Adaptive Intelligent Secondary Control of Microgrids Using a Biologically-Inspired Reinforcement Learning

Control algorithms are the most critical aspects in the successful control of civil structures subjected to earthquake and wind forces. In recent years, adaptive intelligent control algorithms are emerging as an acceptable substitute method to conventional model-based control algorithms. These algorithms mainly work on the principles of artificial intelligence (AI) and soft computing (SC) methods that make them highly efficient in controlling highly nonlinear, time-varying, and time-delayed complex civil structures. The current research probes to control algorithms, that this article set forth an inclusive state-of-the-art review of adaptive intelligent control (AIC) algorithms for vibration control of smart civil structures. First, a general introduction to adaptive intelligent control is presented along with its advantages over conventional control algorithms. Second, their classification concerning artificial intelligence and soft computing methods is provided that mainly consists of artificial neural network-based controller, brain emotional learning-based intelligent controller, replicator dynamics-based controller, multi-agent system-based controller, support vector machine-based controller, fuzzy logic control, adaptive neuro-fuzzy inference system-based controller, adaptive filters-base controller, and meta-heuristic algorithms-based hybrid controllers. Third, a brief review of these algorithms with their developments on the theory and applications is provided. Fourth, we demonstrate a summarized overview of the cited literature with a brief trend analysis is presented. Finally, this study presents an overview of these innovative AIC methods that can demonstrate future directions. The contribution of this article is the anticipation of detailed and in-depth discussion into the perspective of AI and SC-based AIC method advances that enabled practical applications in attenuating vibration response of smart civil structures. Moreover, the review demonstrates the computing advantages of AIC over conventional controllers that are important in creating the next generation of smart civil structures.

Adaptive intelligent speed control of switched reluctance motors with torque ripple reduction

Based on the European Structural Funds it was developed the Intelligent Mobile Box, Intelligent Panel Controller with intelligent adaptive controllers within the industrial research and experimental development in the company Kybernetes, s.r.o. Within the frame of the academic-industry cooperation, the intelligent adaptive controller was tested at the Department of Cybernetics and Artificial Intelligence, Technical university of Kosice, Slovakia. The tests of the mobile intelligent adaptive controller were performed on two levels of university study, on the Bachelor level on the exercises from the subject “Control of Technological Processes” and on the Engineering level the exercises from the subject “Intelligent Control Networks” and on one Diploma project. Goals of students of the Control of the technological processes course had two goals, firstly to connect the intelligent adaptive controller to pre-defined controlled system (real plant, real model or simulated model) and next to validate the control results. Students of the Diploma project on the Engineering level had more advanced goals. Tasks defined for engineering students were to connect the intelligent adaptive controller to non-defined controlled system, setup the adaptivity process of the controller regarding the learning error, parameterize the control system, observe and validate the control results. Both sides concluded this cooperation as very valuable. Main contributions for students were (U1) the challenge to apply studied theoretical knowledge on the real industrial controllers, (U2) experience with new research results and technologies deployed in industry and (U3) the implementation of the control and adaptive algorithms from abstract mathematical area to real PLC controller. On side of industry research company the main contributions were (C1) testing of designed algorithms and (C2) user feedback from students to make the application HMI interface more understandable a native.

PreCrash problem of Intelligent Control of autonomous vehicles robot is a very complex problem, especially vehicle pre-crash scenariws and at points of intersections in real-time environmenta. This Paper presents a novel architecture of Intelligent adaptive control for autonomous vehicle agent that depends on Artificial Intelligence Techniques that applies case-based reasoning techniques, where Parallel CBR Engines are implemented for different scenarios' of PreCrash problem and sub-problems of intersection safety and collision avoidance, in the higher level of the controller and A∗ path planner for path planning and at lower-levels it also uses some features of autonomous vehicle dynamics. Moreover, the planner is enhanced by combination of Case-Based Planner. All modules are presented and discussed. Experimental results are conducted in the framework of Webots autonomous vehicle tool and overall results are good for the CBR Engine for Adaptive control and also for the hybrid Case-Based Planner, A∗ and D∗ motion planner along with conclusion and future work.

Conventional PID controller parameter tuning method needs the precise mathematical model of the controlled object, while the fuzzy control and neural network have strong self-adaptive and self-learning ability. The genetic algorithm is a new global optimization method so they can be used to design an adaptive PID intelligent controller based on fuzzy neural network and GA. First, GA is adopted to optimize the central value and width of the membership function. Then, we use BP to optimize the connection weight coefficient of fuzzy neural network to achieve adaptive and intelligent control of PID. The simulations indicate such scheme improve the adaptive ability and anti-interference ability of the system, which also enhances the robustness of system.

An intelligent adaptive control scheme based on the disturbance compensation was proposed to solve the stability augmentation control problem of the large under the influence of strong interferences. First, considering the influence of strong wind, the coupling multivariable disturbance observer is designed for the control-oriented model to estimate the synthetical uncertainties and disturbances rapidly and accurately and compensate the close-loop controller. And on the basis of the observer, a novel finite-time robust adaptive controller which is the combination of fast super-twisting algorithm and action dependent heuristic dynamic programming is proposed. The control scheme can obtain good control performance that the large airplane can track the control command accurately and steadily in finite time. Finally, to demonstrate the effectiveness of the proposed intelligent adaptive control scheme, the simulation verification of Boeing 747 is carried out. And the simulation results verify the good control performance in attitude stable and rapid maneuvering of the large aircraft.

This paper presents an intelligent adaptive steering control using linear quadratic regulation (LQR) approach and fuzzy cerebella model articulation control (CMAC) method for an electrical unicycle. The fuzzy CMAC is employed to on-line learn unknown frictions between the wheel and the terrain surfaces. The LQR approach is used to design a state feedback controller, in order to simultaneously achieve self-balancing and velocity control for the unicycle with different riders. The performance and merit of the proposed method are well exemplified by conducting simulations on a laboratory-built electric unicycle.

Design of an intelligent optimal neural network-based tracking controller for nonholonomic mobile robot systems

This chapter presents an architecture for precise tracking of autonomous robots based on a novel intelligent adaptive control approach. The scheme evolved from a supervisory loop concept([9]-[12],[18]) consisting of two control algorithms that work in an autonomous manner for the precision control of such nonlinear dynamic systems. The first algorithm is a neuro-controller that monitors the system’s closed loop dynamics while functional changes occur, based on supervised motor learning. The second one is an adaptive controller which controls the system dynamics when parametric changes occur based on a Model Reference Adpative Control (MRAC) strategy. First, the chapter discusses theoretical formulation and design considerations. The theoretical formulations are also analyzed for closed loop stability based on a Lyapunov function candidate. Then, results are presented for simulation and real-time implementation of the position tracking conducted on a single link flexible manipulator nonlinear model and a mobile wheeled robot, respectively. Finally, the chapter concludes by illustrating how this concept can be expanded in the presence of multiple-modes and emergent behaviors.KeywordsRadial Basis Function Neural NetworkAdaptive ControllerLoad TorqueUnmodeled DynamicIntelligent ControllerThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Intelligent control, inspired by biological and AI (artificial intelligence) principles, has increased the understanding of controlling complex processes without precise mathematical model of the controlled process. Through customized applications, intelligent control has demonstrated that it is a step in the right direction. However, intelligent control has yet to provide a complete solution to the problem of integrated manufacturing systems via intelligent reconfiguration of the robotics systems. The aim of this paper is to present an intelligent control architecture and design methodology based on biological principles that govern self-organization of autonomous agents. Two key structural elements of the proposed control architecture have been tested individually on key pilot applications and shown promising results. The proposed intelligent control design is inspired by observed individual and collective biological behavior in colonies of living organisms that are capable of self-organization into groups of specialized individuals capable of collectively achieving a set of prescribed or emerging objectives. The nervous and brain system in the proposed control architecture is based on reinforcement learning principles and conditioning and modeled using adaptive neurocontrollers. Mathematical control theory (e.g. optimal control, adaptive control, and neurocontrol) is used to coordinate the interactions of multiple robotics agents.

In large modern telecommunication networks, the amount of traffic and the number of nodes and links are so large that the traditional network control may not be effective due to high complexity. These networks need adaptive and intelligent systems in order to provide high network reliability, accurate traffic prediction, efficient use of channel bandwidth, and optimized network management in relation to various, dynamically changing environments. Neural networks can contribute to this emerging new telecommunication infrastructure by providing fast, flexible, adaptive, and intelligent control that cannot be performed sufficiently well by digital computers. In this paper, we discuss the neural network approaches for solving various control problems in high-speed communication networks, and present our proposed neural network model for the optimized control of input queues in ATM switches.

In the context of rapidly advancing network technologies and the increasing demand for high-performance applications such as cloud gaming, IoT, and streaming, traditional single-path transmission protocols can no longer satisfy the growing requirements for bandwidth and reliability. Multi-path transmission control aggregates the bandwidth of multiple paths and enables path migration in case of failures or transient fluctuations, which enhances both throughput and robustness. However, existing multi-path schedulers face challenges in adapting to dynamic network conditions and balancing throughput with head-of-line blocking. In this article, we present an Adaptive Intelligent Multi-path Transmission Control Scheduler (AIMS) that leverages Deep Reinforcement Learning (DRL) to optimize packet scheduling. AIMS models the multi-path scheduling problem as a Markov Decision Process (MDP) and employs an Advantage Actor-Critic (A2C) framework to dynamically learn the optimal path selection strategy, balancing throughput, latency, and reliability. AIMS integrates trend-awareness and adaptive reward mechanisms to effectively address network congestion and heterogeneity, while fairness constraints prevent the path starvation problem. Experimental results demonstrate that AIMS consistently outperforms existing multi-path schedulers in terms of throughput, flow completion time, and robustness.

We describe a general method for adaptive model-based control of nonlinear dynamic systems using neural networks, fuzzy logic and fractal theory. The new neuro-fuzzy-fractal method combines soft computing techniques with the concept of the fractal dimension for the domain of nonlinear dynamic system control. The new method for adaptive model-based control has been implemented as a computer program to show that our neuro-fuzzy-fractal approach is a good alternative for controlling nonlinear dynamic systems. We illustrate our new methodology with the case of controlling aircraft dynamic systems. For this case, we use mathematical models for the simulation of aircraft dynamics during flight. The goal of constructing these models is to capture the dynamics of the aircraft, so as to have a way of controlling this dynamics to avoid dangerous behavior of the system.

Semi-active vibration control of building structure by Self Tuned Brain Emotional Learning Based Intelligent Controller

Implementation of Brain Emotional Learning-Based Intelligent Controller for Flocking of Multi-Agent Systems