Control Performance Research Articles

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

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

Providing an Intelligent Frequency Control Method in a Microgrid Network in the Presence of Electric Vehicles

Due to the reduction in fossil fuel abundance and the harmful environmental effects of burning them, the renewable resource potentials of microgrid (MG) structures have become highly highly. However, the uncertainty and variability of MGs leads to system frequency deviations in islanded or stand-alone mode. Usually, battery energy storage systems (BESSs) reduce this frequency deviation, despite limitations such as reducing efficiency in the long term and increasing expenses. A suitable solution is to use electric vehicles (EVs) besides BESSs in systems with different energy sources in the microgrid structure. In this field, due to the fast charging and discharging of EVs and the fluctuating character of renewable energy sources, controllers based on the traditional model cannot ensure the stability of MGs. For this purpose, in this research, an ultra-local model (ULM) controller with an extended state observer (ESO) for load frequency control (LFC) of a multi-microgrid (MMG) has been systematically developed. Specifically, a compensating controller based on the single-input interval type fuzzy logic controller (FLC) was used to remove the ESO error and improve the LFC performance. Since the performance of the ULM controller based on SIT2-FLC depends on specific parameters, all of these coefficients were adjusted by an improved harmony search algorithm (IHSA). Simulation and statistical analysis results show that the proposed controller performs well in reducing the frequency fluctuations and power of the system load line and offers a higher level of resistance than conventional controllers in different MG scenarios.

Degradation Behaviors of Polylactic Acid, Polyglycolic Acid, and Their Copolymer Films in Simulated Marine Environments.

Poly(lactic acid) (PLA) and poly(glycolic acid) (PGA) are extensively studied biodegradable polymers. However, the degradation behavior of their copolymer, poly(lactic-co-glycolic acid) (PLGA), in marine environments has not yet been confirmed. In this study, the changes in macroscopic and microscopic morphology, thermal properties, aggregation, and chemical structure of PLA, PGA, PLGA-85, and PLGA-75 (with 85% and 75% LA content) in simulated marine environments were investigated. Results revealed that degradation occurred through hydrolysis of ester bonds, and the degradation rate of PGA was faster than that of PLA. The amorphous region degraded preferentially over the crystalline region, leading to cleavage-induced crystallization and decreased thermal stability of PLA, PLGA-85, and PLGA-75. The crystal structures of PLGAs were similar to those of PLA, and the higher GA content, the faster was the degradation rate. This study provides a deeper understanding of the seawater degradation behaviors of PLA, PGA, and their copolymers, and provides guidance for the preparation of materials with controllable degradation performance.

Performance analysis of a novel single-flywheel resettable-inertia damper with eddy-current damping

The clutching inertia model presents an ideal Bang-Bang control approach, but it is challenging to realize practically through passive means. In this paper, a single-flywheel resettable-inertia damper (SRID) is proposed on the basis of the clutching inertia model. This device is implemented with a rack-and-pinion system, a right-angle gearbox system, and a flywheel-mounted eddy-current damping system. Initially, a mechanical analysis model of this device is established according to its working mechanism. Subsequently, comparative analyses are performed on the performance of SRID in a single degree-of-freedom (SDOF) structure compared to viscous dampers (VDs) subjected to harmonic and seismic excitations. The results show that the SRID can achieve a comparable control effect as VDs with a much smaller need for the damping coefficient, reflecting a damping amplification effect over 100 times. The SRID is effective in controlling peak displacement and absolute acceleration responses, especially for long-period structures. Moreover, the control performance is significantly improved with increasing magnetic induction intensity.

Research on an Ice Tolerance Control Method for Large Aircraft Based on Adaptive Dynamic Inversion

Considering the effect of icing on aircraft control performance, this paper proposes an adaptive dynamic inverse ice tolerance control method based on piecewise constant. A control allocation algorithm is introduced to compensate for the change of control surface performance caused by icing. This method can achieve satisfactory disturbance estimation accuracy under a given sampling time, and thus ensure a closed-loop system error within an acceptable range. The proposed design method is applied to the design of a flight control law for a transport aircraft, aiming to solve the problem of ice-tolerant flight control, reduce the influence of icing conditions on controllability and safe flight of the transport aircraft, and thus improve the flight quality of the transport aircraft. The simulation results are verified under the influence of both standby ice type and failure ice type, and the interference effect on aircraft aerodynamic parameters is further added. The simulation results show that adaptive dynamic inverse control based on piecewise constant can overcome the influence caused by icing and aerodynamic parameter interference, achieve accurate tracking of command, and provide excellent fault tolerance and robustness, which ensures that the transport aircraft can achieve the desired control performance and safe flight capability.

Model predictive control of a wave-to-wire wave energy converter system with non-linear dynamics and non-linear constraints using a tailored pseudo-spectral method

A novel pseudo-spectral method is proposed to tackle the non-linear control problem of a wave energy converter (WEC) integrated with an all-electrical power-take-off (PTO). The WEC hydrodynamic model is constructed using linear wave theory together with a non-linear viscous damping term. The PTO model encompasses a permanent magnet synchronous generator (PMSG) whose local field weakening mechanism imposes non-linear operational constraints on the WEC’s motion and PTO torque. The energy-maximizing control problem of this wave-to-wire model is non-linear and has not been investigated before. In comparison to pseudo-spectral methods previously studied, the proposed pseudo-spectral method is exempt from the assumption of ideal incoming wave prediction. This is made possible by the introduction of a novel mapping function to convert the finite control horizon onto the Gaussian quadrature interval. This mapping function increases more slowly with time at the beginning of the control horizon where wave prediction attains greater precision. As a result, the collocation points are utilized to the advantage of the better approximation of the objective function, thus better overall control performance.

A hybrid SCSO-QNN approach based load frequency control of three area power system with renewable sources using FOPID controller

ABSTRACT This research proposes a hybrid SCSO-QNN method for designing a FOPID controller in a three-area power system to regulate load frequency. The proposed hybrid approach combines quantum neural networks with sand cat swarm optimization, and it is commonly named as the SCSO-QNN method. The proposed method’s primary goal is to overcome load disturbances and attain the intended output of the interconnected system. The SCSO generates controller parameters tuned by the FOPID controller, providing a significant improvement in performance, while the QNN is employed to forecast the optimal control signal of the converter. Reducing steady-state errors and improving system stability are areas where the FOPID performs better than a traditional integer-order PID controller. Moreover, SCSO-QNN optimization technique exhibits excellent convergence properties, resulting in improved control performance. The proposed method ensures system frequency control by controlling frequency deviation and power fluctuations in the connect line during load disturbances. The proposed method is executed on the MATLAB platform and contrasted with current techniques. The proposed technique outperforms all current techniques, including ABC-FOPID, Genetic Algorithm, and Artificial Bee Colony PID. The proposed method Area control error is 1.8%, which is less than other existing methods.

Rectus sheath block added to parasternal block may improve postoperative pain control and respiratory performance after cardiac surgery: a superiority single-blinded randomized controlled clinical trial

BackgroundThe population undergoing cardiac surgery confronts challenges from uncontrolled post-sternotomy pain, with possible adverse effects on outcome. While the parasternal block can improve analgesia, its coverage may be insufficient to...

An Output Cooperative Controller for a Hydraulic Support Multi-Cylinder System Based on Neural Network Compensation

The straightness control of a fully mechanized working face is the key technology used in intelligent coal mining, so the position control of a hydraulic support multi-cylinder moving system is of great significance. However, due to the harsh environment of coal mines, complex friction, external disturbances, and the coupling relationship between adjacent cylinders, the accuracy of position control is restricted. Therefore, an output cooperative controller is proposed in this paper for a multi-cylinder system. A high-order sliding mode observer is utilized to estimate the system states with the only available output position signal. A neural network disturbance observer is applied to estimate the lump disturbance of the strict-feedback model, including the system uncertainty, disturbance force and the coupling force between adjacent cylinders. Then, continuous motion position tracking simulation is conducted and the estimation performance of the state observer and neural network is analyzed. Furthermore, a multi-cylinder collaborative control test rig is designed, and experiments based on the actual actions of the hydraulic support are conducted. The results show that the proposed output cooperative controller has an excellent position control performance compared with the traditional proportional–integral controller.

Cholinergic Neuromodulation of Prefrontal Attractor Dynamics Controls Performance in Spatial Working Memory.

The behavioral and neural effects of the endogenous release of acetylcholine following stimulation of the nucleus basalis (NB) of Meynert have been recently examined in two male monkeys (Qi et al., 2021). Counterintuitively, NB stimulation enhanced behavioral performance while broadening neural tuning in the prefrontal cortex (PFC). The mechanism by which a weaker mnemonic neural code could lead to better performance remains unclear. Here, we show that increased neural excitability in a simple continuous bump attractor model can induce broader neural tuning and decrease bump diffusion, provided neural rates are saturated. Increased memory precision in the model overrides memory accuracy, improving overall task performance. Moreover, we show that bump attractor dynamics can account for the nonuniform impact of neuromodulation on distractibility, depending on distractor distance from the target. Finally, we delve into the conditions under which bump attractor tuning and diffusion balance in biologically plausible heterogeneous network models. In these discrete bump attractor networks, we show that reducing spatial correlations or enhancing excitatory transmission can improve memory precision. Altogether, we provide a mechanistic understanding of how cholinergic neuromodulation controls spatial working memory through perturbed attractor dynamics in the PFC.

Stability evaluation and design of negative stiffness amplifying damper-incorporated nonlinear structures

Negative stiffness-incorporated dampers have been widely accepted as effective vibration mitigation systems for flexible structural stiffness adjustment and enhanced energy dissipation. However, current investigations and optimal designs of negative stiffness-incorporated structures are limited to the linear elastic assumption of primary structures, potentially causing stability issues and unsatisfactory control performance. In this case, this study derives the stability criterion for negative stiffness amplifying damper (NSAD)-incorporated nonlinear structures and establishes stability-oriented design as well as modification formulae. The theoretical foundation of NSADs and nonlinear primary structures are initially introduced, based on which the stability criterion is derived according to the Routh–Hurwitz stability criterion. Following the existing design of linear elastic assumption, an endurance time acceleration series is generated subsequently to examine the proposed stability criterion for NSAD-equipped nonlinear structures. Consequently, the stability-oriented design method and corresponding modification formulae are proposed for near-field and far-field earthquakes, of which the effectiveness is validated through design cases. The results underscore the importance of considering structural nonlinearity for ensuring stability and designing NSAD-equipped nonlinear structures, demonstrating the feasibility of using the proposed correction factors to guarantee the stability of NSAD-equipped nonlinear structures subjected to multi-type earthquakes with various intensities. Additionally, the established design framework successively provides a high-efficiency energy dissipation device for seismic control of nonlinear structures, simultaneously generating easy-to-use design formulae for NSADs by combining the linear structure-based design and modification factors, facilitating direct design of nonlinear structures.

Application of modified PVC membranes with GO-ZnO nanoparticles in MBR: sludge characteristics, fouling control and removal performance

BackgroundMembrane fouling is still one crucial impediment in the application of membrane bioreactors (MBR). MethodsAntifouling properties of GO and GO-ZnO embedded PVC membranes in MBR were investigated. ZnO nanoparticles were decorated onto GO nanosheets and then membranes were made by NIPS method. Significant findingsThe results revealed that the contact angle decreased from 80° for pure PVC to 67° for 0.1 wt% GO-ZnO embedded membrane. Pure water flux increased from 33 L/m2h for neat membrane to 128 L/m2h for 0.075 wt% PVC/GO-ZnO membrane. The performance of membranes was evaluated during 35 days MBR filtration system using several analyses including COD, turbidity, SMP, EPS, PSD, and EEM. Results indicated that the presence of GO-ZnO nanoparticles in the membranes has a positive effect on COD and turbidity removal due to the oxygen-containing groups attracting SMP through intermolecular force interaction. In addition, the inclusion of nanoparticles in the membranes leads to a reduction in EPS concentration. Research on PVC membranes with graphene oxide and zinc oxide nanoparticles in membrane bioreactor processes is crucial for advancing water treatment technology. These nanomaterials promise enhanced membrane performance, longevity, and efficiency in treating wastewater, leading to more sustainable and effective water treatment solutions.

Optimal weighting factor design based on entropy technique in finite control set model predictive torque control for electric drive applications

In the conventional finite control set model predictive torque control, the cost function consists of different control objectives with varying units of measurements. Due to presence of diverse variables in cost function, weighting factors are used to set the relative importance of these objectives. However, selection of these weighting factors in predictive control of electric drives and power converters still remains an open research challenge. Improper selection of weighting factors can lead to deterioration of the controller performance. This work proposes a novel weighting factor tuning method based on the Multi-Criteria-Decision-Making (MCDM) technique called the Entropy method. This technique has several advantages for multi-objective problem optimization. It provides a quantitive approach and incorporates uncertainties and adaptability to assess the relative importance of different criteria or objectives. This technique performs the online tuning of the weighting factor by forming a data set of the control objectives, i.e., electromagnetic torque and stator flux magnitude. After obtaining the error set of control variables, the objective matrix is normalized, and the entropy technique is applied to design the corresponding weights. An experimental setup based on the dSpace dS1104 controller is used to validate the effectiveness of the proposed method for a two-level, three-phase voltage source inverter (2L-3P) fed induction motor drive. The dynamic response of the proposed technique is compared with the previously proposed MCDM-based weighting factor tuning technique and conventional MPTC. The results reveal that the proposed method provides an improved dynamic response of the drive under changing operating conditions with a reduction of 28% in computational burden and 38% in total harmonic distortion, respectively.

Reinforcement-Learning-Based Visual Servoing of Underwater Vehicle Dual-Manipulator System

As a substitute for human arms, underwater vehicle dual-manipulator systems (UVDMSs) have attracted the interest of global researchers. Visual servoing is an important tool for the positioning and tracking control of UVDMSs. In this paper, a reinforcement-learning-based adaptive control strategy for the UVDMS visual servo, considering the model uncertainties, is proposed. Initially, the kinematic control is designed by developing a hybrid visual servo approach using the information from multi-cameras. The command velocity of the whole system is produced through a task priority method. Then, the reinforcement-learning-based velocity tracking control is developed with a dynamic inversion approach. The hybrid visual servoing uses sensors equipped with UVDMSs while requiring fewer image features. Model uncertainties of the coupled nonlinear system are compensated by the actor–critic neural network for better control performances. Moreover, the stability analysis using the Lyapunov theory proves that the system error is ultimately uniformly bounded (UUB). At last, the simulation shows that the proposed control strategy performs well in the task of dynamical positioning.

Active suspension hierarchical control with parameter uncertainty and external disturbance of electro-hydraulic actuators

In order to improve the ride comfort and handling stability of the electro-hydraulic active suspension system, a hierarchical control strategy is proposed. For the active suspension system with body mass uncertainty and safety constraints, an enhanced constraint adaptive backstepping controller is designed to generate the target force of body vertical motion. When dealing with constraints, nonlinear filters are introduced, backstepping technology and quadratic Lyapunov function are used to combine the control target and domain constraints, and the vertical displacement of the body and the suspension deflection control index are synthesized into a single controlled variable, so that the control variable converging to zero meets the suspension dynamic displacement limit. The ride comfort and safety of the active suspension system are improved. Secondly, stability analysis is conducted on the zero dynamic error system to ensure that all safety performance indicators are bounded. The effects of external disturbances, parameter uncertainties and modelling errors on the force tracking accuracy of asymmetric cylinder electrohydraulic systems are addressed. A backstepping dynamic surface control method based on a nonlinear perturbation observer is proposed, which estimates the composite perturbation terms such as modelling error and external perturbation, and uses the estimated value of the nonlinear observer to design a feedforward compensating control term to offset the effect of the composite perturbation on the system performance. In addition, the dynamic surface control method is used to calculate the derivative of the target force input, which avoids the derivative explosion phenomenon and reduces the computational complexity of the system. Simulation test results show that this method reduces the computational complexity of the system and improves the control performance. And under random and bumpy road conditions, the root-mean-square value of sprung mass acceleration performance index is reduced by 48.354 % and 72.677 % compared with passive suspension, which improves the ride comfort of the vehicle.

Preparation of Thermal Controlled Deoxygenation Inhibitor and Its Inhibition of Coal Spontaneous Combustion

ABSTRACT The purpose of this study was to prepare a new type of thermal controlled deoxygenation inhibitor successfully. By adding a particular inorganic salt hydrate, this new deoxidizing inhibitor effectively slows down the oxidative-exothermic reaction that may occur during the action of the original deoxidizing inhibitor, improving the efficiency of preventing coal from spontaneous combustion. The heat shielding approach was employed, using the inorganic salt hydrate’s high heat capacity to absorb and decelerate heat transport during the phase change process. To assess the heat shielding effect of inorganic salt hydrates like Na2CO3·10 H2O, MgSO4·7 H2O, Na2SO4·10 H2O, MgCl2·6 H2O, and CaCl2·6 H2O, we conducted tests using thermogravimetric analysis (TG) and differential scanning calorimetry (DSC). With a heat shielding effect of 171.2 J/g, which is much superior to that of other hydrates, the testing results demonstrate that Na2CO3·10 H2O has the best thermal control performance. In addition, we verified the effective inhibition of coal spontaneous combustion by the thermal controlled deoxygenation inhibitor, especially under the condition of increasing temperature, and the blocking effect of the inhibitor was enhanced with the increase of temperature. The findings of this study provide a new strategy for spontaneous combustion prevention during coal mining and storage and provide a scientific basis for further industrial application and optimization.

Robust security‐based model predictive control for nonlinear cyber‐physical system under random occurring deception attacks and actuator saturation

AbstractThis article presents a robust security‐based model predictive control (MPC) framework for a class of discrete‐time nonlinear cyber‐physical systems (CPSs) suffering random deception attacks and persistent actuator saturation. In CPSs, the attacks can significantly degrade the control performance. Naturally, a larger degree of control is required, prompting the consideration of actuator saturation in CPS's structure. In our work, the cyberattacks on the controller‐actuator (C‐A) channel are described as a stochastic process with specific probability and bounded energy. The security‐related constraint is introduced to improve the resistance and robustness against attacks. Mean‐square quadratic boundedness is derived to estimate the robustly invariant set. By placing the saturated input into a convex hull, a robust security‐based MPC optimization problem is addressed less conservatively with linear matrix inequality technique. Furthermore, the feasibility of the proposed method is proved recursively, and the stability and security of the closed‐loop system are established in mean‐square sense. In the end, the laboratory tank and reactor‐separator process are applied to validate the effectiveness of the proposed algorithm.

Online Adaptive Neural Observer for Prescribed Performance Hyper-Chaotic Systems

This paper proposes a new neural-based algorithm applied to online identifying, observing, and adaptively controlling of uncertain nonlinear systems via which the system transient and residual values independently regulated. The Lyapunov stability concept is used for enhancing the control performance. Especially, distinguished from recent related studies, not only identified model but the learning method and control rules are also innovatively implemented through which it independently separates the transient-time regulation from residual error adjustment.Sequentially, an advanced adaptive neural controller for an uncertain nonlinear plant with state feedback is implemented using the open-loop estimation result. The key contribution of the online estimator will be verified in this proposed control algorithm. Eventually, as to verify the prescribed performance and the flexibility of the suggested control approach, the estimation and control of an uncertain hyper-chaotic weld plant will be tested.

Combining finite-time control and prescribed tracking performance for uncertain PMSM driven steer-by-wire system with unknown disturbance

To solve the uncertainties in permanent magnet synchronous motor (PMSM) driven steer-by-wire (SbW) system, a prescribed-performance-control (PPC) based finite-time fuzzy controller is proposed. Specifically, the dynamics of SbW system driven by PMSM is analyzed, and combine PPC technology to transform the tracking error coordinates. Then, the velocity signal is obtained from the sigmoid function-based tracking differentiator by using the position signal. To solve the problem of mismatched disturbance, fuzzy logic system (FLS) are used as a universal approximator to estimate the lumped nonlinear friction and other unknown functions, the boundary of lumped disturbance is estimated by the designed adaptive updating law. By fusing the designed virtual controller and the intermediate control law, the controller needs no precise parameters of system, under which the effect of uncertainty of control gain can be compensated completely. Finally, rigorous theoretical analysis based on the Lyapunov stability theory is provided to demonstrate the finite-time stability of the closed-loop system under consideration. Simulation and experiment results are presented to show the effectiveness of the developed control approach, and some comparisons are given to show the rapid and accurate position tracking control performance.

Optimized FOPID controller for nuclear research reactor using enhanced planet optimization algorithm

Nuclear reactor control is pivotal for the safe and efficient operation of nuclear power plants, facilitating the regulation of reactor reactivity. This study introduces an optimized fractional-order proportional-integral-derivative (FOPID) controller tailored for maintaining reactivity levels in nuclear power plants, particularly during load-following operations. The controller adjusts the position of control rod to regulate power output effectively. We enhance FOPID controller's performance using a modification of Planet Optimization Algorithm (POA-M), leveraging the strengths of the Arithmetic Optimization Algorithm (AOA) to improve its exploitation capabilities. We evaluate the efficacy of POA-M-FOPID controller against traditional techniques, including POA, AOA, and Particle Swarm Optimization (PSO). We assess its performance using the Egyptian Testing Research Reactor (ETRR-2) as a case study. Our results demonstrate that the POA-M-FOPID controller outperforms alternative algorithms across various control metrics, exhibiting superior resilience and efficiency. Notably, the utilization of the POA-M-FOPID controller yields remarkable improvements in reactor power performance, achieving significantly reduced settling time (25.27 sec) and maximum overshoot (0.67 %) compared to conventional FOPID controllers incorporating POA, AOA, and PSO methods. These findings underscore the effectiveness of POA-M-FOPID in enhancing nuclear reactor control systems, offering potential benefits for broader nuclear power industry in terms of safety, stability, and operational efficiency.