Control Coefficients Research Articles

Nonlinear control design for a spherical Wave Energy Converter

This paper discusses the development of two nonlinear controls for a nonlinear spherical wave energy converter (WEC) to maximize the energy it harvests from the waves. The first control is a simple nonlinear damping control, which is designed based on the hydrodynamic damping coefficients. These control coefficients are then optimized using a Genetic Algorithm. The second is a nonlinear optimal control derived analytically using the Pontryagin minimum principle for comparison. The study found that the nonlinear optimal control improves the device's performance by effectively leveraging the hydrodynamic nonlinearity from the floater's shape. The nonlinear bang-singular-bang (BSB) control showed an average 20% performance improvement over the nonlinear damping control (NLDC).

Development of automatic control system of drum boiler on the basis of fuzzy controller with pid-controller adaptation

The aim of the study is to reduce energy losses in a drum steam boiler by introducing an automatic control system realized on the basis of fuzzy adaptation of proportional-integral-derivative (PID) controller coefficients using fuzzy term-multiplications. Control algorithms of the main technological parameters of the boiler unit (air quantity, fuel quantity, steam pressure, feed water flow rate), reducing energy costs due to the maintenance of the ratio of fuel and air pressure in accordance with the mode map of the boiler unit (optimal technological process of combustion and steam formation in a steam boiler) are developed. The structural scheme of the object control is offered, in which, besides regulation of values of technological parameters, full compensation of mutual influence of all regulation circuits is made.

A Bidirectional Virtual Inertia Control Strategy for the Interconnected Converter of Standalone AC/DC Hybrid Microgrids

The interconnected converter (IC) links the ac and dc microgrids (MGs) and exchanges powers to maintain the hybrid system stability. However, inertia stored in the ac or dc side cannot be transferred through IC to support each other flexibly according to demand. This paper proposes a novel bidirectional virtual inertia (BVI) control strategy for the IC with an inertia transferring mechanism. IC actively transfers the inertial power to balance the transient performance of both sides of the interconnected MGs. The adaptive strategy is proposed for coefficients of the BVI control to change the inertial power flexibly according to the inertia gap caused by disturbances. Experimental results demonstrate that the proposed strategy of transferring inertia strikes a transient balance between the dc voltage and ac frequency and hence, enhance the transient stability of the hybrid system.

Mathematical and computer modeling of automatic control in the presence of disturbances

One of the most important tasks of the automatic motion control system in real operating conditions is to compensate for the influence of disturbances acting on the object, taking into account the peculiarities of its dynamics. This article presents a method for calculating the coefficients of automatic control, which provides a minimum of the size of the set of reactions to non-deterministic external influences, limited by the norm, and the necessary location of the roots of the characteristic polynomial of a system closed by such control. The specified algorithm is implemented in MATLAB and tested on the example of a specific marine vessel. Based on the simulation results, a conclusion is made about the acceptable quality of the generated algorithm.

Lattice Boltzmann method based feedback control approach for pinned spiral waves

Spiral waves are common in nature and have received a lot of attention. Spiral wave is the source of ventricular tachycardia and fibrillation, and pinned spiral wave is less likely to be eliminated than free spiral wave. Therefore, it is important to find an effective method to control the pinned spiral wave. In this work, we investigate the feedback control approach to eliminating pinned spiral wave based on the lattice Boltzmann method, by using the FitzHugh-Nagumo model as an object. The numerical results show that the feedback control method has a good control effect on the pinned spiral wave no matter whether it is pinned on a circular or rectangular obstacle. In addition, the excitability coefficient, amplitude of feedback control, recording feedback signal time and obstacle size are systematically investigated by numerical simulation. The study shows that there are three cases of pinned spiral wave cancellation. Firstly, the amplitude of feedback control and excitability coefficient are related to the time required to eliminate the pinned spiral wave, and the larger the amplitude of feedback control signal or the smaller the excitability coefficient, the faster the cancellation of the pinned spiral waveis. Secondly, the size of the obstacle and the excitability coefficient affect the time interval between the time of recording the feedback signal and the time of adding the feedback control that can successfully control the pinned spiral wave. Finally, the recorded feedback signal time affects the minimum amplitude of feedback control required to successfully eliminate the pinned spiral wave, while the added feedback control time is constant. According to the discussion in this paper, it can be seen that the feedback control method has a good control effect on the pinned spiral wave.

Research the adaptive neural networks using possibility in control systems for cycle chemistry at thermal power plants

The study is devoted to the analysis of the learning possibilities of adaptive neural networks in the context of water-chemical regime management systems. During a series of experiments on a laboratory stand with a disturbing effect, transient characteristics describing changes in the controlled value were obtained. The obtained mathematical models using the TunePID program served as the basis for the analysis and research of adaptive neural networks. Adaptive neural networks are a powerful tool capable of learning from the data obtained and dynamically changing their structure and parameters to accurately predict and control the behavior of the system. Unlike classical mathematical models, adaptive neural networks have the ability to process nonlinear dependencies and adapt to changing conditions without the need for manual reconfiguration. The stages of identification of disturbances and adaptation of compensator settings were described and illustrated. The study included an analysis of the work of a neural network with a different number of hidden neurons and various activation functions. The results showed that neural networks trained on data using an adaptive approach are able to predict and control the system with an accuracy comparable to the calculated parameters of the control coefficients calculated manually.

Click-pixel Cognition Fusion Network with Balanced Cut for Interactive Image Segmentation.

Interactive image segmentation (IIS) has been widely used in various fields, such as medicine, industry, etc. However, some core issues, such as pixel imbalance, remain unresolved so far. Different from existing methods based on pre-processing or post-processing, we analyze the cause of pixel imbalance in depth from the two perspectives of pixel number and pixel difficulty. Based on this, a novel and unified Click-pixel Cognition Fusion network with Balanced Cut (CCF-BC) is proposed in this paper. On the one hand, the Click-pixel Cognition Fusion (CCF) module, inspired by the human cognition mechanism, is designed to increase the number of click-related pixels (namely, positive pixels) being correctly segmented, where the click and visual information are fully fused by using a progressive three-tier interaction strategy. On the other hand, a general loss, Balanced Normalized Focal Loss (BNFL), is proposed. Its core is to use a group of control coefficients related to sample gradients and forces the network to pay more attention to positive and hard-to-segment pixels during training. As a result, BNFL always tends to obtain a balanced cut of positive and negative samples in the decision space. Theoretical analysis shows that the commonly used Focal and BCE losses can be regarded as special cases of BNFL. Experiment results of five well-recognized datasets have shown the superiority of the proposed CCF-BC method compared to other state-of-the-art methods. Code will be publicly available at https://github.com/lab206/CCF-BC.

Comparative Investigation of DSP-Based Speed Control of PMSM Using Proportional Integral and Takagi-Sugeno Fuzzy Logic Controller

This paper presents the simulation and experimental results of speed control of a three-phase Permanent Magnet Synchronous Motor (PMSM). The control techniques employed in this work include a Proportional Integral (PI) controller and a Takagi-Sugeno Fuzzy Logic Controller (TS-FLC). While theory and simulation help in predicting the PI controller settings for real drive implementation, further adjustment of the PI controller coefficients is still needed for optimal performance. However, the complexity of PI controller tuning can be overcome by employing a fuzzy logic controller, which is less affected by variations in model parameters and load torque. Two variants of the proposed TS-FLC are implemented: a simple algorithm with 9 rules and a standard algorithm with 49 rules. The drive system utilizes Field Oriented Control (FOC). The control program is developed using the C/C++ programming language and executed on the Texas Instruments TMS320LF28335 Digital Signal Processor (DSP), known for its intelligent motor control capabilities. The drive system evaluation was performed through simulations and experimentation using the MCK28335 professional development kit provided by Technosoft Company. The simulation and experimental results of the proposed controllers are compared under various conditions, including speed reversal and load torque variations.

Adaptive Control of a Two-zone Model of Electric Ship Power Electronics Power Distribution System

Power system operations must be robust to unexpected non-ideal conditions. In an attempt to create more robust Power Electronic Power Distribution Systems in ships in the event of changes in the ambient characteristics such as temperature or load changes, a Model Reference Adaptive Control was implemented. Our approach was based on distributed control of the converters in a two-zone model and for the two types of converters present in the system. This work used adaptive law based on instantaneous cost to modify the existing control coefficients in an online mode. The result is that the designed adaptive control can readjust the transient and steady state responses of the converters when they deviate from their original forms in case of any of the unexpected conditions.

An $ L_{\infty} $ performance control for time-delay systems with time-varying delays: delay-independent approach via ellipsoidal $ \mathcal{D} $-invariance

<p>This paper is concerned with a delay-independent output-feedback controller synthesis suppressing the $ L_{\infty} $-gain of linear time-delay systems with time-varying delays. We first proposed a continuous-time version of the existing discrete-time ellipsoidal $ {{\mathcal D}} $-invariant set and established its existence condition in terms of some linear matrix inequalities (LMIs). This existence condition was further extended to characterizing the $ L_{\infty} $-gain of linear time-delay systems with time-varying delays. Because of the delay-independent property of the proposed $ {{\mathcal D}} $-invariant set, the $ L_{\infty} $-gain analysis does not depend on the choice of delays including their magnitudes and time derivatives. Based on this analysis method, we also constructed an output-feedback controller synthesis for ensuring the $ L_{\infty} $-gain of time-delay systems bounded by a performance level $ \rho $. In an equivalent fashion to the $ L_\infty $-gain analysis method, this controller synthesis is independent of the delays in the sense that the obtained controller coefficients do not depend on the delay characteristics. Finally, numerical results were given to demonstrate the effectiveness and validity of the proposed results.</p>

Adaptive Control for Nonlinear Time-Varying Systems With Unknown Control Coefficients and External Disturbances

Adaptive Control for Nonlinear Time-Varying Systems With Unknown Control Coefficients and External Disturbances

Path Planning and Trajectory Tracking for Autonomous Obstacle Avoidance in Automated Guided Vehicles at Automated Terminals

During the operation of automated guided vehicles (AGVs) at automated terminals, the occurrence of conflicts and deadlocks will undoubtedly increase the ineffective waiting time of AGVs, so there is an urgent need for path planning and tracking control schemes for autonomous obstacle avoidance in AGVs. An innovative AGV autonomous obstacle avoidance path planning and trajectory tracking control scheme is proposed, effectively considering static and dynamic obstacles. This involves establishing three potential fields that reflect the influences of obstacles, lane lines, and velocities. These potential fields are incorporated into an optimized model predictive control (MPC) cost function, leveraging artificial potential fields to ensure effective obstacle avoidance. To enhance this system’s capability, a fuzzy logic system is designed to dynamically adjust the weight coefficients of the hybrid artificial potential field model predictive controller, strengthening the autonomous obstacle avoidance capabilities of the AGVs. The tracking control scheme includes a fuzzy linear quadratic regulator based on a fuzzy logic system, a dynamics model as a lateral controller, and a PI controller as a longitudinal tracker to track the pre-set trajectory and speed autonomously. Multi-scenario simulation tests demonstrate the effectiveness and rationality of our autonomous obstacle-avoidance control scheme.

A new algorithm for adjusting PI controller coefficients in shearer operation control

A new algorithm for adjusting PI controller coefficients in shearer operation control

Exploration of cross-border e-commerce and its logistics supply chain innovation and development path for agricultural exports based on deep learning

Abstract This paper studies the cross-border e-commerce of agricultural products and its logistics supply chain collaborative management approach, the overall transaction mode and basic content, and proposes a cross-border e-commerce supply chain conceptual model. Aiming at the problems of agricultural product supply chains, a method for predicting agricultural product export prices is proposed. The Prophet algorithm under deep learning is utilized to construct the Prophet agricultural product price prediction model for trend, cycle, and holiday terms. Over the introduction of RNN algorithms and LSEM algorithms to optimize the prediction performance of the model, as well as the gradient explosion. On this basis, GRU neural networks are proposed as an improved model of RNN-LSTM. Prediction comparison experiments are designed to empirically analyze agricultural export price prediction and supply chain logistics risk control, and the results of the empirical analysis show that the vegetable export price predicted by using Prophet algorithm during the period of date 2013/4-2013/9 is 2.975, which differs from the actual price by 0.009 yuan, and the margin of error is in the interval of [-0.091,0.014], which is the smallest variation among the three algorithms, which shows that Prophet model has the best performance. After optimizing the FAPSC risk control coefficient, the risk value of supply chain logistics and transportation was successfully reduced from 0.364 to 0.296, and FAPSC effectively minimized the risk.

Calculation and Measurement of Dielectric Parameters of Ceramic and Ferroelectric Materials in the Microwave Range

Introduction. Determination of the electrophysical parameters of linear and nonlinear dielectric materials for use in microwave technology represents an important direction. Linear dielectrics are used as a basis for the substrates of microwave circuits, as well as volumetric elements for the construction of frequency selective or resonant structures that operate across a wide temperature range. Therefore, the issue of stabilizing the electrical parameters of such structures from temperature influences appears relevant. A possible solution lies in the use of a multilayer combination of dielectrics, both with linear and nonlinear properties. Due to their nonlinear properties, ferroelectrics find application in functional units with an electrical rearrangement of frequency and phase characteristics. Therefore, it is important to determine not only the relative permittivity of the material, but also the control coefficient in the RF– microwave wavelength ranges.Aim. Construction of computational mathematical models for layered bulk and film structures to determine the relative permittivity of linear and nonlinear dielectrics in the ultrahigh frequency range.Materials and methods. The construction of computational mathematical models for the analysis of complex layered structures was carried out on the basis of Maxwell’s equations and the Galerkin method using boundary conditions for electromagnetic field components.Results. An electrodynamic analysis of a two-layer volumetric disk resonator was performed, and numerical results of calculating the resonant frequency were obtained. A numerical analysis of a multielectrode half-wave resonator with a ferroelectric film (FEF) was carried out.Conclusion. The mathematical models created and the experiment performed made it possible to numerically evaluate the properties of linear and nonlinear dielectric bulk and film materials in the microwave range.

Global adaptive neural network control of nonlinear time-varying systems with unknown control coefficients and model uncertainties

This paper investigates the adaptive control problem for the strict-feedback nonlinear time-varying system (NTVS) with unknown control coefficients and model uncertainties. To address the problem that the neural network (NN) can only achieve local approximation of unknown nonlinear functions, a novel NN control scheme is proposed. The scheme consists of a NN controller that operates inside the approximation domain and a robust controller that operates outside the approximation domain, and a novel switching function is designed to enable the two controllers to switch smoothly in the vicinity of the approximation domain to ensure that all signals of the closed-loop system are globally uniformly ultimately bounded. To address the problem that unknown time-varying control coefficients are difficult to handle, an adaptive fault-tolerant control scheme is proposed in combination with the congelation of variables method. The scheme employs classical adaptive control to adapt to the variation of unknown control coefficients and robust control to eliminate the time-varying disturbance terms, and introduces a positive integrable time-varying function to achieve asymptotic tracking of an arbitrary reference signal. The combination of these two schemes constitutes a global adaptive neural network control (GANNC) method for the NTVS with unknown control coefficients and model uncertainties. Finally, an adaptive attitude fault-tolerant controller for launch vehicles is designed by using the GANNC method, which can realize the accurate tracking of the attitude control system to the reference command under normal status, strong disturbances and actuator failures, and comparative experiments prove the effectiveness and superiority of the controller.

Connectivity‐preserving consensus: An adaptive event‐triggered strategy

SummaryThe connectivity of communication graph is indispensable for consensus of multi‐agent systems (MASs), which in many applications (e.g., wireless sensor) depends on the relative distances between agents. But, in the adaptive setting particularly with system nonlinearities and event‐triggered communication, it is rather difficult to enforce the relative distances within the limited range for the connectivity. This paper focuses on developing an adaptive event‐triggered control strategy with connectivity preservation in the context of nonidentical unknown control coefficients and heterogeneous nonlinearities coupling with parameter uncertainties. First, a group of potential functions are introduced acting as control barrier functions to constrain the relative distances between agents within the limited range for all time. Also, two dynamic gains are specialized for each agent to handle the system uncertainties, system nonlinearities and negative effect of the execution error. Then, an adaptive event‐triggered protocol is designed for each agent such that the connectivity‐preserving consensus of MASs is achieved and Zeno behavior is excluded. Moreover, an extended study is conducted on a leader‐following scenario. Two simulation examples illustrate the effectiveness of the proposed event‐triggered control strategy.

Metaheuristic algorithm-based cascade PID controller design for fixed wing unmanned aerial vehicle

In this study, the nonlinear model of the longitudinal motion and altitude of a fixed-wing unmanned aerial vehicle with assured geometrical features and aerodynamic parameters is linearized under certain conditions. A cascade Proportional Integral Differential Controller is designed on the linear model. The controller coefficients that applied to the model of the UAV were optimized with an artificial intelligence technique, which is based on a metaheuristic search algorithm. The four different controller gains in the system are optimized using four different objective functions. Controller performances were tested in simulation environment for unit step input responses., Considering the longitudinal dynamics of the aircraft, among the ITAE, ISE, MSE, and IAE fitness functions, IAE can be shown as the optimum result for altitude control.

On linear stabilization of a class of nonlinear systems in a critical case

In this paper, we address the stabilization problem for nonlinear systems in a critical case. Namely, we study the class of canonical nonlinear systems. Canonical nonlinear systems or chain of power integrators is an important subject of research. Studying such systems is complicated by the fact that they cannot be mapped onto linear systems. Moreover, they have the uncontrollable first approximation. Previous results on smooth stabilization of such systems were obtained under the assumption that the powers in the right-hand side are strictly decreasing. In this work, we consider a case of non-increasing powers in the right-hand side for a three-dimensional system. A popular approach for studying such systems is the backstepping method, which is a method of step-wise stabilization. This method requires a sequential investigation of lower-dimensional subsystems. Backstepping enables the study of a wide range of nonlinear triangular systems but requires technically complex and cumbersome computations. Therefore, a natural question arises about constructing stabilizing controls of a simple form. Polynomial controls can serve as an example of such controls. In the paper, we demonstrate that linear controls can be considered as stabilizing controls. We derive sufficient conditions for the coefficients of the linear control that ensure the asymptotic stability of the zero equilibrium point of the corresponding closed-loop system. The asymptotic stability is proven using the Lyapunov function method, which is found as the sum of squares. The negative definiteness of the Lyapunov function derivative in a neighborhood of the origin guarantees asymptotic stability. In contrast to the case of strictly decreasing powers, additional conditions on the control coefficients, apart from their negativity, emerge. The obtained result extends to a broader class of nonlinear systems through stabilization by nonlinear approximation. This allows the consideration of systems with higher-order terms in the right-hand side. The effectiveness of the applied approach is illustrated by several model examples. The method used in this work to investigate the case of non-increasing powers can be applied to systems of higher dimensions.

Performance Analysis of Meta-Heuristic Algorithms for Optimal PI Tuning of PFC

The popularity of the average current mode (ACM) controlled boost type power factor correction (PFC) topologies is increasing due to their suitability for power quality problems. Proportional-Integral (PI) controller method is generally used in ACM controlled boost PFC circuits which include two controllers. However, the most important complexity of ACM controlled PFC circuits is tuning the coefficients of the PI controllers optimally. Therefore, this paper proposes the optimal tuning of PI coefficients used in ACM controlled boost PFC circuits using different meta-heuristics algorithms. First, the proposed ACM controller-based boost PFC topology is analyzed in MATLAB/Simulink software by using variable loads. Then the simulation results of the Cuckoo Optimization Algorithm (COA) based ACM controlled boost PFC converter are compared with the results determined via Ziegler-Nichols (ZN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Imperialistic Competitive Algorithm (ICA), and Invasive Weed Optimization (IWO). Finally, the experimental verification of the topology has been done using a 600 W prototype and eZdsp F28335. As COA showed better results among other evolutionary algorithms used in this paper, we used COA parameters to observe the performance of the proposed tuning method. The experimental studies have been done under different load variations similar to the simulation studies.