Intelligent Adaptive Control Research Articles

Nonlinear Adaptive Neural Control of Power Converter‐Driven DC Motor System: Design and Experimental Validation

ABSTRACTThis article presents an intelligent adaptive neural control scheme to track the output speed trajectory in power converter‐driven DC motor system. The proposed technique integrates an adaptive polynomial‐neural network with a backstepping strategy to yield a robust control system for output tracking in DC motor. Such a unification of online neural network‐based estimation and adaptive control, results in effective regulation of the output across a wide load torque uncertainties, besides yielding a promising transient and steady‐state performance. The stability of the entire closed‐loop system is ensured through Lyapunov stability criterion. The efficacy of the proposed strategy is revealed through an extensive experimental investigation under various operating points during start‐up, step‐reference tracking, and external step‐load torque disturbances. The real‐time experimentation is conducted on a laboratory prototype of power converter‐driven DC motor of 200 W, using dspace DS1104 control board with MPC8240 processor. The results obtained confirm an improvement in the transient response of the output speed by significantly reducing the settling time to and yielding a steady state behavior with no peak over/undershoots during load disturbances, in contrast to other similar works presented in the literature intended for same the application.

Dynamic Surface Tracking Control for Nonlinear Constraints

Abstract An adaptive control scheme based on dynamic surface control (DSC), and intelligent control techniques is developed for a 3-DOF helicopter with unknown disturbance and asymmetric saturation. The reconstructed dynamic neural network (NN) is established to compensate for nonlinear terms in real-time. A designed smooth nonlinear sigmoid function is used to model saturation nonlinearity. The combination of dynamic surface control with adaptive intelligent control techniques resolves and improves the tracking and robust performance of the system. The stability and ultimately bound of all signals of the controlled system are demonstrated by theoretical analysis. Numerical results prove the feasibility of the presented control scheme.

A novel system architecture of intelligent adaptive cruise control for safety aspects

Following industrial and safety standards for autonomous vehicles, Adaptive Cruise Control (ACC) is a widely employed Advanced Driving Assistance System (ADAS) feature in modern vehicles. ACC currently facilitates speed control based on the driver's desired speed value. This study introduces a significant advancement: the Intelligent Adaptive Cruise Control (IACC) feature, accompanied by the development of a control system architecture poised to make noteworthy contributions in scientific, economic, and social dimensions through its integration into autonomous vehicles. The design incorporates crucial elements such as Traffic Sign and Limit Recognition (TSLR), ADAS features, and Global Positioning System (GPS) data, primarily enhancing driver safety through these supportive features. The main focus revolves around designing a system architecture that accommodates these new features to ensure safe driving. The creation of the IACC system architecture is approached using Model-Based System Engineering (MBSE). Through this MBSE methodology, system-level diagrams were crafted, and security considerations were systematically addressed. Several scenarios were devised to evaluate the contributions and were subsequently tested and analyzed. The architecture places particular emphasis on the security aspects of IACC. Leveraging the TSLR feature, the system interprets traffic signs and acquires speed limit data from external sources, preventing the vehicle's speed from exceeding the specified limit. The comparison between the set speed value and the speed limit ensures adherence to safety parameters. In such scenarios, the system enhances driver support on winding roads by utilizing GPS data to recognize the vehicle in front. This approach significantly elevates the reliability of the IACC feature, particularly in terms of safety sensitivity, when compared to other adaptive cruise control concepts.

Analysis of existing control systems for the technological regime of the oil refining process

The control system is a critically important component in every aspect of modern life, especially in oil refining processes. It ensures continuous monitoring and optimization of technological operations such as purification, distillation, and oil heating. Improved performance of such systems leads to increased overall efficiency, which in the long term results in significant savings in resources and energy, as well as enhanced quality of petroleum products. Numerous control systems have been specifically developed for this purpose over the past few decades in response to the ongoing need for improved performance. With this in mind, this article aims to classify and analyze controllers of various types. The transition from conventional to more intelligent adaptive controllers is highlighted along with the relevant principles. Conventional regulators are those designed for linear systems without considering potential parametric changes. Adaptive controllers, on the other hand, have the ability to automatically learn and adapt to the plant’s conditions, ensuring more precise system behavior.

Lithology identification of coal-bearing strata based on data-driven dual-channel relevance networks in coal mine roadway drilling process

The accurate prediction of coal seam thickness and the identification of the lithology of coal-bearing strata represent a crucial aspect of the intelligent perception of the stratigraphic environment. These technologies represent a fundamental aspect of intelligent mining operations with respect to coal. Accordingly, this paper concentrates on the investigation of lithology identification in coal-bearing strata through the utilization of data-driven dual-channel relevance networks in the context of coal mine roadway drilling operations. In consideration of the impact of coal seam thickness fluctuations on lithology identification, the region in question is initially delineated into two distinct categories: those comprising relatively stable coal seams and those comprising unstable coal seams. This is achieved through the application of coal seam thickness prediction and stability evaluation techniques. Subsequently, in consideration of the varying challenges posed by the region's coal seams, which can be either relatively stable or unstable, distinct lithology identification schemes are employed in different regions. In regions where the coal seam is relatively stable, there is a significant demand for classification of soft and hard coal seams. Additionally, the non-coal seam samples belong to a limited number of classes, necessitating the use of the SMOTE-GBDT algorithm to classify the coal-bearing strata into soft coal seam, hard coal seam, and non-coal seam. In the case of an unstable coal seam, the ability to distinguish between different seams is of paramount importance for lithology identification purposes. To this end, a double-layer binary classification RVM approach is employed to categorize coal-bearing strata into weak interlayer, coal seam and mudstone layer. The proposed methodology entails the utilization of diverse techniques in accordance with the specific characteristics of varying coal seam stability regions, with the objective of meticulously delineating the targets of lithology identification. This approach facilitates the generation of a comprehensive understanding of the coal-bearing strata environment, which can subsequently inform the implementation of intelligent adaptive control strategies.

Geological adaptive intelligent control of earth pressure balance shield machine based on deep reinforcement learning

Scientific and precise control of tunnelling parameters is of utmost importance during the construction of shield machines. Given the complexity of the working environment, manual operation is highly prone to causing safety accidents. Therefore, achieving intelligent control of the shield machine is crucial. Based on this, this paper proposes a geological adaptive intelligent control method of earth pressure balance shield machine using the Deep Deterministic Policy Gradient (DDPG) algorithm as the framework, with Actor-Critic as the basis. Firstly, DDPG agent is constructed to replace the screw conveyor control system as the main body of strategy implementation. Secondly, an environmental model is established by utilizing the mechanism model between the sealed cabin pressure and the screw conveyor speed. The real-time sealed cabin pressure, target pressure, and pressure error serve as the state space, while the screw conveyor speed is used as the action space. A combined reward function is set based on safety and accuracy. Finally, the Actor network interacts with the environment under the supervision of the reward function and Critic network. Successful training is achieved when the cumulative reward value is maximized, resulting in the output of optimal control strategy. In this paper, the method dynamically regulates the screw conveyor speed by interacting with the geological environment, to realize the precise control of the sealed cabin pressure and ensure the dynamic balance between sealed cabin pressure and excavation face pressure. The test results show that this method has a good control effect on the sealed cabin pressure under various geological conditions, and can complete 72 kinds of soil transition tasks. It has strong soil adaptability and can respond well to the dynamic changes of soil conditions. This approach enhances the intelligence of the shield machine, mitigating inaccuracies attributed to human operation, which provides a guarantee of safe shield machine operation, whilst exhibiting valuable engineering applications.

Composite Learning Adaptive Intelligent Self-Triggered Fault-Tolerant Control With Improved Performance Assurance for Autonomous Surface Vehicle

Composite Learning Adaptive Intelligent Self-Triggered Fault-Tolerant Control With Improved Performance Assurance for Autonomous Surface Vehicle

Flexible Constraints-Based Adaptive Intelligent Event-Triggered Control for Slowly Switched Nonlinear Systems Using Reinforcement Learning

Flexible Constraints-Based Adaptive Intelligent Event-Triggered Control for Slowly Switched Nonlinear Systems Using Reinforcement Learning

Adaptive Intelligent Output Feedback Consensus Control for Nonlinear Multiagent Systems With Switched Topologies and Dead-Zone

Adaptive Intelligent Output Feedback Consensus Control for Nonlinear Multiagent Systems With Switched Topologies and Dead-Zone

Disturbance Observer-Based Adaptive Intelligent Control of Marine Vessel With Position and Heading Constraint Condition Related to Desired Output.

This article studies the adaptive control about the geodetic fixed positions and heading of three-degree-of-freedom dual-propeller vessel. During the navigation of a vessel at sea, due to the unpredictable sea, on the one hand, it is important to ensure that the vessel can smoothly follow the desired geodesic fixed position and heading; on the other hand, when the sailing environment is harsh, it is even more important that the vessel can adapt to the desired geodesic fixed position and heading that change at any time for safe driving. Therefore, this article selects the time-varying function related to the desired geodesic fixed position and heading as the constraint condition, and the constraint condition will change in real time as the expected position and heading change. The design of the control strategy is difficult, and the designed control strategy will be more suitable for complex maritime navigation conditions. First, the article constructs a log-type barrier Lyapunov function. Second, by introducing an unknown external disturbance observer, the external disturbances caused by the environment that may be encountered during the vessel's voyage can be observed. Then, combined with the backstepping algorithm, a neural network (NN) control strategy and adaptive law are designed. Among them, for the uncertain function in the process of designing the control strategy, the NN is used to approximate it. Furthermore, through the Lyapunov stability analysis, it is shown that applying the designed control strategy to the vessel system in this article can ensure that the system is closed-loop stable. The final simulation experiment shows the effectiveness of the designed control strategy.

Reinforcement learning-based adaptive tracking control for mismatched non-affine nonlinear systems with coupled uncertainties

This paper studies a reinforcement learning-based adaptive non-affine tracking control method for a class of uncertain mismatched non-affine nonlinear systems. The considered system is not only affected by external mismatched disturbances and internal uncertainties, but also influenced by the non-affine control structures. Firstly, an auxiliary integral system is developed for the purpose of isolating the non-affine control input. Secondly, by designing the actor-critic networks to evaluate the system control performance and generate the reinforcement signal, the unknown internal uncertainties can be handled. Thirdly, based on the output of reinforcement learning network, several disturbance compensation laws are constructed to address the adverse impact of matched and mismatched disturbances. As a result, a novel intelligent adaptive non-affine controller is proposed by integrating actor-critic reinforcement learning framework, disturbance compensation and adaptive laws. It has been proved that closed-loop system are stable and the tracking errors are bounded. The numerical simulation results show the effectiveness and superiority of the proposed method.

Prescribed-Time Adaptive Intelligent Formation Controller for Nonlinear Multiagent Systems Based on Time-Domain Mapping

Prescribed-Time Adaptive Intelligent Formation Controller for Nonlinear Multiagent Systems Based on Time-Domain Mapping

Adaptive intelligent formation control for multiple underactuated surface vessels with prescribed performance

To control a leader-follower formation in underactuated surface vessels (USVs) with model uncertainty and external disturbances, a control strategy combining a fuzzy wavelet cerebellar model articulation controller (FWCMAC) and bidirectional fuzzy wavelet brain emotional learning controller (BFWBELC) is proposed in this paper to maintain connections between the USVs. Combined with the barrier Lyapunov function performance constraint method, a performance function is introduced as the boundary function of the formation error to ensure the transient and steady-state performance of the formation control system. FWCMAC is taken as the formation guidance controller, and the online learning law is derived using the gradient descent method. The BFWBELC is considered as the main controller, and the online learning rules are constructed by combining the bidirectional reward mechanism. The wavelet function is introduced to improve the processing ability of the controller for the stimulus signal. In addition, an adaptive compensator is proposed to handle the residual error of the adaptive intelligence controller with respect to the ideal controller. Based on Lyapunov stability theory, it is proven that all signals in the formation control system are ultimately bounded. The numerical simulation results demonstrate the effectiveness of the proposed formation control system.

Adaptive plc intelligent control based on the integrated system of smart grid thermal energy conversion

During the construction of smart grids, due to technological limitations, traditional sequential control systems have been unable to meet the needs of grid operation, exposing problems such as single control methods and functions, low operational efficiency, inability to independently complete complex tasks, and poor anti-interference ability, which in turn affect the power supply quality and operational stability of the grid, and fail to truly achieve the reliability, safety, and reliability of smart grids Economic and efficient goals. For this purpose, the author describes an adaptive intelligent control method for an integrated biomass energy heat conversion system consisting of more than one suction biomass gasifier, biomass gas burner, and steam boiler. The system uses PLC as a tool and takes the boiler outlet steam pressure as the control parameter. Based on the changes in the boiler outlet steam pressure, it automatically adjusts the feed and gas production of the gasifier, and subsequently adjusts the air supply of the burner. Ultimately, it achieves the goal of automatically adjusting the boiler heating volume with changes in steam pressure. The integrated heating and adaptive intelligent control system based on biomass gasification has been reliably verified in industrial applications.

Adaptive Intelligent Control for Nonlinear Stochastic Cyber-Physical Systems With Unknown Deception Attacks: Switching Event-Triggered Scheme

Adaptive Intelligent Control for Nonlinear Stochastic Cyber-Physical Systems With Unknown Deception Attacks: Switching Event-Triggered Scheme

A cascade fuzzy adaptive based interaction torque control of a pneumatically actuated forearm rehabilitation robot under disturbance effects

In this study, an intelligent adaptive interaction torque control for a pneumatically actuated forearm rehabilitation robot has been proposed. The main objective is to provide a haptic environment that ensures stable interaction torque fields at changing levels. To achieve this goal, a cascade fuzzy adaptive controller, that is specifically tailored to handle varying levels of interaction torque and ensure stability throughout the rehabilitation process, has been designed. To improve the efficiency of the controller, non-linear friction torque identification of the pneumatic actuator based on changing operating conditions has been conducted. Parallel to this, a user motion intention detection algorithm has been designed to provide compliant, safe and suitable human-robot interactions. The disturbance cases have been considered to make the system robust to unknown conditions. Stability analysis has been performed, specifically focusing on the boundary-input boundary-output (BIBO) stability conditions. In order to demonstrate the superior performance of the proposed cascade fuzzy adaptive algorithm, a cascade PID algorithm has also been meticulously designed for comparison. Numerous experimental validation tests involving a healthy user were conducted in a Hardware-in-the-Loop environment, focusing on torque trajectory tracking performance. The proposed control technique exhibited improved convergence dynamics compared to the cascade PID algorithm, yielding mean absolute error levels of 0.0218 Nm and 0.099 Nm for target interaction torque under disturbance-free and disturbed conditions, respectively.

Frequency Regulation of Microgrid with Renewable Sources Using Intelligent Adaptive Virtual Inertia Control Approach

Frequency control of small inertia microgrids (MGs) with amalgamation of renewable energies like wind and solar is a difficult job. Virtual inertia control (VIC) scheme is the concept of enhancing the MGs inertia employing storage elements. In the present study, an optimized fuzzy adaptive virtual inertia control strategy has been proposed for adaptive allocation of virtual inertia constant. To tune the suggested controllers, an improved golden jackal optimization (i-GJO) is projected in which priority is given to the better jackal’s during the search process, and also sine cosine adopted scaling factor is used to modify the jackal’s position throughout search process. The effectiveness of i-GJO method is evaluated by comparing the results with golden jackal optimization (GJO) and other similar recently proposed optimization algorithms. Then, proportional-integral-derivative controllers are tuned in VIC environment with fixed virtual inertia constant (KVT ) using proposed i-GJO technique, and the superiority of i-GJO over genetic algorithm, particle swarm optimization, and GJO is demonstrated. Finally, fuzzy based controller with constant KVT as well as fuzzy adaptive KVT has been designed using the proposed i-GJO technique. The sensitivity of the controller is also examined under irregular load changes and various rates of RES amalgamation.

A novel ANFIS-controlled customized UPQC device for power quality enhancement

Power quality is crucial for the reliable and efficient operation of domestic and industrial loads. Nonlinear power electronic converter loads have increased in usage, which has led to a decline in both voltage and current quality. To overcome these power quality issues, a universal power quality compensator (UPQC) has been developed and integrated into the power distribution network. The UPQC comprises dual active power filters which are equipped with a DC-link capacitor. The voltage of the DC-link capacitor is controlled by a DC-link voltage controller. However, the conventional PI controller is not suitable for regulating voltage at a defined level due to inappropriate gain values. In this work, we propose an intelligent adaptive neuro-fuzzy inference system-based DC voltage controller for customized UPQC devices. The proposed control scheme aims to mitigate the existing power quality challenges by reducing the issues in classical controllers and incorporating intelligence knowledge with subjective decisions. Using the MATLAB/Simulink software tool, we have tested a customized UPQC device controlled by ANFIS for critical operation and performance. The simulation results are presented along with valid comparisons.

Neuro-Fuzzy-Based Adaptive Control for Autonomous Drone Flight

Adaptive control is the capability of a control system to modify its operation and achieve the best possible operation mode. A quadcopter is a nonlinear, unstable and under-actuated dynamic system, thus providing a challenge to control engineers in controlling and stabilising it during flight. This paper proposes the design, development, and application of an intelligent adaptive hybrid controller to control and stabilise the drone. The training data for adaptive neuro-fuzzy inference systems (ANFIS) are generated by the Linear Quadratic Regulator (LQR) under white-noise disturbance. The trained ANFIS is subsequently used to estimate the parameters of the control distribution matrix for the actual fault condition and the reconfiguration is carried out by computing new feedback gain using the pseudo-inverse technique. For the simple adaptive controller, LQR is also used to generate the desired trajectories of the reference model. In both experiments, the extended Kalman filter is implemented due to its non-linearity benefit. We demonstrate the performance of the proposed approach as a representative case study. The preliminary numerical simulation results further indicate that the proposed method is promising compared to conventional control techniques to control and stabilise a quadcopter drone.

Temperature control in electric furnaces: Methods, applications, and challenges

Temperature control of electric heating furnaces, a common piece of equipment in industrial production, is essential for assuring product quality and enhancing production effectiveness. In order to better understand the similarities and differences in temperature management between various types of electric heating furnaces, this paper first discusses the fundamental concepts and classifications of electric heating furnaces. Following that, it systematically describes the applications of the various temperature control techniques now used for electric heating furnaces, such as PID control, fuzzy logic control, genetic algorithm control, and model predictive control. This paper further analyzes the difficulties in controlling the temperature of electric heating furnaces and identifies potential future development trends in light of the issues that currently used control methods must deal with in practical applications, such as insufficient control accuracy, slow response speed, and poor stability. Finally, it summarizes the information in the entire paper and considers the direction of temperature control research for electric heating furnaces. This research will focus on new control theories and algorithms, furnace body design optimization, control system integration, and intelligent adaptive temperature control. In order to advance the development and advancement of technology in this area, the goal of this article is to provide a thorough theoretical reference and practical advice for the temperature management of electric heating furnaces.