Parameter Adaptive Control Research Articles

Design of multi-module adaptive fuzzy power tracking control for nonlinear four-point reactor core model under multiple transient conditions

In this work, a multi-module adaptive fuzzy controller is designed for multipoint reactor core model of VVER-1000 to minimize the axial power variations among the corresponding nodes under efficient load tracking abilities and disturbance rejection. The validated four-point reactor model is syndicated using diffusion strategy and consideration of thermal hydraulics, reactivity feedbacks, poison concentration and three groups of delayed neutron precursors. The control approach followed here is parametric adaptive control which uses the online identification of a process model as its performance monitor. The adaptive design makes adjustments among the various modules according to the changing dynamic properties of the nonlinear reactor process. The multi-module structure is based on the absolute error level and develops online switching of control modes with anticipation of optimized gains for all operating conditions. The selection of fuzzy parameters regarding severity level of control output is based on desired performance measures and actuator design capacity limitations under multiple transient conditions. A Lyapunov-like function is established for the stability criteria of the four-point reactor control model. Simulation results show the effectual and robust load following power control of model under large load rejection, parametric perturbations and external reactivity variations with axial power deviations as low as 0.004% in comparison with PID configured controller.

Digital Twins in deep drawing for virtual tool commissioning and inline parameter optimization

Fluctuating boundary conditions and the highly nonlinear process behavior of deep drawing operations make experience-based selection of suitable control parameters difficult. Nowadays, commissioning deep drawing tools, i.e., die spotting and identification of suitable control parameters for defect-free parts, is conducted on real world try-out presses. Transferring the tools to production machines entails adaption of these initial parameters. Once production is ramped up, any changes to material properties, lubrication and press behavior require continuous manual machine parameter tuning. Virtual tool commissioning and utilizing these simulation models in the production phase to adapt control parameters would reduce time and cost over the entire life cycle of the machine and the tool sets. The virtual representative, which provides services for a plant over several life cycle phases, is also referred to as Digital Twin. In this paper, the authors present a Digital Twin concept for deep drawing presses to predict the state of the system and optimize the control parameters during the production. The integration of all involved subsystems into one system simulation and its efficient calculation is the biggest challenge. The authors combine a virtual commissioning simulation tool with a finite element model to implement all relevant properties of the deep drawing press and the interaction of its subsystems. It is shown, how the idea of a system simulation makes predictions of system parameters specific to a production situation possible, and therefore, can help to select suitable control parameters that leads to a reduction of the error rate in deep drawing.

Selecting a method for the parametric adaptation of pi-controller in the control systems of boiler assemblies at thermal power stations with supercritical parameters

In the context of the growing share of renewable energy sources, the role of thermal power plants (TPPs) as means of balancing the daily power demand curve is increasing. During the day, the load on working units varies widely. Boiler assemblies of these power units undergo changes in their dynamic characteristics when the load changes. Control systems must, regardless of the mode of operation, meet requirements for the quality of operation. This paper has analyzed the latest research and advancements in the field of synthesis of adaptive and robust control systems for inertial contours of direct-flow boiler assemblies. It reports a model of the section of a water-steam flow path, which takes into consideration changes in the dynamic characteristics of the section when changing the load of the power unit. A model of the temperature control system for a boiler assembly has been built involving a tabular method for adjusting the PI-controller parameters. Alternative methods for the adaptation of parameters were proposed. The resulting expressions demonstrate a piecewise-linear approximation of parameter changes depending on the load. In addition, an adaptation unit based on fuzzy logic were suggested. Static characteristics of the adaptation units for PI-controller parameters depending on the load of the power unit were defined. Based on computer modeling, a comparative analysis of the quality indicators of the functioning of the designed control systems was carried out. A method for estimating the stability of systems with adaptation of adjustment parameters was proposed. Based on the static characteristics of the pairs of settings of the PI-controller and the parameters of the control object for each load value at the predefined discreteness, stability reserves were calculated for gain and phase. The results reported here indicate the advantages of a control system with the adaptation of controller parameters based on piecewise-linear dependences

Prescribed Performance-Based Adaptive Terminal Sliding Mode Control for Virtual Synchronous Generators

Due to the lack of inertia and damping in the distributed power system with power electronic devices as interface, the stability of the power system will be adversely affected when the distributed power system is connected to the grid. In order to solve the problem of low inertia and improve stability, an adaptive terminal sliding mode control method based on the electromagnetic transient characteristics of virtual synchronous generators is proposed. During the design process, projection operators and parameter adaptive law control methods are introduced to compensate for parameter errors caused by environmental interference. Furthermore, by utilizing a prescribed performance function, the terminal sliding surface of the system will have the desired dynamic response, and variations of the system tracking error can be restricted. Simulation results show that the proposed controller has improved control accuracy and response speed, frequency stability, and power response.

An Adaptive Three-Dimensional Improved Virtual Force Coverage Algorithm for Nodes in WSN

The original virtual force algorithm (VFA) is proposed for the two-dimensional node coverage and localization of wireless sensor networks (WSN). This work proposes a novel three-dimensional improved virtual force coverage (3D-IVFC) algorithm for the 3D coverage of nodes in WSN. Firstly, the node coverage theory is analyzed, which is about node coverage in three-dimensional space. Secondly, an improved three-dimensional space virtual force coverage method is proposed with an adaptive virtual force parameter control strategy. Finally, simulation experiments are utilized to verify the performance of the 3D-IVFC approach. Experimental results show that during random initialization, the average coverage rate of the improved 3D space coverage algorithm was increased by 0.76% and the deployment time was reduced by 0.1712 s; during center initialization, the average coverage rate of the improved 3D space coverage algorithm was increased by 0.65% and the coverage time increased slightly. Moreover, the proposed method is also used to solve the three-dimensional surface node coverage of the WSN.

Co-Design of the Control and Power Stages of a Boost-Based Rectifier with Power Factor Correction Depending on Performance Criteria

Rectifiers with power factor correction are key devices to supply DC loads from AC sources, guaranteeing a power factor close to one and low total harmonic distortion. Boost-based power factor correction rectifiers are the most widely used topology and they are formed by a power stage (diode bridge and Boost converter) and a control system. However, there is a relevant control problem, because controllers are designed with linearized models of the converters for a specific operating point; consequently, the required dynamic performance and stability of the whole system for different operating points are not guaranteed. Another weak and common practice is to design the power and control stages independently. This paper proposes a co-design procedure for both the power stage and the control system of a Boost-based PFC rectifier, which is focused on guaranteeing the system’s stability in any operating conditions. Moreover, the design procedure assures a maximum switching frequency and the fulfillment of different design requirements for the output voltage: maximum overshoot and settling time before load disturbances, maximum ripple, and the desired damping ratio. The proposed control has a cascade structure, where the inner loop is a sliding-mode controller (SMC) to track the inductor current reference, and the outer loop is an adaptive PI regulator of the output voltage, which manipulates the amplitude of the inductor current reference. The paper includes the stability analysis of the SMC, the design procedure of the inductor to guarantee the system stability, and the design of the adaptive PI controller parameters and the capacitor to achieve the desired dynamic performance of the output voltage. The proposed rectifier is simulated in PSIM and the results validate the co-design procedures and show that the proposed system is stable for any operating conditions and satisfies the design requirements.

Adaptive differential evolution with ensembling operators for continuous optimization problems

Differential evolution is one of the most popular evolutionary algorithms for continuous optimization. In this paper, we introduce a new algorithm named the adaptive differential evolution with ensembling populations. In the proposed algorithm, two sets of mutation and crossover operators are utilized to generate offspring to better balance the exploitation and exploration abilities of the algorithm. Besides, an adaptive parameter control strategy is integrated to dynamically adjust the parameter setting of the algorithm so as to further improve the search efficacy. In the experimental studies, it is demonstrated that the proposed algorithm presents competitive performance on benchmark functions as well as on the real-world wireless sensor localization application, in terms of global search ability and search efficiency.

Adaptive backtracking search optimization algorithm with a dual-learning strategy for dynamic economic dispatch with valve-point effects

Dynamic economic dispatch with valve-point effect (DED_vpe) is a dynamic nonlinear high-dimensional optimization problem with non-smooth and non-convex characteristics. Meta-heuristic methods have become the mainstream for solving the DED_vpe problem. However, most of these methods only focus on minimizing the generation costs and ignore the algorithmic robustness. In this paper, an adaptive backtracking search optimization algorithm with a dual-learning strategy (DABSA) is proposed for solving the DED_vpe problem. In DABSA, a dual-learning strategy (DL) based on the current and historical optimal individuals is developed to update each individual. This updating strategy helps DABSA improve solution accuracy and overcome premature convergence. In addition, an adaptive parameter control mechanism (APC), which can automatically adjust parameter ‘mixrate’ value according to the current iteration number, is presented. To handle the system constraints, a ‘repair + penalty’ constraints-handling approach is employed to lead non-feasible solutions towards the feasible region quickly. The performance of DABSA is assessed by testing on four DED problems containing 5, 10 and 30 units. The experimental results show that DABSA is very competitive compared with reported representative methods in yielding low fuel costs along with high robustness.

REDACS: Regional emergency-driven adaptive cluster sampling for effective COVID-19 management

As COVID-19 is spreading, national agencies need to monitor and track several metrics. Since we do not have perfect testing programs on the hand, one needs to develop an advanced sampling strategies for prevalence study, control and management. Here we introduce REDACS: Regional emergency-driven adaptive cluster sampling for effective COVID-19 management and control and justify its usage for COVID-19. We show its advantages over classical massive individual testing sampling plans. We also point out how regional and spatial heterogeneity underlines proper sampling. Fundamental importance of adaptive control parameters from emergency health stations and medical frontline is outlined. Since the Northern hemisphere entered Autumn and Winter season (this paper was originally submitted in November 2020), practical illustration from spatial heterogeneity of Chile (Southern hemisphere, which already experienced COVID-19 winter outbreak peak) is underlying the importance of proper regional heterogeneity of sampling plan. We explain the regional heterogeneity by microbiological backgrounds and link it to behavior of Lyapunov exponents. We also discuss screening by antigen tests from the perspective of “on the fly” biomarker validation, i.e., during the screening.

Enhancing the search ability of a hybrid LSHADE for global optimization of interplanetary trajectory design

The global optimization of interplanetary trajectory design is a challenge and tough problem in deep space. To cope with the extreme nonlinearity of the search space and a large number of locally optimal solutions, a novel hybrid algorithm based on success-history based adaptive differential evolution with linear population size reduction (LSHADE), called HLSHADE, is proposed. In HLSHADE, after the global search finds a better solution, a new two-step local search with an adaptive control parameters strategy is designed to enhance the exploitation ability of HLSHADE. The presented method is tested on well-known global trajectory optimization problems (GTOPs) developed by the Advanced Concepts Team of the European Space Agency (ESA). Experimental results have demonstrated the competitive performance of HLSHADE with respect to the three sets of compared algorithms, including LSHADE and ten of its variants, six famous algorithms in the IEEE Competitions on Evolutionary Computation (CEC) and eleven algorithms in the software platform PyGMO developed by the ESA's Advanced Concepts Team.

A novel pseudo-random number generator for IoT based on a coupled map lattice system using the generalised symmetric map

Pseudo-random number generators (PRNGs) are one of the building blocks of cryptographic methods and therefore, new and improved PRNGs are continuously developed. In this study, a novel method to generate pseudo-random sequences using coupled map lattices is presented. Chaotic maps only show their chaotic behaviour for a specified range of control parameters, what can restrict their application in cryptography. In this work, generalised symmetric maps with adaptive control parameter are presented. This novel idea allows the user to choose any symmetric chaotic map, while ensuring that the output is a stream of independent and random sequences. Furthermore, to increase the complexity of the generated sequences, a lattice-based structure where every local map is linked to its neighbouring node via coupling factor has been used. The dynamic behaviour and randomness of the proposed system has been studied using Kolmogorov–Sinai entropy, bifurcation diagrams and the NIST statistical suite for randomness. Experimental results show that the proposed PRNG provides a large key space, generates pseudo-random sequences and is computationally suitable for IoT devices.

Adaptive Internal Model Control of SCR Denitration System Based on Multi-Objective Optimization

Many thermal power plants use selective catalytic reduction (SCR) systems to complete flue gas denitration. The system has the characteristics of large inertia, large delay and nonlinearity. At the same time, it is difficult to use the controller under a single working condition to meet the control task of the system under the full working conditions. In order to solve the above control difficulties, This paper proposes a two-degree-of-freedom internal model control (2-DOF-IMC) method based on controller parameter adaptation and multi-model adaptation to realize the control of the full working conditions of the SCR system. First, the intelligent identification method based on Differential evolution quantum particle swarm optimization (DEQPSO) is used to obtain the sub-model of ammonia injection-SCR outlet Nitrogen Oxide (NOx) concentration in typical working conditions. A 2-DOF-IMC controller under each sub-model is established. Then, aiming at system set point tracking performance, anti-disturbance performance, and control energy consumption, the Pareto optimal solution of each controller is obtained by using the Non-dominated Sorting Genetic Algorithms-II (NSGA-II) algorithm based on chaotic mutation. Finally, the controller parameter adaptive strategy is designed based on the Takagi-Sugeno (T-S) fuzzy model, and the multi-model adaptive strategy is designed based on the recursive bayesian probability weighting. Through the dynamic performance test and the anti-disturbance performance test, it is verified that the 2-DOF-IMC based on controller parameter adaptation and multi-model adaptation has good control performance and adaptability to all working conditions. The method proposed in this paper combines the advantages of multi-parameter and multi-model strategies, further improves the control quality, and provides a new solution for the control of thermal power plant SCR system.

Overall Dynamic Optimization of Metal Mine Technical Indicators Considering Spatial Distribution of Ore Grade Using AADE

In optimizing the production of a metal mine, either the overall dynamic relations between technical indicators or the spatial distribution of the ore grade are usually considered, but few studies have considered both factors together. These two factors in combination have a greater effect on the optimization of mine production in terms of economic benefit and resource utilization than they do individually. We proposed an overall dynamic optimization model of technical indicators of metal mine production that considers the spatial distribution of the ore grade to better optimize the technical indicators and improve sustainable development of mineral resources. We incorporated an adaptive mutation strategy and adaptive control parameters into a differential evolution algorithm (AADE) in order to overcome the drawbacks of the differential evolution algorithm in solving this optimization model. The adaptive mutation strategy and adaptive control parameters were used to increase the rate of convergence and improve the search for a global maximum. To assess the performance of AADE, we used a real case and four test functions (the Sphere, Griewank, Rastrigin and Rosenbrock functions) in tests that compared AADE with a standard genetic algorithm, a standard differential evolution algorithm and the recently developed adaptive differential evolution algorithm. The results indicate that the optimization model we created is better aligned with mine production processes than current optimization models. In optimizing the technical indicators of metal mine production to maximize economic benefits, AADE performed significantly better than the other three algorithms tested in terms of convergence rate and global search ability.

Research on Model-Free Adaptive Control of Electro-Hydraulic Servo System of Continuous Rotary Motor

The model-free adaptive controller (MFAC) for continuous rotary electro-hydraulic servo motor was proposed to deal with uncertainties such as friction, leakage and noise. The model of electro-hydraulic servo motor system was replaced by universal model, and the structure adaptive control and parameter adaptive control were combined, and MFAC controller of the motor system was designed by using the input and output data of the system to realize the low speed and tracking control of the motor system. It is proved by simulation and experiment that MFAC controller effectively improves the low-speed stability and anti-interference ability of electro-hydraulic servo system compared with traditional PID control, and MFAC controller widens the frequency response of the system and realizes the precise control of continuous rotary motor servo system.

Reinforcement Learning-Based Control for Resilient Community Microgrid Applications

A novel microgrid control strategy is presented in this paper. A resilient community microgrid model, which is equipped with solar PV generation and electric vehicles (EVs) and an improved inverter control system, is considered. To fully exploit the capability of the community microgrid to operate in either grid-connected mode or islanded mode, as well as to achieve improved stability of the microgrid system, universal droop control, virtual inertia control, and a reinforcement learning-based control mechanism are combined in a cohesive manner, in which adaptive control parameters are determined online to tune the influence of the controllers. The microgrid model and control mechanisms are implemented in MATLAB/Simulink and set up in real-time simulation to test the feasibility and effectiveness of the proposed model. Experiment results reveal the effectiveness of regulating the controller’s frequency and voltage for various operating conditions and scenarios of a microgrid.

Adaptive conductance control

Neuromodulation is central to the adaptation and robustness of animal nervous systems. This paper explores the classical paradigm of indirect adaptive control to design neuromodulatory controllers in conductance-based neuronal models. The adaptive control of maximal conductance parameters is shown to provide a methodology aligned with the central concepts of neuromodulation in physiology and of impedance control in robotics.

Ship RBF neural network sliding mode PID heading control

In view of the inherent non-linearity, complexity, susceptibility to external wind, wave, and current interference of under-driven ships, and the difficulty of adjusting and adjusting control parameters, to improve the performance of ship’s autopilot, a kind of RBF neural network sliding mode variable structure PID controller is designed. Traditional PID control is sensitive to parameter changes, online tuning is difficult, and easy to overshoot. In order to solve this problem, combining the variable structure characteristics of PID, a differential compensation term is added to the integral term to convert the PID control parameters into three parameters with more obvious physical meanings, and then combined with the RBF neural network learning and identification function to realize online tuning and adaptive control of ship control parameters. Using MATLAB software to simulate the container ship “MV KOTA SEGAR” MMG model shows that the designed RBF neural network sliding mode PID controller can effectively eliminate the ship’s lateral deviation caused by external interference such as wind, waves, currents, etc., with high control accuracy,robustness and strong adaptability.

A Discrete Fractional Order Adaptive Law for Parameter Estimation and Adaptive Control

In this article, a discrete fractional order adaptive law (DFOAL) is designed based on the Caputo fractional difference to perform parameter estimation of structured uncertainties. The paper provides a rigorous stability analysis of the DFOAL parameter estimation method. The DFOAL is then modified in order to improve parameter estimator performance to show that, under certain conditions, it provides asymptotic convergence to the true parameter values even when the regressor is not persistently exciting. A method to allow for practical implementation of the DFOAL and the modified DFOAL is developed. Finally, the modified DFOAL is used to identify the plant parameters in an indirect adaptive control law for a class of nonlinear discrete-time systems with structured uncertainty.

Research on UBI Auto Insurance Pricing Model Based on Parameter Adaptive SAPSO Optimal Fuzzy Controller

Aiming at the problem of “dynamic” accurate determination of rates in UBI auto insurance pricing, this paper proposes a UBI auto insurance pricing model based on fuzzy controller and optimizes it with a parameter adaptive SASPO. On the basis of the SASPO algorithm, the movement direction of the particles can be mutated and the direction can be dynamically controlled, the inertia weight value is given by the distance between the particle and the global optimal particle, and the learning factor is calculated according to the change of the fitness value, which realizes the parameter in the running process. Effective self-adjustment. A five-dimensional fuzzy controller is constructed by selecting the monthly driving mileage, the number of violations, and the driving time at night in the UBI auto insurance data. The weights are used to form fuzzy rules, and a variety of algorithms are used to optimize the membership function and fuzzy rules and compare them. The research results show that, compared with other algorithms, the parameter adaptive SAPAO algorithm can calculate more reasonable, accurate and high-quality fuzzy rules and membership functions when processing UBI auto insurance data. The accuracy and robustness of UBI auto insurance rate determination can realize dynamic and accurate determination of UBI auto insurance rates.

Automatic Correction of Parameters of Rice Phenology Prediction Model Based on Random Forest Algorithm

With the rapid development of various Internet of Things and cloud computing, a large amount of data is rapidly accumulated in various fields, and the resulting data dimension is getting higher and higher. Evolutionary algorithm has been widely used in the field of crop model parameter calibration. Because the structure of crop model includes many sets of ordinary differential equations, which are nonlinear, discontinuous and multi peak, it is particularly important to choose an appropriate evolutionary algorithm according to the characteristics of crop model. At the same time, the parameters of evolutionary algorithm have a great impact on the performance of the algorithm. The improved differential evolution algorithm with adaptive control parameters shows good convergence speed and stability in the parameter calibration of rice phenology model. Variable selection is particularly important in high-dimensional data processing, and the importance rating of variables is the key problem. Aiming at the problem of low classification accuracy and generalization ability of the model in the single variable selection algorithm, this paper proposes a hybrid variable selection algorithm. The algorithm is divided into two stages: the filtering stage, which uses mutual information to quickly eliminate some irrelevant variables and reduce the dimension of the sample space; In the encapsulation stage, under the framework of replacement theory, the residual variables are selected by random forest, which shows that the algorithm has higher classification accuracy and generalization ability than the comparison algorithm.