Self-tuning Control Research Articles

Synthesis and Soft Implementation of Supervisory Control Scheme in an Industrial Fluid Catalytic Cracking (FCC) Unit of a Nigerian Refinery

In this paper, 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. The controller has been applied to a control study of Fluid Catalytic Cracking (FCC) unit riser temperature, and regenerator temperature respectively. Performance comparison 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.

High porosity PEG-based hydrogel foams with self-tuning moisture balance as chronic wound dressings.

A major challenge in chronic wound treatment is maintaining an appropriate wound moisture balance throughout the healing process. Wound dehydration hinders wound healing due to impeded molecule transport and cell migration with associated tissue necrosis. In contrast, wounds that produce excess fluid contain high levels of reactive oxygen species and matrix metalloproteases that impede cell recruitment, extracellular matrix reconstruction, and angiogenesis. Dressings are currently selected based on the relative amount of wound exudate with no universal dressing available that can maintain appropriate wound moisture balance to enhance healing. This work aimed to develop a high porosity poly(ethylene glycol) diacrylate hydrogel foam that can both rapidly remove exudate and provide self-tuning moisture control to prevent wound dehydration. A custom foaming device was used to vary hydrogel foam porosity from 25% to 75% by adjusting the initial air-to-solution volume ratio. Hydrogel foams demonstrated substantial improvements in water uptake volume and rate as compared to bulk hydrogels while maintaining similar hydration benefits with slow dehydration rates. The hydrogel foam with the highest porosity (~75%) demonstrated the greatest water uptake and rate, which outperformed commercial dressing products, Curafoam® and Silvercel®, in water absorption, moisture retention, and exudate management. Investigation of the water vapor transmission rates of each dressing at varied hydration levels was characterized and demonstrated the dynamic moisture-controlling capability of the hydrogel foam dressing. Overall, the self-tuning moisture control of this hydrogel foam dressing holds great promise to improve healing outcomes for both dry and exudative chronic wounds.

A self-tuning PID controller based on analog-digital hybrid computing with a double-gate SnS2 memtransistor.

Most commercial drones utilize a traditional proportional-integral-derivative (PID) controller because of its design simplicity. However, the traditional PID controller has certain limitations in terms of optimality and robustness; it is difficult to actively adjust the PID gains under some disturbances. In this study, we demonstrated an analog-digital hybrid computing platform based on double-gate SnS2 memtransistors to implement a self-tuning/energy-efficient PID controller in drones. The customized analog circuit with memtransistors executes the PID control algorithm with low power consumption; we experimentally verified that the energy consumption of the proposed hybrid computing-based PID controller is only 63% of that of the traditional PID controller. In addition, the precise tunability of analog conductance states in the memtransistor proved to be capable of reconfiguring the performance of the PID controller, where the developed self-tuning algorithm can automatically find the optimal PID control performance.

Automated system for control and monitoring of explosion safety of the coal grinding process in ball drum mills

The article presents the results of research on automated systems of control and monitoring of explosion safety of dust and gas mixture during coal grinding in ball drum mills (BDM). The problem of the gas-dynamic factor, which arises at the disintegration of coals of middle stage of metamorphism (gas coals) in the atmosphere of closed or limited space, for example in mine workings, chambers, furnace furnaces, etc., is considered. Earlier, when burning anthracite coals, such a problem did not arise, since anthracite does not have a gas component of hydrocarbons. Therefore, all automated control systems were developed from the condition of grinding quality and optimal loading of the BDM. The work aims to develop an automated system of control and monitoring of explosion safety of coal-gas mixture in ball drum mills in the process of grinding gas coals in preparation for combustion. It is established that the sizes of coal grinding fractions in the BDMs from 50 microns to 250 microns correspond to the sizes of supermelonites from 10 microns to 200 microns to particles that are formed during sudden coal and gas emissions in mines. Coal supermelonites are the main source of gas emission into the atmosphere of mine workings, as at their formation there is the disintegration of the supramolecular organization of coal macromolecule and generation of methane. Crushing of gas coals in the BDM inevitably leads to the formation of the explosive methane-coal mixture, which further enters the furnace of the boiler unit. As a result, a new functional scheme for automatic regulation and optimization of parameters of the BDM with a measuring and computing system for controlling the temperature of the gas medium, pressure drop, and a self-tuning microprocessor controller was developed. A technological scheme for installing control and measuring equipment has been developed for preparing coal for burning in a thermal power plant boiler unit. Explosion safety control in the technological cycle of preparing gas coals for burning is carried out constantly using methane and oxygen concentration sensors. The operability of the control system was confirmed on the industrial mill of the boiler unit BKZ-160-100PT TPP of "Kramatorskteploenergo" TPP. Keywords: ball drum mill, coal grinding, methane, explosion safety, control system.

An ADRC Parameters Self-Tuning Control Strategy of Tension System Based on RBF Neural Network

High precision control of substrate tension is the premise and guarantee for producing high-quality products in roll-to-roll precision coating machine. However, the complex relationships in tension system make the problems of decoupling control difficult to be solved, which has limited the improvement of tension control accuracy for the coating machine. Therefore, an ADRC parameters self-tuning decoupling strategy based on RBF neural network is proposed to improve the control accuracy of tension system in this paper. Firstly, a global coupling nonlinear model of the tension system is established according to the composition of the coating machine, and the global coupling model is linearized based on the first-order Taylor formula. Secondly, according to the linear model of the tension system, a parameters self-tuning decoupling algorithm of the tension system is proposed by integrating feedforward control, ADRC and RBF. Finally, the simulation results show that the proposed tension control strategy has good decoupling control performance and effectively improves the tension control accuracy for the coating machine.

Hybrid Intelligent Controller Design and Stability Analysis for Controlling Boeing-767 Dynamics using Matlab

The main objective of this research is to provide better controlling and stability to B767 aircraft, where a study of elevators for pitch control for the described longitudinal movement is conducted. Intelligent controllers are designed and introduced to the surfaces of the linearized model of B767 aircraft, to identify the performance level based on time response and stability analysis by using different transfer functions. Self-tuning PID Controller and designed hybrid intelligent controller with a combination of PID controller and fuzzy controller are proposed along with few sensors installation to obtain results. A comparative assessment and quantitative analysis of controllers are carried out using MATLAB Simulink. This research has shown that the performance of the aircraft has improved significantly.

Self-Tuning WEC Controller for Changing Sea States

A self-tuning proportional-integral control law prescribingmotor torques was tested in experiment on a threedegree-of-freedom wave energy converter. The control objectivewas to maximize electrical power. The control law relied uponan identified model of device intrinsic impedance to generate afrequency-domain estimate of the wave-induced excitation forceand measurements of device velocities. The control law was testedin irregular sea-states that evolved over hours (a rapid, butrealistic time-scale) and that changed instantly (an unrealisticscenario to evaluate controller response). For both cases, thecontroller converges to gains that closely approximate the postcalculatedoptimal gains for all degrees of freedom in a sufficientlyshort-time for realistic sea states. In addition, electricalpower was found to be relatively insensitive to gain tuning overa broad range of gains, implying that an imperfectly tunedcontroller does not result in a large penalty to electrical powercapture. Because the controller relies on an identified model ofdevice intrinsic impedance, the sensitivity of power capture wasevaluated with respect to uncertainty in the constituent termsof intrinsic impedance. Power capture is found to be relativelyinsensitive to uncertainty of 20% in constituent terms of theidentified intrinsic impedance model. An extension of this controllaw that allows for adaptation to a changing device impedancemodel over time is proposed for long-term deployments, aswell as an approach to explicitly handle constraints within thisarchitecture.

Filtered-error RLS for self-tuning disturbance feedforward control with application to a multi-axis vibration isolator

High performance vibration isolation can be realized by disturbance feedforward control with a self-tuning generalized FIR filter and residual noise shaping. For this application, filtered-error recursive least squares (FeRLS) self-tuning is proposed in a multi-input multi-output context. In comparison to filtered-error least mean squares (FeLMS), FeRLS achieves faster and more uniform parameter convergence without the need of pre-whitening. Efficient implementation is realized by exploiting sparsity in the involved matrices. Feasibility of implementation is demonstrated on a multi-axis hard-mount vibration isolation setup. Experimental results show the better parameter convergence and the ability to track changes in the floor vibration spectrum. A reduction of the transmissibility of floor vibrations up to 40 dB in the frequency range of interest is obtained, reducing vibration power by 90–94% in the 1–300 Hz frequency band in multiple directions.

2-DoF Self-tuning Fuzzy PID Controller for Under-actuated Inverted Pendulum System with Uncertainties in Model

2-DoF Self-tuning Fuzzy PID Controller for Under-actuated Inverted Pendulum System with Uncertainties in Model

High-Precision Aircraft Guidance System with Axial Acceleration Self-Tuning

Creation of aircraft control systems that provide high quality guidance is an urgent task associated with increasing efficiency of the modern missile systems. Classical autonomous guidance systems without integration with any other external correction systems are making it impossible to ensure high-precision target engagement. An approach based on the use of adaptive guidance system is proposed. Besides, an approach to synthesis and analysis of the self-tuning aircraft control system that implements terminal homing is illustrated. A technique for forming the control system structure is presented ensuring the control quality constant level in all operating modes due to self-tuning of the constituent elements variable coefficients. Structural schemes and control equations were determined to correct the aircraft flight. Implementation of the developed technique on board the aircraft is proposed determining relationship between the aircraft control system parameters and the apparent acceleration. Adaptive system operation is demonstrated on a typical model of the aircraft flying in the atmosphere with guidance at the stationary target. Results of numerical simulation are presented, and high efficiency of the developed technique is revealed

Simulation on control system of the parallel ankle rehabilitation mechanism driven by Pneumatic Artificial Muscles

The ankle is the most weight-bearing joint in the body, which plays a vital role in ensuring people’s walking. Aiming at the problems of complex mechanism, rigid drive and difficult control of the current ankle joint rehabilitation equipment, a 3-SPS/S type ankle parallel rehabilitation mechanism based on pneumatic artificial muscles is developed, and a self-tuning PID controller based on BP neural network is proposed to improve the accuracy of the control system. According to the static characteristics of the pneumatic artificial muscle, the physicians mathematical model of the artificial muscle is optimized, the kinematic position inverse solution of the ankle joint parallel rehabilitation mechanism is analysed and the control system is established. Combined with traditional PID control and BP neural network control algorithm, the BP_PID controller is completed, the control system is simulated and analysed. The simulation results show that BP_PID controller can meet design requirements for the ankle joint parallel rehabilitation mechanism, and is obviously superior to the PID controller in response speed and control accuracy.

Study on torsional vibration characteristics and suppression of electric vehicles with dual-motor drive system

The battery electric vehicles with dual-motor coupling drive system have serious torsional vibration problems when the driving torque changes rapidly. This paper targets at reducing the negative effect of the torsional vibration with an active control strategy. The torsional vibration model of the dual-motor coupling drive system is established through the lumped mass method. A four-inertia modeling method which can reflect the powertrain configuration characteristics is proposed, by simplifying the traditional lumped mass model, and the feasibility is verified based on the error comparing of the torsional vibration characteristics between the two models. Combined Kalman filter theory, PID control theory and fuzzy control theory, a real-time self-tuning active control strategy is proposed to compensate motor torque by considering vehicle acceleration as the control input. Finally, the four-inertia model with the control strategy is simulated under such conditions as the torque abrupt change and the C-WTVC driving cycle. The simulation results indicated that the proposed active control strategy performs well in the torsional vibration suppression and the control effect is robust to the different structural parameters.

Self-regulatory Fractional Fuzzy Control for Dynamic Systems: An Analytical Approach

This paper presents an analytical design of a fractional order fuzzy proportional integral plus derivative (FOFPI + D) controller. Artificial intelligence is incorporated into the controller with the help of a formula-based fuzzy logic system. The designed scheme combines fractional order fuzzy PI (FOFPI) and fractional order fuzzy D (FOFD) controller, derived from fundamental FOPID control law. The proposed scheme enjoys the linear structure of the FOPID controllers with non-linear gains that provide self-tuning control capability. The sufficient condition for stability of the closed-loop system is also established using the graphical approach. Performance of the proposed FOFPI + D, its integer order variant (FPI + D), and conventional controllers is examined for control of a highly non-linear and uncertain two-link robotic manipulator system. The optimum parameters of controllers are found by minimising aggregated control variation and error objective through non-dominated sorting genetic algorithm-II (NSGA-II). The comparison for trajectory tracking shows that FOFPI + D has the minimum integral absolute error (IAE) compared to other controllers. Further, rigorous performance investigations are performed to verify the robustness of designed controllers against parametric uncertainties, the varying boundary conditions of reference trajectory and disturbance rejection. It is concluded from the results that the proposed FOFPI + D controller exhibits superior performance.

Adaptive Neuro-Fuzzy Self Tuned-PID Controller for Stabilization of Core Power in a Pressurized Water Reactor

There has been a lot of interest in generating electricity using nuclear energy recently. This interest is due to the features of such a source of energy. The main part of the nuclear energy system is the reactor core, especially the most widely used Pressurized Water Reactor (PWR). This reactor is the hottest part of the nuclear system; security risks and economic possibilities must be considered. Controlling this reactor can increase the security and efficiency of nuclear power systems. This study presents a dynamic model of the (PWR), including the reactor's core, the plenums of the upper and lower, and the connecting piping between the reactor core and steam generator. In addition, an adaptive neuro-fuzzy (ANFIS) self-tuning PID Controller for the nuclear core reactor is presented. This adaptive controller is used to enhance the performance characteristics of PWR by supporting the profile of the reactor power, the coolant fuel, and hot leg temperatures. The suggested proposed ANFIS self-tuning controller is estimated through a comparison with the conventional PID, neural network, and fuzzy self-tuning controllers. The results showed that the proposed controller is best over traditional PID, neural network, and fuzzy self-tuning controllers. All simulations are throughout by using MATLAB/SIMULINK.

Research on DC Electronic Load System Based on PSO-PID Algorithm

At present, the control part of the DC electronic load is mainly based on analog control, that is, the control loop is controlled by an operational amplifier. Therefore, it is easy to cause slow response speed and overshoot in the process of current rise, and the improvement of response speed is of great significance to the test link. Therefore, this paper studies the digital control of DC electronic load. The DC electronic load control system has the characteristics of nonlinearity, and it is difficult to complete the theoretical modeling. Therefore, the MATLAB system identification toolbox is used to obtain the mathematical model. The traditional PID tuning method has a long time and poor effect. This paper adopts a parameter self-tuning PID controller based on particle swarm optimization algorithm. Through MATLAB simulation, the traditional PID control and particle swarm optimization PID control are simulated and compared. The results show that the digital controller has faster response speed and smaller overshoot than the conventional controller, and the system performance is significantly improved.

Self-Tuned Fuzzy PID Temperature Controller Implementation in Hot-Air Coffee Roasting Process

Self-Tuned Fuzzy PID Temperature Controller Implementation in Hot-Air Coffee Roasting Process

A Self-Tuning Adaptive Control Scheme for a Grid-Connected Three-Phase PV System

This article presents a new self-tuning sinusoidal recursive controller (STSRC) for handling the uncertainties and disturbances in a three-phase grid-connected photovoltaic system (GCPVS). The performance of a GCPVS is greatly influenced by the uncertainties, such as intermittency in solar irradiation, change in capacitance and equivalent series resistance (ESR) of dc-link capacitor, and variation of impedance between the inverter and the grid. This STSRC comprises improved linear sinusoidal tracer-based recursive least-square estimation (IRLS) to achieve robust performances, despite the parametric uncertainties through real-time estimation of its parameters, and updation of its gains. The efficacy of the proposed STSRC is verified through simulation using MATLAB/Simulink and experimental studies pursued on a 2.5-kW prototype. It is observed that STSRC exhibits superior tracking of PV voltage with reduced ripple and improved grid current with reduced total harmonic distortion (THD) of 1.66%. The estimation accuracy is found above 92%.

Design and Simulation of Satellite Attitude Control Algorithm Based on PID

According to the tasks of mapping and reconnaissance, the satellite needs to adjust its attitude and keep its attitude stable when it is in orbit. Firstly, the kinematic model and dynamic model of satellite attitude are established.Then the digital simulation of satellite attitude control system is built based on the mathematical model of satellite attitude, in which the control modules use PID controller and fuzzy PID controller respectively. Finally, the steady-state performance and dynamic performance of the two controllers are compared and analyzed.The experimental results show that both fuzzy PID control and fuzzy PID control can be used as effective means of attitude control.Both of them have high precision, fast speed and good steady-state performance to achieve the desired attitude. Fuzzy PID control also has the ability of self-tuning controller parameters, which makes the control process smoother.

Self-tuning proportional-derivative pitch control for internal fault-tolerance in wind turbine benchmark

ABSTRACT The wind turbine (WT) dynamics are affected by wind variations and system parameter changes as well. To deal with the pitch control problem a Self-Tuning Controller (STC) with Proportional-Derivative (PD) structure has been proposed for the benchmark model (BM). This BM also models some sensors, actuators and system faults. The STC design herein is pivoted on a second-order auto-regressive exogenous (ARX) model. At first, three identification methods for the ARX model are presented: Recursive Least Square method (RLS), RLS with exponential forgetting (RLS-EF) and RLS with adaptive directional forgetting (RLS-ADF). In comparison, the performance of RLS-ADF is better than the rest two. The STC PD controller supersedes contemporary adaptive control approaches to fix up the generated power to its rating for above-rated wind. Secondly, on analysis, the proposed STC PD controller is fault-tolerant for nine types of faults. In the third stage, in extensive Monte Carlo Simulation (MCS), the sensitivity test to system parameter variation is carried out including the sensor faults. The result in the presence of a fault, as well as parameter variation, shows minimal deviation from normal conditions indicating the suitability of the STC PD controller to keep the system intact except in case of multiple faults.

Adaptive Intelligent Model Predictive Control for Microgrid Load Frequency

In this paper, self-tuning model predictive control (MPC) based on a type-2 fuzzy system for microgrid frequency is presented. The type-2 fuzzy system calculates the parameters and coefficients of the control system online. In the microgrid examined, there are sources of photovoltaic power generation, wind, diesel, fuel cells (with a hydrogen electrolyzer), batteries and flywheels. In simulating the load changes, changes in the production capacity of solar and wind resources as well as changes (uncertainty) in all parameters of the microgrid are considered. The performances of three control systems including traditional MPC, self-tuning MPC based on a type-1 fuzzy system and self-tuning MPC based on a type-2 fuzzy system are compared. The results show that type-2 fuzzy MPC has the best performance, followed by type-1 fuzzy MPC, with a slight difference between the two results.