43.77) while for the same indicators under Fuzzy-PID control the following values: overshoot (3.3%); settling time (40 seconds) ;Integral absolute error (8.811); integral square error (14.5); integral time absolute error(280),;integral time square error (31.11) .The results allude to the superiority of the fuzzy-PID scheme over the PID scheme in tracking the optimal set point of riser temperature. More so, for tracking the regenerator set point temperature of 746oC, comparative study of step response under the two schemes gave the following results in six performance indicators: overshoot (PID (12.6%) / Fuzzy-PID (6%)); settling time (PID (80 seconds) / Fuzzy-PID (20seconds)); Integral absolute error (PID (14.29) / Fuzzy-PID (8.63)); integral square error (PID(6.713). Fuzzy-PID (4.506)); integral time absolute error(PID(2858)/Fuzzy-PID(305.9)), integral time square error (PID(77.55)/Fuzzy-PID(33.05)) . Moreover, the fuzzy-PID controller also showed superior performance over the conventional PID controller in terms disturbance rejection (regulatory response) of both riser and regenerator temperature. The results from this study suggest that the application of fuzzy-PID scheme to temperature control offers good promise of improved fluid catalytic cracking unit (FCCU) operations.

Self-tuning regulatory controller of cyclical disturbances using data-driven frequency estimator based on fuzzy logic

This paper proposes two learning-type regulatory controllers: adaptive fuzzy iterative learning controller (AF-ILC) and adaptive fuzzy repetitive generalized predictive controller (AFR-GPC). The proposed controllers can reject the cyclical disturbances with small range frequency variations present in many control loops of industrial processes. Moreover, they can estimate the disturbance cycle using fuzzy logic with rules based on the previous values of the integral of the absolute error between the setpoint and output. The estimated period of the disturbance cycle is sent to the regulatory controllers to define the specific control actions required to reduce the oscillations in the process output. The advantage of this technique is that adaptive controllers are automatically adjusted by a frequency estimator, which only depends on process output data. The performance levels of the developed adaptive controllers are compared in the computational simulator of a nonlinear mold level of a continuous casting process employed in the steel industry. This process counteracts the periodic oscillations in the mold level caused by bulging disturbance. The numerical results show that both AF-ILC and AFR-GPC can reduce the oscillations in the output when the disturbance frequency changes by up to 5.2% and 13%, respectively.