Research developments in adaptive intelligent vibration control of smart civil structures

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

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.

A new active control performance index for vibration control of three-dimensional structures

In modern industries, to improve energy efficiency and performance, adaptive control applications are growing step by step owing to their ability to cope with the effect of nonlinearities and time-varying phenomena of the dynamic system in a stochastic environment. Electro-hydraulic actuators (EHA) are such a kind of dynamic system which can be easily affected by uncertainties. However, EHAs have a wide range of applications ranging from teleoperated excavation to precise numerical control. Meanwhile, various uncertain phenomena can easily influence the overall control performance. This paper presents a psycho-biological motivated adaptive control featured by an emotional learning mechanism: a brain emotional learning-based intelligent controller (BELBIC) to control the trajectory of the electrohydraulic actuator. The control signal generated by this computationally efficient controller consists of an adaptive control signal to compensate for the uncertainties and handle the nonlinearities. It tries to keep the trajectory error as minimum as possible, corresponding to a random disturbance. Experimental work was carried out to evaluate the performance compared to conventional PID and fuzzy-PID controllers. The results confirmed the effectiveness of the proposed BELBIC controller over the conventional PID and fuzzy PID controller. The proposed adaptive BELBIC controller provides an excellent response for a 0.5 Hz sinusoidal input signal where the PID and fuzzy PID show insufficient responses.

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.

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

The increasing complexity of modern plant systems and the limitations of precise mathematical modeling have led to a shift towards data-driven control methods. These methods present an effective alternative that treats plants as black-box systems without requiring explicit models. The safe experimentation dynamics algorithm (SEDA) is one such method that optimizes controller parameters using data-driven techniques. Nonetheless, control performance can be limited by the fixed probability coefficient used in the original SEDA to disrupt design parameters, especially when balancing exploration and exploitation phases. This study proposes an enhanced version of the SEDA: the modified SEDA (MSEDA), to address this issue. The MSEDA introduces a dynamic probability coefficient that decreases with each iteration. The adjustment improves the balance between exploration and exploitation phases, which enhances control accuracy. The MSEDA was used to tune the brain emotional learning-based intelligent controller (BELBIC) together with a proportional-integral-derivative (PID) controller. The result was the BELBIC-PID controller inspired by the limbic system of the human brain, which has high precision. The effectiveness of the proposed MSEDA-BELBIC-PID was validated using simulations on multi-input multi-output (MIMO) systems, with a focus on tracking performance and computational efficiency. The statistical analysis of 30 independent trials demonstrated that the proposed MSEDA-BELBIC-PID was significantly improved over the original SEDA-BELBIC-PID. Wilcoxon's rank test yielded a fitness function p-value < 0.05, which confirmed the robustness and effect of the proposed enhancement. The comparative results demonstrated that the MSEDA-BELBIC-PID consistently performed better than the original approach and had improved fitness function values, reduced total integral square error, and lower total integral square input. These findings underscored the MSEDA suitability as a data-driven tool for controller design parameter optimization. Furthermore, the low computational burden of MSEDA rendered it a strong alternative to heuristic multi-agent algorithms, which frequently encounter high computational costs with large controller design parameters.

Quadrotor is a key technology widely used in national defence, electric power and agriculture. In order to solve the problems of response hysteresis and sensitivity to sudden input changes of the conventional Proportional-Integral-Derivative (PID) control algorithm in quadrotor control, a filter is added to the outer differential term based on the conventional serial PID control to form an incomplete differential algorithm, which improves the dynamic characteristics of the system and reduces the sensitivity to sudden signal changes; at the same time, a filter is added based on the conventional serial PID control algorithm to form an incomplete differential algorithm, which improves the dynamic characteristics of the system and reduces the sensitivity to sudden signal changes. A filter is added based on the conventional series PID control algorithm to form an incomplete differential algorithm, which improves the dynamic characteristics of the system and reduces the sensitivity to abrupt signal changes; at the same time, a filter is added based on the conventional series PID control algorithm to achieve a smooth controller response curve. Simulation results show that compared with the conventional PID controller, the improved PID control algorithm reduces the maximum overshoot of the system step response from the original 8% to 3%, and its robustness and reliability are greatly improved.

Motors stand out as critical components in every element of modern life that requires motion. Control systems that govern the operation of these devices in industrial or commercial applications become crucial as a result. Improved performance results in increased efficiency, which in the long term translates to significant cost and energy savings and improved output quality. Numerous control systems have been developed specifically for this purpose over the last few decades in response to a persistent requirement for performance improvements. With that in mind, this paper is aimed at classifying and analysing a variety of different types of motor controllers in relation to previous work done with them. The shift from conventional to more intelligent adaptive controllers is highlighted along with their respective principles. Conventional controllers are those designed for linear systems without taking into account the possibility of parametric variation occurrence. Adaptive controllers have the ability to automatically learn and adapt with the state of the plant. Using transient attributes of closed loop control systems, their performance impact and feasibility are studied in detail. Subsequent conclusions are also drawn out to provide clear and concise reference.

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.

We develop a new intelligent adaptive control algorithm that is applicable to systems with large parameters variations and multiple operating modes. It uses a set of models, called the dynamic model bank, that guides the adaptation process. The dynamic model bank summarizes the parameters of the models that successfully approximate the plant. The model bank is automatically created and updated and does not call for an initial set of models. It uses a soft switching mechanism that provides a smooth transition from an interpolative to a pure "hard" switching scheme between the models in the bank. We also demonstrate the advantage of using this approach on several examples considering the control of systems with large parameter variations.

In this paper digital control solutions for speed control of direct current motor and level control of dual water tank system are described using conventional control algorithms such as (PID (Proportional + Integral + Derivative) and PI (Proportional + Integral)) as well as intelligent control algorithms based on fuzzy logic. Appropriate software tools are used to allow remote activation of the systems. Observation of the system behavior from remote terminals is made possible using novel tele-reality capability. This capability allows realistic CAD (Computer Aided Design) drawings that accurately represent the physical systems on the remote terminals to exhibit appropriate motion corresponding to the actual movement of the physical system in the University of Maryland Eastern Shore Mechatronics and Automation Laboratory (UMESMAL).

Intelligent mixed H2/H∞ adaptive tracking control system design using self-organizing recurrent fuzzy-wavelet-neural-network for uncertain two-axis motion control system

In this paper, a biologically-inspired adaptive intelligent secondary controller is developed for microgrids to tackle system dynamics uncertainties, faults, and/or disturbances. The developed adaptive biologically-inspired controller adopts a novel computational model of emotional learning in mammalian limbic system. The learning capability of the proposed biologically-inspired intelligent controller makes it a promising approach to deal with the power system non-linear and volatile dynamics without increasing the controller complexity, and maintain the voltage and frequency stabilities by using an efficient reference tracking mechanism. The performance of the proposed intelligent secondary controller is validated in terms of the voltage and frequency absolute errors in the simulated microgrid. Simulation results highlight the efficiency and robustness of the proposed intelligent controller under the fault conditions and different system uncertainties compared to other benchmark controllers.

PurposeThe purpose of this paper is to propose a novel event-triggered aperiodic intermittent sliding-mode control (ETAI-SMC) algorithm for master–slave bilateral teleoperation robotic systems to further save communication resources while maintaining synchronization precision.Design/methodology/approachBy using the Lyapunov theory, a new event-triggered aperiodic intermittent sliding-mode controller is designed to synchronize master–slave robots in a discontinuous method. Unlike traditional periodic time-triggered continuous control strategy, a new ETAI condition is discussed for less communication pressure. Then, the exponential reaching law is adopted to accelerate sliding-mode variables convergence, which has a significant effect on synchronization performance. In addition, the authors use quantizers to make their algorithm have obvious progress in saving communication resources.FindingsThe proposed control algorithm performance is validated by an experiment developed on a practical bilateral teleoperation system with two PHANToM Omni robotic devices. As a result, the synchronization error is limited within a small range and the control frequency is evidently reduced. Compared with a conventional control algorithm, the experimental results illustrate that the proposed control algorithm is more sensitive to system states changes and it can further save communication resources while guaranteeing the system synchronization accuracy, which is more practical for real bilateral teleoperation robotic systems.Originality/valueA novel ETAI-SMC for bilateral teleoperation robotic systems is proposed to find a balance between reducing the control frequency and synchronization control precision. Combining the traditional sliding-mode control algorithm with the periodic intermittent control strategy and the event-triggered control strategy has produced obvious effect on our control performance. The proposed ETAI-SMC algorithm helps the controller be more sensitive to system states changes, which makes it possible to achieve precise control with lower control frequency. Moreover, we design an environment contact force feedback algorithm for operators to improve the perception of the slave robot working environment. In addition, quantizers and the exponential convergence law are adopted to help the proposed algorithm perform better in saving communication resources and improving synchronization precision.