Optimal Values Of Control Parameters Research Articles

A novel adaptive trajectory tracking control for complex environments based on accelerated back-propagation neural network

This work addresses the issue of trajectory tracking accuracy degradation for vehicle dynamics parameters and road conditions changing, an adaptive trajectory tracking controller is designed for the intelligent vehicle. Based on the yaw dynamics model, a model predictive controller (MPC) is developed for the trajectory tracking process. Meanwhile, the impacts of different control parameters on trajectory tracking at various speeds are compared. And the change rule between optimal control parameter values and reference speeds is determined by cubic polynomial fitting to ensure minimal error during the trajectory following process. Then, an estimator for vehicle tire cornering stiffness and road adhesion coefficient is designed and tested based on the accelerated back-propagation neural network (ABPNN) model, with its estimation values being compared to those obtained through recursive least square method. Last, a co-simulation platform combining MATLAB/Simulink and CarSim software is established to conduct the trajectory tracking experiment under variable working conditions. Experimental results show that the proposed adaptive controller not only exhibits high accuracy in trajectory tracking but also demonstrates excellent stability and adaptability under medium to low speed conditions, particularly when encountering changing road conditions.

Optimization and development of a new rotary magnetic refrigerator

AbstractMagnetic refrigeration is becoming increasingly relevant as a means to achieve sustainability in refrigeration, thanks to its durability, efficiency, and low environmental impact. This paper regards the design of a new rotary magnetic refrigerator feasible for domestic use at room temperature. An interdisciplinary approach was necessary to perform the design, construction, and optimization of the device through mechanical design, thermo-hydraulic design, and integration into a proper test rig. Optimization of critical parts, such as the regenerator, the magnet structure, and the external heat exchangers, was performed using numerical models. The optimal values of control parameters were determined using a parametric sweep study. The results of all these steps of the study resulted in significant progress in designing and constructing an efficient rotary magnetic refrigerator. This work presents the insights obtained from the project implementation, including the design and construction aspects, presented in a practical way that can guide future projects in the field of magnetic refrigeration. Graphical abstract

Иерархическая модель оптимизации технологических параметров комплекса рабочих переходов для процесса механической обработки

Optimization of the parameters of the product manufacturing process is one of the key tasks of technological preparation of production. The technological process of mechanical processing has a complex hierarchical struc-ture. The effectiveness of optimizing the manufacturing process of a product directly depends on the level of its detail and the optimal choice of targets and control parameters. In this case, the technological process of mechanical processing, as an object of control, can be described in the form of a hierarchical model. Thus, the task of optimizing the technological process of mechanical processing is to determine the optimal values of control parameters for each structural element (intermediate state of the control object) of the hierarchical control model. The aim of the work is to develop a hierarchical model for optimizing the parameters of a complex of working transitions for machining operations. The structure of the hierarchical model of the product manufacturing process on metal-cutting machines is described. Within the framework of the developed model, five control levels are identified, control parameters for individual structural elements of the model are defined, as well as the relationship between them. For the intermediate states of the control object (structural elements), a description of single and vector optimization criteria is presented. The practical implementation of the developed control model is presented using the example of optimizing the technological parameters for the “Bushing” part. The application of the developed control model will increase the efficiency of the technological process of manufacturing products on metal-cutting machines by optimizing technological parameters based on a multi-criteria analysis at the stage of technological preparation of production.

Analysis of Inverted Planar Perovskite Solar Cells with Graphene Oxide as HTL using L9 OA Taguchi Method

In this work, the Taguchi Method approach is used to optimize graphene oxide (GO) as the hole transport layer (HTL) in inverted perovskite solar cells (IPSC). By using this method, the data from the numerical modelling Solar Cell Capacitance Simulator-One Dimensional (SCAPS-1D) was optimized. While it has distinct parameter results and diverse causes, it also takes a long time to complete the analysis process. The Taguchi method is reported to be able to find the most significant factor and reduce the parameter variations in less time. The Taguchi algorithm is used in this experiment because it is based on orthogonal array (OA) experiments, which provide a substantially smaller variance for the experiment with optimal control parameter values. SCAPS-1D software was used to simulate the IPSC with GO as HTL. The results obtained with the software are then analysed and compared with the performance of the solar cell. The final results show that the Taguchi Method optimized IPSC with GO as HTL achieved better Power Conversion Efficiency (PCE) compared to previous researchers, with the efficiency increasing from 18.53%.to 23.408%.

Automatic smooth map generation of internal combustion engines via local-global model based calibration technique

The addition of new sensors and actuators to the engine, to reduce fuel consumption and emissions besides improving the engine operation, complicates the control commands stored in the engine control unit (ECU). Substitution of mechanical actuators with electronic ones increases the engine’s degrees of freedom and the number of control parameters, which results in the increased engine calibration time and cost. The aim of this paper is to take advantage of optimization techniques to achieve optimal values of control parameters in a fast and automated way. In this regard, it requires replacing the real engine with the virtual model and implementing the model-based calibration by coupling the virtual engine model with optimization algorithms. In this study, deep neural network (DNN) modeling and genetic algorithm (GA, NSGA-II) optimization are used for model-based calibration. The effect of all input control parameters, including ignition angle, continuously variable valve timing, etc., on all output control parameters including, brake-specific fuel consumption, emissions level, knock limit, combustion stability, etc., are investigated simultaneously by a valid global model, which is a remarkable achievement in the model-based calibration. Dynamic lag of some actuators delays the execution of control commands sent from ECU. To avoid abrupt variations in the actuators values, smoothness of the engine maps is considered in the calibration process. To reduce fuel consumption, decrease emission levels and attain smooth maps, the calibration of control parameters is performed by local-multi-objective optimization and global-single-objective optimization. Local-global model-based calibration presented in this study reduces 3.7% of the brake-specific fuel consumption and 7%–10% of emissions level at breakpoints of the engine map compared to manual calibration. In addition, the calibration time and costs while producing better engine performance can be reduced by automating the calibration process. Finally, calibrated maps are stored as a lookup table (LUT) in ECU. Generating an optimal lookup table involves the pre-calculation of several points that cover the calculation domain and allow the interpolation for other points. Selecting the optimal points for exact calculation is of great importance in the size and accuracy of LUT. In this study, an optimization tool is also presented to generate accurate and efficient LUT.

Control parameter optimization of underwater gliders with uncertainty using hierarchical optimization method and interval analysis theory

This article attempts to solve the engineering optimization problem for control parameter configuration of underwater gliders with parameter uncertainty. The optimization objectives are maximizing the glider energy utilization rate and average voyage velocity; design variables include the net buoyancy adjustment amount and movable mass block translation amount; and uncertain parameters include the glider control parameter errors, manufacturing errors and sensor measurement errors. First, a dynamic simulation-based performance evaluation model of the glider is established, and interval number order theory is used to quantify the uncertainty. Then, surrogate models of the performance evaluation model are established, and the Sobol’ method-based sensitivity analysis is executed to identify the key uncertain parameters. Thus, a hierarchical optimization framework is introduced, and its system level and sub-level are used for determining optimal control parameter values and performance evaluation parameter intervals, respectively. Finally, optimization calculation is executed under different parameter configurations, and some rules are summarized.

A hybrid polynomial-based optimization method for underwater gliders with parameter uncertainty

As a type of enduring platform for ocean exploration, underwater glider usually contains some uncertain parameters in actual engineering applications, which are related to manufacturing errors, assembly errors, and sensor measurement errors. Uncertain parameters will make the glider performance show uncertain characteristics, so it is necessary to consider their effects in the design optimization process for an underwater glider. In this paper, a hybrid polynomial-based optimization method is proposed to solve optimization problems of underwater gliders with parameter uncertainty. The hybrid polynomial consists of power function terms and trigonometric function terms, and it is used for fitting surrogate models of the glider dynamic models. The fitted surrogate models are used for quickly calculating the performance evaluation parameters of underwater gliders. Specifically, power function terms and trigonometric function terms correspond to optimization design variables and uncertain parameters, respectively. The hybrid polynomial method covers the convenience of the traditional response surface method and the advantage of the Chebyshev polynomial in interval analysis. Based on surrogate models and interval analysis, we can estimate boundary functions of performance evaluation parameters, and they are employed to construct optimization objective functions. Then, the optimization iteration calculation can be carried out. Here, the proposed method is used for determining the optimal control parameter values of the Petrel-II glider with the uncertainty of some configuration and measurement parameters. The optimization objectives are to minimize the specific energy consumption and maximize the average advance velocity simultaneously. Finally, some laws of the glider performance are summarized.

Integrated control policies of production, returns’ replenishment and inspection for unreliable hybrid manufacturing-remanufacturing systems with a quality constraint

This paper deals with the production planning and control problem within unreliable Hybrid Manufacturing-Remanufacturing Systems (HMRSs) evolving in a stochastic and dynamic environment. The customer demand can then be fulfilled by either manufacturing of new units or remanufacturing of returned ones. However, both used materials and returns may contain a fraction of defective items. The paper investigates how companies could benefit from such integrated control policy to assist their managers in determining production rates, the sequence and the size of replenishment orders as well as their sample size to be inspected. The corresponding problem aims to propose an efficient control policy integrating four central decisions to coordinate remanufacturing, manufacturing, replenishment of returns and their quality control while minimizing the total cost and satisfying a quality constraint requested by customers. A simulation-based optimization approach is used to tackle the problem. It aims to determine the optimal values of control parameters of the proposed policy minimizing the total cost. The results provide relevant insights and bring effective solutions to help decision makers manage simultaneously manufacturing, remanufacturing, replenishment of returns and their quality control activities. A comparative study is also performed showing that our policy, based on sampling inspection of delivered returns, outperforms in terms of costs those most used in the literature where complete (i.e. 100%) or no inspection strategies are used.

Adaptive Fractional‐Order Super‐Twisting Sliding Mode Controller for Lower Limb Rehabilitation Exoskeleton in Constraint Circumstances Based on the Grey Wolf Optimization Algorithm

In this work, a lower limb exoskeleton with uncertainties and external disturbances under constrained motion has been controlled by developing a model‐free adaptive optimal fractional‐ordersuper‐twisting sliding mode (AOFSTSM) controller. Contrary to popular existing modeling methods, the modeling of the lower limb exoskeleton has been accomplished in the case of contact with the ground. A fractional‐order operator and super‐twisting sliding mode control have been combined to achieve an excellent tracking performance, chatter‐free control inputs, and robustness to external disturbances and uncertainties. The controller structure is model‐independent which has been derived without needing to know the dynamics of the system. An adaptive control strategy has been adopted as an approximating method to evaluate the uncertain dynamics of the system without the necessity for knowing the prior knowledge of the upper bounds. A grey wolf optimization technique has been used to find the optimal values of controller parameters. The stability of the overall system has been investigated and derived from the Lyapunov stability criterion. To validate the effectiveness of our proposed controller structure, a series of comparative simulations have been conducted with optimal fractional‐order super‐twisting sliding mode (OFSTSM) controller and fractional‐order super‐twisting sliding mode (FSTSM) controller. The comparative simulations have been also carried out with other types of recently existing control methods and recently existing optimization algorithms. The results of numerical simulations indicate the superiority of the AOFSTSM controller over other types of recently existing optimization algorithms and controllers in terms of tracking error and robustness towards the uncertainties and external disturbances.

Dynamic Analysis and Optimal Control of Rumor Propagation Model with Reporting Effect

Media reports and the number of rumors in the media have an important impact on the spread of rumors. Currently, studies that consider these two factors comprehensively on rumor propagation are uncommon. It is better to consider both effects comprehensively than to consider one of them alone. In this paper, we established a new propagation model, which regards the number of rumors in the media as a subclass that changes with time, and then comprehensively considered the effect of these two factors on the process of rumor propagation. We proved the existence and stability of equilibrium points of the model. Then, we selected two reasonable control variables: science popularization intensity and punishment intensity. We find that the control effects are optimal when these two control variables take the maximum value. Theoretical analysis and numerical simulation results showed that positive media publicity can reduce the spread of rumors, but cannot stop the spread of rumors. And under the parameters given in this paper, the optimal control parameter values satisfying the constraints can be calculated through quantitative analysis. This method provided a new idea for studying the optimal control of rumor propagation.

Design of a fractional order two layer fuzzy logic controller for drug delivery to regulate blood pressure

The main objective of this research work is to propose an effective, robust, optimal and oscillation free controller for automatic drug infusion in mean arterial blood pressure (MAP) control for anaesthesia administration, accidental case, cardiac surgery, critically-ill and post-surgery recovery wherein this system also faces uncertainties like external disturbances or noises and parameter uncertainties that adversely affect the system performance. As a consequence, the controllers designed for the MAP become indispensable to compensate such uncertainties and it is quite a complicated task for control engineers. Therefore, a controlled drug administration is required to regulate the MAP of a person during surgery/observation. In this research work, a new fractional order two layer fuzzy logic controller (FO-TLFLC) is designed to regulate MAP of a patient by administering the drug sodium nitroprusside (SNP) in a controlled manner for tracking desired blood pressure of patient in various conditions. Further, a well-established optimization technique named as grey wolf optimization (GWO) is employed to obtain optimal values of control parameters while minimizing integral absolute error (IAE). In order to present the effectiveness and robustness of proposed FO-TLFLC controller, the performance of proposed controller is compared with integer order two layer fuzzy logic controller (IO-TLFLC), single layer fuzzy logic controller (SLFLC), and proportional integral derivative (PID) controllers for maintaining the MAP to 100 mmHg in reasonable limit through SNP drug infusion. From the experimental results, it is concluded that FO-TLFLC controller can not only maintain the desired MAP but also improve the overshoot, settling-time, and error along with robustness.

Optimizing the control of transition to turbulence using a Bayesian method

The nonlinear robustness of laminar plane Couette flow is considered under the action of in-phase spanwise wall oscillations by computing properties of the edge of chaos, i.e. the boundary of its basins of attraction. Three measures are used to quantify the chosen control strategy on laminar-to-turbulent transition: the kinetic energy of edge states (local attractors on the edge of chaos), the form of the minimal seed (least energetic perturbation on the edge of chaos), and the laminarization probability (the probability that a random perturbation from the laminar flow of given kinetic energy will laminarize). A novel Bayesian approach is introduced to enable the accurate computation of the laminarization probability at a fraction of the cost of previous methods. While the edge state and the minimal seed provide useful information about the dynamics of transition to turbulence, neither measure is particularly useful to judge the effectiveness of the control strategy since they are not representative of the global geometry of the edge. In contrast, the laminarization probability provides global information about the edge and can be used to evaluate the control effectiveness by computing a laminarization score (the expected laminarization probability) and the associated expected dissipation rate of the controlled flow. These two quantities allow for the determination of optimal control parameter values subject to desired constraints. The results discussed in the paper are expected to be applied to a wide range of transitional flows and control strategies aimed at suppressing or triggering transition to turbulence.

Self-tuning control of parametrically excited active magnetic bearing system due to harmonic base motion using fuzzy logic

Using active magnetic bearings for vibration control of flexible rotors subject to large base motion is both interesting and challenging. Rotors on ships, airplanes, and space-crafts fall in this category. These applications pose challenge for the active magnetic bearing designer, as the large motion of base renders the rotor-shaft-AMB system, time varying in nature, and also causes parametric excitation to the rotor system. Apart from stability concerns in such systems, it is difficult to design and choose optimal values of controller parameters because the system is subject to different base motions at different times during operation. In order to address this issue, this work applies fuzzy logic and proposes a simple yet effective selftuning control (STC) to control vibrations of flexible rotors supported by active magnetic bearings, which is subject to excitations due to base motion, in addition to unbalance excitation, usually present in rotors. To this end, first, the controller parameters are optimized considering the levitation performance of active magnetic bearings based on the transient response. Next, the fuzzy logic-based self-tuning algorithm is presented. Detailed comparison of performance between optimal and self-tuning control for different base motion conditions show that the proposed self-tuning controller outperforms the optimal control.

Optimal control parameters for bat algorithm in maximum power point tracker of photovoltaic energy systems

Partial shading conditions generate multiple peaks in the P-V curve of photovoltaic (PV) arrays. Smart MPPT optimization techniques should be used to capture the global peak (GP) and avoid being trapped in one of the local peaks (LPs). The tracking of the GP should be fast and reliable to enhance the stability and increase the generated efficiency of the PV systems. Bat algorithm (BA) is one of the fastest swarm optimization techniques. The BA control parameters (BA-CPs) have substantial effects on their performance. This paper introduced a nested BA strategy called BA-BA strategy to determine the optimal values of control parameters of BA for the lowest convergence time and failure convergence rate to be used in the online MPPT of PV systems. The inner BA loop used the BA as an MPPT of the PV system, meanwhile, the outer BA loop used the inner BA loop as a fitness function to determine the optimal BA-CPs for minimum convergence time and failure rate. Ten benchmark BA strategies, particle swarm optimization (PSO), and cuckoo search (CS) algorithm have been used to compare their results with the results obtained from the BA-BA strategy. The results of the BA-BA strategy reduced the convergence time of 250% of the time associated with the best benchmark BA strategy, 518%, and 395% as compared to the PSO, and CS algorithm, respectively. The simulation and experimental results obtained from the BA-BA strategy showed its superior for determining the optimal control parameters for BA in MPPT of PV systems or any other applications.

A Novel Strategy for Optimal PSO Control Parameters Determination for PV Energy Systems

This study introduces a novel strategy that can determine the optimal values of control parameters of a PSO. These optimal control parameters will be very valuable to all the online optimization problems where the convergence time and the failure convergence rate are vital concerns. The newly proposed strategy uses two nested PSO (NESTPSO) searching loops; the inner one contained the original objective function, and the outer one used the inner PSO as a fitness function. The control parameters and the swarm size acted as the optimization variables for the outer loop. These variables were optimized for the lowest premature convergence rate, the lowest number of iterations, and the lowest swarm size. The new proposed strategy can be used for all the swarm optimization techniques as well. The results showed the superiority of the proposed NESTPSO control parameter determination when compared with several state of the art PSO strategies.

Minimisation of power oscillations with a novel optimal control strategy for distributed generation inverter under grid faulty and harmonic networks

The increasing penetration of distributed generation (DG) inverters supplying power to the networks bring challenges to the power system under the grid faulty and harmonic grid since negative sequence components lead to double oscillations. Reference current generation (RCG)-based flexible control strategies ensure solutions to mitigate oscillations on the control signals and to maintain a more stable, secure and reliable power system operation. In this direction, this study proposes an RCG-based optimal control strategy that reduces oscillations of active and reactive power at optimal control parameter values and ensures minimum harmonic contents at the grid side under an unbalanced and harmonic distortion grid. The main cause of the power oscillations is comprehensively demonstrated, optimised and examined with numerical studies and theoretically. Control parameters are accurately selected by the courtesies of optimisation function to minimise power oscillations. A fast and robust third-order sinusoidal signal integrator (TOSSI) extracts positive–negative sequence components for the calculation of the RCG. To provide a fast transient response and achieve almost current tracking errors at a zero level, proportional complex integral (PCI) current regulation controller is selected. Various case studies have been performed to show the validity and availability of the proposed entire system, including flexible control, optimisation, TOSSI and PCI.

An IoT Framework for Modeling and Controlling Thermal Comfort in Buildings

Humans spend more than 90% of their day in buildings, where their health and productivity are demonstrably linked to thermal comfort. Building thermal comfort systems account for the largest share of U.S energy consumption. Despite this high-energy cost, due to building design complexity and the variety of building occupant needs, addressing thermal comfort in buildings remains a difficult problem. To overcome this challenge, this paper presents an Internet of Things (IoT) approach to efficiently model and control comfort in buildings. In the model phase, a method to access and exploit wearable device data to build a personal thermal comfort model has been presented. Various supervised machine-learning algorithms are evaluated to produce accurate personal thermal comfort models for each building occupant that exhibit superior performance compared to a general model for all occupants. The developed comfort models were used to simulate an intelligent comfort controller that uses the particle swarm optimization(PSO) method to search for optimal control parameter values to achieve maximum comfort. Finally, a framework for experimental validation of the new proposed comfort controller that interactively works with the HVAC element has been introduced.

INVESTIGATION ON AL6061-SIC MMC FOR OPTIMIZATION OF DRILLING PARAMETERS USING BEE COLONY OPTIMIZATION ALGORITHM

In this investigation, Bee Colony Optimization algorithm (BCO) has been utilized to extract the best suitable optimal control parameter values for the Electric Discharge Machining (EDM) in drilling holes over Al6061-SiC metal matrix composite for yielding better surface finish (Ra), maximum Material Removal Rate (MRR) and minimum Tool Wear Rate (TWR). Further to formulate an empirical relational equation between the above mentioned EDM process parameters, in this research, 27 different experiments had been carried out by varying the values of input control variables and are SiC particle Size (Ps), discharge current (Id) and pulse on time (Pon). The formulated second order relational equations have been used as the objective fitness function of the BCO algorithm to pinpoint the worthiness of the results. The developed BCO algorithm will examine the best blend of input process variables from the solution space at random, which can yield maximum MRR, minimum TWR and better Ra. Validation tests had also been conducted to check the rightness of the investigation. Also it is observed that the BCO results and experimental results have revealed a good agreement with acceptable deviations. So in this research, it has been concluded that the BCO algorithm can be one of the best optimization algorithm, that can be used to detect the better optimal values for the EDM control parameters to attain desired output.

Effect of different parameters and aging time on wear resistance and hardness of SiC-B4C reinforced AA6061 alloy

Applications of aluminum matrix composites are increasing in aerospace, automotive and biomedical fields because of their high strength, light weight and good wear resistant properties. From an intense literature review it is observed that various combinations of aluminum alloys have been developed through different manufacturing methodologies and their characteristics have been studied. It is identified that the study of a composite material consists of AA6061 alloy as a matrix material reinforced with SiC and B4C; a comparative experiment on sliding wear characteristics, hardness and wear resistant properties of nanocomposite with different aging time is a novel area yet to be undertaken. This paper presents a study of sliding wear characteristics in silicon carbide, boron carbide reinforcement prepared by using stir casting process. The hardness and wear resistance properties of nanocomposites with different aging time were measured in the work. When comparatively analyzed with AA6061, AA6061 reinforced with silicon carbide, AA6061 reinforced with boron carbide exhibited a steady and higher hardness than the other two metal composites. Also, we observed that when sliding wear characteristics are compared, the AMC reinforced with boron carbide exhibits an optimum wear rate. The optimal values of control parameters in wear rate and friction coefficient for boron carbide reinforced aluminum matrix composites are determined by response surface methodology. The control parameters considered are applied load, speed, weight % of reinforcement and distance. The effect of control parameters on the output variables was investigated by analysis of variance (ANOVA) and response plot. Interestingly, 27% increase in hardness value for B4C reinforced composite material at the aging time of 200 minutes was observed. Furthermore, wear rate for B4C reinforced composite material was four times reduced as compared to monolithic AA6061. These improvements may be attributed to the homogeneous particle size, particle distribution without agglomeration and optimized parameters.

Robust optimal control and identification of adaptive cruise control systems in the presence of time delay and parameter uncertainties

This article deals with the robust optimal control and identification of uncertain adaptive cruise control systems. Both measurement delay and engine’s lag are investigated in system modeling and control design. The control structure consists of two steps. In the first step, by using a new approach, the uncertain parameters of longitudinal dynamics of each vehicle is identified. Afterwards, at the second step, by using the relative position and velocity measurement with respect to ahead vehicle, a robust control against size changing of platoon is presented to assure the internal stability, string stability, and safety of heterogeneous vehicular platoons. A cost function involving important metrics internal stability, maximum overshoot, and settling time is introduced, and the particle swarm optimization algorithm is used to find the optimal values of control parameters minimizing the cost function. It will be shown that the proposed robust controller guarantees the internal stability, string stability, and safety of adaptive cruise control systems in the presence of measurement and parasitic delays. Several simulation results are provided to show the effectiveness of the proposed approaches.