Control Performance Assessment Research Articles

Trim Tab Flight Stabilisation System Performance Assessment under Degraded Actuator Speeds

One of the areas involved in changing current aircraft into more electric ones is decreasing energy consumption by the aircraft’s automatic flight control. Therefore, some aircraft types have tested the possibility of controlling the flight in automatic mode or stabilising the flight with trimmers. Previous research on cost-effective and less electrical-energy-consuming automatic stabilisation systems for an aircraft resulted in constructing a laboratory model of the system. Such a feature is beneficial for initiatives like Future Sky, electric aircraft and aircraft stabilisation system retrofits. The system was developed using model-based design and next tuned and tested in model, pilot and hardware-in-the-loop simulations. The implementation of this system does not modify the pilot’s primary manual controls. Instead, the electrical trim system is used for automatic stabilisation or manual trimming, depending on the chosen operation mode. The paper presents the development process of the laboratory model of the system and its simulation under degraded actuator speeds. The results were the basis for its control performance assessment. First, the control performance measure was defined. Then the simulation scenarios that compare system behaviour in stabilisation mode after aerodynamic disturbance with three different trim tab actuator speeds were described. The performance measure is highly degraded by the slower actuator speeds, although altitude and heading are finally stabilised in all cases. Moreover, the performance of stabilisation in a lateral channel is less affected by the slowest actuator than in a longitudinal channel.

Modelling, design and simulation of plasma magnetic control for the Spherical Tokamak for Energy Production (STEP)

Magnetic controller performance requirements and design solutions for the Spherical Tokamak for Energy Production (STEP) are driven by the need to produce equilibrium and scenario trajectories, maintaining steady plasma vertical stabilization and shape control for a period of thousands of seconds or longer while avoiding contact with the plasma-facing components (PFCs). Axisymmetric magnetic control schemes including vertical stabilization, plasma current control, plasma shape control and poloidal field coil current control for the STEP Prototype Reactor (SPR) concept are being developed using a suite of control analysis tools (known collectively as TokSys) supporting the integrated plasma control design process. The vertical growth rate based on a linear rigid plasma response model in TokSys is used for assessing the controllability of the vertical instability in SPR. TokSys closed-loop simulations with the axisymmetric non-linear, free-boundary evolution code, GSevolve, are performed for the assessment, identification, and verification of algorithm implementation and controller performance for the various axisymmetric control systems. Dynamic control performance and controllability are shown to be consistent with noise-affected scenario requirements under reasonable power supply and sensor performance assumptions.

Optimal performance assessment of intelligent controllers used in solar-powered electric vehicle

Introduction. Increasing vehicle numbers, coupled with their increased consumption of fossil fuels, have drawn great concern about their detrimental environmental impacts. Alternative energy sources have been the subject of extensive research and development. Due to its high energy density, zero emissions, and use of sustainable fuels, the battery is widely considered one of the most promising solutions for automobile applications. A major obstacle to its commercialization is the battery's high cost and low power density. Purpose. Implementing a control system is the primary objective of this work, which is employed to change the energy sources in hybrid energy storage system about the load applied to the drive. Novelty. To meet the control objective, a speed condition-based controller is designed by considering four separate math functions and is programmed based on different speed ranges. On the other hand, the conventional/intelligent controller is also considered to develop the switching signals related to the DC-DC converter’s output and applied the actual value. Methods. According to the proposed control strategy, the adopted speed condition based controller is a combined conventional/intelligent controller to meet the control object. Practical value. In this work, three different hybrid controllers adopted speed condition based controller with artificial neural network controller, adopted speed condition based controller with fuzzy logic controller, and adopted speed condition based controller with proportional-integral derivative controller are designed and applied separately and obtain the results at different load conditions in MATLAB/Simulink environment. Three hybrid controller’s execution is assessed based on time-domain specifications.

Stealthy Cyberattacks Detection Based on Control Performance Assessment Methods for the Air Conditioning Industrial Installation

This paper aims to study the workflow of the detection centre of stealthy attacks on industrial installations that generate an increase in energy consumption. Such long-lasting, undetected attacks on industrial facilities make production more expensive and less competitive or damage the installation in the long term. We present the concept of the remote detection system of cyberattacks directed at maliciously changing the controlled variable in an industrial process air conditioning system. The monitored signals are gathered at the PLC-controlled installation and sent to the remote detection system, where the discrepancies of signals are analysed based on the Control Performance Assessment indices. The results of performed tests prove the legitimacy of the adopted approach.

Prescribed-time Closed-loop Performance Assessment: Comparative Case Study

Control loop performance assessment is addressed in the context of the output feedback stabilization in fixed-time. Existing fixed- and prescribed-time output feedback control algorithms are reviewed and their pros and cons are discussed for performing the afore-mentioned task. The control performance assessment is based on integral indices. These indices are computed from the available data obtained from numerical simulations with a simple pendulum system affected by matched disturbances and measurement noise. Simulations results corroborates the pros and cons of the fixed-time output feedback algorithms in question.

Performance assessment of speed controllers for Five-Phase PMSG with Integrated P&O MPPT algorithms Based Wind Energy Conversion Systems

Performance assessment of speed controllers for Five-Phase PMSG with Integrated P&O MPPT algorithms Based Wind Energy Conversion Systems

Control performance assessment of cable-MR damper system based on pole assignment theory

Semi-active MR dampers have been proposed for cable vibration mitigation of long stay cables, relative theoretical and experimental studies have been carried out in the literature, and have been installed in some of the long span cable-stayed bridges for engineering practice. However, the actual efficiency of the semi-active control of cable-MR damper system has yet to be verified, which is important in real engineering practice. In this paper, the pole assignment theory is used and the corresponding numerical studies is carried out to verify the efficiency of the proposed semi-active control system. The simulation results show that MR damper using semi-active control is more efficient than using passive control and the semi-active control of cable-MR damper system could achieve the optimal control effect and better control efficiency of the system. This paper investigates the efficiency and effectiveness as well as the applicability of the proposed optimal control algorithm using MR damper for mitigating cable vibrations, which is important in real engineering practice.

Performance assessment and validation of inverter control current controllers in reduced sensor maximum power point tracking based photovoltaic‐grid tied system

A relative assessment on conventional and adaptive current controllers used in reduced sensor-maximum power point tracking (MPPT) based photovoltaic (PV)-grid tied inverter systems for the improvement of system power quality is suggested. The steady-state and transients errors produced in the conventional PI and proportional resonant controllers, which are used to generate the references, can be fixed by using an intelligent ADALINE-LMS adaptive controller; moreover, it helps in reducing the %THD (total harmonic distortion) level measured at different power zones. Also, to track the maximum PV power, which is further integrated to DC-bus, a reduced sensor-based technology is added into the circuit that sidesteps the problem of tracking local MPP instead of global MPP and the drawbacks of using current sensors. The use of a reduced sensor-based MPPT controller confirms extraction of maximum PV power and it guarantees a constant DC-link voltage under all the possible test conditions. The overall control architectures and system performances, which are tested under different system dynamics, are validated through MATLAB/Simulink as well as experimental findings obtained using the dSPACE RTI 1202 interfacing kit. These experimental results confirm that the adaptive control technique used in reduced sensor-MPPT based PV-grid tied inverter systems performs unbeatably with balanced load and grid voltages, less harmonics, quick response time etc. under the operation of linear, non-linear and transient loads, whereas, conventional controllers are best only for the linear loads.

Performance Assessment of Smart Dynamic Controller for Variable CCT White LED Light Source - A SVM Based Approach

Performance Assessment of Smart Dynamic Controller for Variable CCT White LED Light Source - A SVM Based Approach

Closed-loop identification of plant and disturbance models based on data-driven generalized minimum variance regulatory control

The paper addresses a closed-loop identification method based on generalized minimum variance (GMV) evaluation. Since the proposed method uses routine operation data, it requires no extra experiment with an external excitation signal. The model parameters of the plant and the disturbance are obtained simultaneously using a single set of input–output data generated by stochastic disturbance. A new variance criterion for closed-loop identification is derived through the conversion of the GMV evaluation function that has originally been developed for data-driven regulatory control. In the conversion, the feedback invariant polynomial, which is estimated from time series analysis of the closed-output signal, plays a key role. The features of the proposed approach lead to bridge closed-loop identification with control performance assessment as well as data-driven controller parameters tuning. The paper proves that the optimization of the proposed criterion results in the unique optimal solution, which corresponds to the true plant and disturbance model parameters. In numerical examples, the proposed method is applied to datasets obtained from a continuous stirred tank reactor (CSTR), which is operated around an unstable steady state. The result illustrates the effectiveness of the proposed closed-loop identification method.

Mobility of organotin pesticides: azocyclotin and cyhexatin in clayey and sandy soils from the Northern Paraná state—Brazil

Azocyclotin and cyhexatin are pesticides commonly used in mite control. However, these organotin compounds (OTC) are highly harmful to the aquatic ecosystem and supposedly mobile in the soil. In addition to not existing defined rules of use, few studies have been carried out on organotins’ behavior and environmental control. Liquid chromatography has been pointed out for the OTC quantitation because of gas chromatography’s thermal stability and derivatization limitations. Hence, a new high-performance liquid chromatography method with photodiode array detection was developed for quality assurance and quality control and environmental performance assessment purposes. Hysteresis index (HI) and mobilization factor were determined from sorption/desorption in sandy and clayey soils to assess mobility and environmental risk. Mobilization was observed for the two compounds by applying the dual-mode Freundlich–Langmuir model to the isotherms. Azocyclotin showed greater mobility, 23% and 19%, and HI of − 0.15 and 7.8 \(\times \) 10\(^{-4}\) for clayey and sandy soil samples, respectively. Although cyhexatin was practically immobile for both soil samples, it can be mobilized as an azocyclotin metabolite, increasing the environmental impact and risk for agricultural uses.

Practical control performance assessment method using Hurst exponents and crossover phenomena

This paper proposes a new Hurst exponent-based method to evaluate the performance of control loops by estimating Minimum Variance Index (MVI). Incorrect estimation of time delays dramatically affects MVI-based performance assessment accuracy. The main advantage of the proposed method over existing methods is that it only requires routine operation data, without any need for prior information of the system model parameters and time delays. Hence, this method is more suitable to be applied on various industrial control processes. Recently, crossover phenomena have been reported in the graphs used to estimate the Hurst exponent of different control signals, which complicate deciding the Hurst exponents values. To address this problem, the crossover phenomenon is investigated for control signals, then the Hurst index is modified based on it. The proposed method is successfully used to assess performance of various types of control loops such as the cascade loop and verified through simulation results.

Performance assessment for non-Gaussian systems by minimum entropy control and dynamic data reconciliation

Control performance of the industrial process is inevitably influenced by the measurement noises and non-Gaussian external disturbances. This influence has not been fully considered in the traditional variance-based controller design. To reduce the influence, a novel scheme that can enhance the control performance is developed by integrating dynamic data reconciliation (DDR) into minimum rational entropy control (MREC) in this paper. Firstly, the influence of measurement noise is fully considered, and a novel DDR method is proposed to deal with the minimum entropy control (MEC) process such that the influence of measurement noise can be reduced, and the control performance will be improved. Then, based on the DDR-MREC performance index, a benchmark for evaluating the control performance of non-Gaussian systems is established. Finally, the proposed control performance assessment (CPA) method is applied to the wind energy conversion system and compared with the CPA method based on DDR-minimum variance control. The experimental results have demonstrated that the proposed new method is more effective than existing works.

Control Performance Assessment of Fractional-Order PID Controllers Applied to Tracking Trajectory Control of Robotic Systems

Performing tracking tasks in robotic manipulators presents many challenges for the controller design, especially in presence of external disturbances that affects the dynamical behavior of the robotic system. This paper presents the design of a Fractional-Order PID controller with the computed torque control strategy for the tracking control of a two degree of freedom robotic manipulator. The proposed technique is contrasted against the classical PID controller with the computed torque control strategy. To validate the proposed controllers, the robotic system is simulated using an MSC-ADAMS/MATLAB co-simulation model, which is employed for identification and control tasks. The proposed model is tested in presence of external disturbances in the applied torque, random noise in the feedback loop and payload variations. Obtained results show that the Fractional-Order PID controller with the computed torque control strategy has a better performance in presence of the analyzed external disturbances for tracking tasks.

Tuning of extended state observer with neural network-based control performance assessment

The extended state observer (ESO) is an inherent element of robust observer-based control systems that allows one to estimate the impact of disturbance on system dynamics. Proper tuning of ESO parameters is necessary to ensure a good quality of estimated quantities and impacts the overall performance of the robust control structure. In this paper, we propose a neural network (NN) based tuning procedure that allows the prioritization between selected quality criteria such as the control and observation errors and the specified features of the control signal. The designed NN provides an accurate assessment of the control system performance and returns a set of ESO parameters that delivers a near-optimal solution in terms of the user-defined cost function. The proposed tuning procedure, using an estimated state from the single closed-loop experiment, produces near-optimal ESO gains within seconds.

Reinforcing Hurst Exponent with Oscillation Detection for Control Performance Analysis: An Industrial Application

Control performance assessment is one of the most important components of control assets monitoring. In the past decades, many different data-based approaches have been presented to assess control performance. Due to the high number of control loops for production industries, the methods requiring less parameters and less priori information became popular in industrial use. Detrended Fluctuation Analysis (DFA) is one of these methods, requiring only closed loop data and a pair of segment length to estimate Hurst exponent based control performance. However, the selection of segment lengths plays an important role in estimation, especially for oscillating control loops. In this paper, we have proposed an additional step (i.e. a step zero) to DFA to estimate control performance. This step uses an autocorrelation based and robust oscillation detection and characterization method to identify the oscillation period. Then, the calculated period is used to select the minimum segment length which leads more robust and precise estimation of control performance based on DFA. The proposed method has been applied and tested on several industrial control loops. The comparisons of the results between two approaches are given.

Control Strategy and Performance Assessment for Mixed-Autonomy Traffic at Signalized Intersection Networks: A Drl Approach

Control Strategy and Performance Assessment for Mixed-Autonomy Traffic at Signalized Intersection Networks: A Drl Approach

SIMULATING FLOOD RECOVERY MANOEUVRES USING A FREE-RUNNING SUBMARINE MODEL

Managing submarine safety, effectively, requires an understanding of many areas of platform performance, including its ability to manoeuvre. QinetiQ’s free-running submarine model (FRM) capability, the second generation Submarine Research Model (SRMII), forms a key part of the UK’s predictive manoeuvring capability that supports the MoD’s ability to conduct hydrodynamic assessment of the manoeuvring and control performance of the Royal Navy’s current and future submarines. Uniquely for an FRM, the SRMII has a large and capable ballast system. This is able to emulate a flooding incident within a submarine compartment and the subsequent emergency recovery procedures, which may include blowing the submarine’s main ballast tanks. This paper discusses how the SRMII’s ballast system was used to generate model-scale trajectories, which are not obtainable with many other FRMs. The experimental data were used to successfully validate the mathematical model, which predicts the maximum pitch angle response of a full-scale submarine to a compartment flood, to within an average accuracy of 1% at model-scale. However, the range of the non- dimensional flow angles the FRM exhibited was shown to be within that for a full-scale flood trajectory. Therefore, further tests have been proposed to increase the extent of the data in the future.

Multiobjective Tuning and Performance Assessment of PID Using Teaching-Learning-Based Optimization.

There have been many studies on the optimal tuning and control performance assessment (CPA) of the PID controller. In the optimal tuning, the trade-off between the setpoint tracking and the disturbance rejection performance is a challenge. Minimum output variance (MOV) is very widely used as a benchmark for CPA of PID, but it is difficult to be observed due to the non-convex optimization problem. In this paper, a new multiobjective function, considering both the OV in the CPA problem and integral of absolute error, is proposed to tune PID for this trade-off. The CPA-related non-convex problem and tuning-related multiobjective problem are solved by teaching–learning-based optimization, which guarantees a tighter lower bound for MOV due to the excellent capability of local optima avoidance and has higher computational efficiency due to the low complexity. The numerical examples of CPA problems show that the algorithm can generate better MOV than existing methods with less calculation time. The relationship between the weight of the multiobjective function and the performance, including setpoint tracking, stochastic and step disturbance rejection, is revealed by simulation results of the tuning method applied to two temperature control systems. The proper adjustment of the weight with a multistage strategy can achieve the trade-off to obtain excellent setpoint tracking performance in the initial stage and satisfying disturbance rejection performance in the steady stage.

Study on outlier robustness of minimum variance control performance assessment

SummaryMinimum variance (MinVar) method for control performance assessment constitutes one of the most common approaches to the control quality estimation. There are dozens of versions, enriched with numerous reported industrial implementations. MinVar methodology uses the idea of minimum variance, which has been introduced by Kalman. Therefore, it should be remembered that MinVar concept relies on the same assumptions as an idea of the minimum variance control. Among other assumptions, it is essential that the modeled disturbance is an independent random sequence. This article addresses scenarios, when loop noise exhibits non‐Gaussian properties and is characterized by outliers having fat‐tailed distribution. Sensitivity analysis of minimum variance method against such disturbances is evaluated using commonly used PID SISO control benchmarks. It is shown that MinVar method may be significantly biased in such non‐Gaussian situations, which are very frequent in the industrial reality. Reasons of the biased performance are traced and respective solutions are proposed.