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This paper aims to keep the pendulum on the linear moving car vertically balanced and to bring the car to the equilibrium position with the designed controllers.

As inverted pendulum systems are structurally unstable and nonlinear dynamic systems, they are important mechanisms used in engineering and technological developments to apply control techniques on these systems and to develop control algorithms, thus ensuring that the controllers designed for real-time balancing of these systems have certain performance criteria and the selection of each controller method according to performance criteria in the presence of destructive effects is very helpful in getting information about applying the methods to other systems.

As a result, the designed controllers are implemented on a real-time and real system, and the performance results of the system are obtained graphically, compared and analyzed.

In this study, motion equations of a linear inverted pendulum system are obtained, and classical and artificial intelligence adaptive control algorithms are designed and implemented for real-time control. Classic proportional-integral-derivative (PID) controller, fuzzy logic controller and PID-type Fuzzy adaptive controller methods are used to control the system. Self-tuning PID-type fuzzy adaptive controller was used first in the literature search and success results have been obtained. In this regard, the authors have the idea that this work is an innovative aspect of real-time with self-tuning PID-type fuzzy adaptive controller.

This study aims to present a method for the conceptual design and simulation of an aircraft flight control system.

The design methodology is based on particle swarm optimization (PSO). PSO can be used to improve the performance of conventional controllers. The aim of the present study is threefold. First, it attempts to detect and isolate faults in an aircraft model. Second, it is to design a proportional (P) controller, a proportional derivative (PD) controller, a proportional-integral (PI) controller and a fuzzy controller for an aircraft model. Third, it is to design a PD controller for an aircraft using a PSO algorithm.

Conventional controllers, an intelligent controller and a PD controller-based PSO were investigated for flight control. It was seen that the P controller, the PI controller and the PD controller-based PSO caused overshoot. These overshoots were 18.5, 87.7 and 2.6 per cent, respectively. Overshoot was not seen using the PD controller or fuzzy controller. Steady state errors were almost zero for all controllers. The PD controller had the best settling time. The fuzzy controller was second best. The PD controller-based PSO was the third best, but the result was close to the others.

This study shows the implementation of the present algorithm for a specified space mission and also for study regarding variation of performance parameters. This study shows fault detection and isolation procedures and also controller gain choice for a flight control system. A comparison between conventional controllers and PD-based PSO controllers is presented. In this study, sensor fault detection and isolation are carried out, and, also, root locus, time domain analysis and Routh–Hurwitz methods are used to find the conventional controller gains which differ from other studies. A fuzzy controller is created by the trial and error method. Integral of squared time multiplied by squared error is used as a performance function type in PSO.

The purpose of this paper is to gain trajectory-tracking controllers for autonomous aircraft are optimized using a modified evolutionary, or genetic algorithm (GA).

The GA design utilizes real representation for the individual consisting of the collection of all controller gains subject to tuning. The initial population is generated randomly over pre-specified ranges. Alternatively, initial individuals are produced as random variations from a heuristically tuned set of gains to increase convergence time. A two-point crossover mechanism and a probabilistic mutation mechanism represent the genetic alterations performed on the population. The environment is represented by a performance index (PI) composed of a set of metrics based on tracking error and control activity in response to a commanded trajectory. Roulette-wheel selection with elitist strategy are implemented. A PI normalization scheme is also implemented to increase the speed of convergence. A flexible control laws design environment is developed, which can be used to easily optimize the gains for a variety of unmanned aerial vehicle (UAV) control laws architectures.

The performance of the aircraft trajectory-tracking controllers was shown to improve significantly through the GA optimization. Additionally, the novel normalization modification was shown to encourage more rapid convergence to an optimal solution.

The GA paradigm shows much promise in the optimization of highly non-linear aircraft trajectory-tracking controllers. The proposed optimization tool facilitates the investigation of novel control architectures regardless of complexity and dimensionality.

The addition of the evolutionary optimization to the WVU UAV simulation environment enhances significantly its capabilities for autonomous flight algorithm development, testing, and evaluation. The normalization methodology proposed in this paper has been shown to appreciably speed up the convergence of GAs.

The paper provides a flexible generalized framework for UAV control system evolutionary optimization. It includes specific novel structural elements and mechanisms for improved convergence as well as a comprehensive PI for trajectory tracking.

This paper presents the particle swarm optimization (PSO) algorithm in conjuction with the fuzzy logic method in order to achieve an optimized tuning of a proportional integral derivative controller (PID) in the DTC control loops of dual star induction motor (DSIM). The fuzzy controller is insensitive to parametric variations, however, with the PSO-based optimization approach we obtain a judicious choice of the gains to make the system more robust. According to Matlab simulation, the results demonstrate that the hybrid DTC of DSIM improves the speed loop response, ensures the system stability, reduces the steady state error and enhances the rising time. Moreover, with this controller, the disturbances do not affect the motor performances.

Various control structures and approaches are in use nowadays. Development of new ideas allows to obtain better quality in control of different industrial processes and hence better quality of products. As it may seem that everything in the classical systems has already been discovered, more and more research centres are tending to incorporate fuzzy or neural control systems. The purpose of this paper is to present an application of the adaptive neuro-fuzzy PID speed controller for a DC drive system with a complex nonlinear mechanical part.

The model of the driven object including such elements as nonlinear shaft with backlash and friction has been modelled using Matlab-Simulink software. Afterwards experimental verification has been made using a dSPACE control card and experimental system with two DC motors connected with an elastic shaft.

The presented study shown that the adaptive controller is able to damp the torsional vibration effectively even for the wide range of the system nonlinearities. What is more the design approach for controllers design parameters has been described. Proposed approach is based on requested properties of system. Using proposed tuning scheme no detailed information about the object are needed.

This paper presents for the first time fully an PID adaptive neuro-fuzzy controller. The inputs are the weighted tracking error, error’s derivative and integrated error. What is more the adaptation algorithm consists of a model tracking error its derivative and integer. Also the proposed tuning algorithm in such a form is an original outcome.

The purpose of this paper is to address a fractional order fuzzy PID (FOFPID) control approach for solving the problem of enhancing high precision tracking performance and robustness against to different reference trajectories of a 6-DOF Stewart Platform (SP) in joint space.

For the optimal design of the proposed control approach, tuning of the controller parameters including membership functions and input-output scaling factors along with the fractional order rate of error and fractional order integral of control signal is tuned with off-line by using particle swarm optimization (PSO) algorithm. For achieving this off-line optimization in the simulation environment, very accurate dynamic model of SP which has more complicated dynamical characteristics is required. Therefore, the coupling dynamic model of multi-rigid-body system is developed by Lagrange-Euler approach. For completeness, the mathematical model of the actuators is established and integrated with the dynamic model of SP mechanical system to state electromechanical coupling dynamic model. To study the validness of the proposed FOFPID controller, using this accurate dynamic model of the SP, other published control approaches such as the PID control, FOPID control and fuzzy PID control are also optimized with PSO in simulation environment. To compare trajectory tracking performance and effectiveness of the tuned controllers, the real time validation trajectory tracking experiments are conducted using the experimental setup of the SP by applying the optimum parameters of the controllers. The credibility of the results obtained with the controllers tuned in simulation environment is examined using statistical analysis.

The experimental results clearly demonstrate that the proposed optimal FOFPID controller can improve the control performance and reduce reference trajectory tracking errors of the SP. Also, the proposed PSO optimized FOFPID control strategy outperforms other control schemes in terms of the different difficulty levels of the given trajectories.

To the best of the authors’ knowledge, such a motion controller incorporating the fractional order approach to the fuzzy is first time applied in trajectory tracking control of SP.

In this paper, a novel Lyapunov–Krasovskii stable fuzzy proportional-integral-derivative (PID) (FPID) controller is introduced for load frequency control of a time-delayed micro-grid (MG) system that benefits from a fuel cell unit, wind turbine generator and plug-in electric vehicles.

Using the Lyapunov–Krasovskii theorem, the adaptation laws for the consequent parameters and output scaling factors of the FPID controller are developed in such a way that an upper limit (the maximum permissible value) for time delay is introduced for the stability of the closed-loop MG system. In this way, there is a stable FPID controller, the adaptive parameters of which are bounded. In the obtained adaptation laws and the way of stability analyses, there is no need to approximate the nonlinear model of the controlled system, which makes the implementation process of the proposed adaptive FPID controller much simpler.

It has been shown that for a different amount of time delay and intermittent resources/loads, the proposed adaptive FPID controller is able to enforce the frequency deviations to zero with better performance and a less amount of energy. In the proposed FPID controller, the increase in the amount of time delay leads to a small increase in the amount of overshoot/undershoot and settling time values, which indicate that the proposed controller is robust to the time delay changes.

Although the designed FPID controllers in the literature are very efficient in being applied to the uncertain and nonlinear systems, they suffer from stability problems. In this paper, the stability of the FPID controller has been examined in applying to the frequency control of a nonlinear input-delayed MG system. Based on the Lyapunov–Krasovskii theorem and using rigorous mathematical analyses, the stability conditions and the adaptation laws for the parameters of the FPID controller have been obtained in the presence of input delay and nonlinearities of the MG system.

This study aims to propose an improved linear quadratic regulator (LQR) based on the adjusting weight coefficient, which is used to improve the performance of the vehicle direct yaw moment control (DYC) system.

After analyzing the responses of the side-slip angle and the yaw rate of the vehicle when driving under different road adhesion coefficients, the genetic algorithm and fuzzy logic theory were applied to design the parameter regulator for an improved LQR. This parameter regulator works according to the changes in the road adhesion coefficient between the tires and the road. Hardware-in-the-loop (HiL) tests with double-lane changes under low and high road surface adhesion coefficients were carried out.

The HiL test results demonstrate the proposed controllers’ effectiveness and reasonableness and satisfy the real-time requirement. The effectiveness of the proposed controller was also proven using the vehicle-handling stability objective evaluation method.

The objective evaluation results reveal better performance using the improved LQR DYC controller than a front wheel steering vehicle, especially in reducing driver fatigue, improving vehicle-handling stability and enhancing driving safety.

Ovens are semi-industrial multipurpose equipment that are used to provide a desired temperature for specific chemical processes. Temperature regulation in the presence of different type of disturbances and dealing with nonlinear dynamics with large dead time (up to a few minutes) are some undesirable factors that have to be considered in the controller design procedure of the oven systems. Due to these factors, the classical PID controller tuned using Cohen-Coon or Ziegler–Nichol’s tuning methods often fails to meet satisfactory closed-loop performance.

In this paper, to deal with the limitations on the oven system due to the undesirable factors, a hierarchical automaton-guided form controller has been designed. The proposed controller includes several discrete PI controllers, each of which operates locally in the defined operating regions whose separation idea is specific to this paper. Based on the idea proposed in the separation of regions, the controller’s coefficients tuning rules are extracted prior to any determination. Then, a supervisor controller has assumed the task of switching between local controllers. In the next step, by considering a conceptual model for the oven system and using a candidate Lyapunov function, the stability conditions of closed-loop system are discussed and the necessary conditions for the asymptotic stability are derived. The proposed controller is practically implemented with the help of the Arduino Nano platform.

Using several experiments, the superiority of the proposed hierarchical controller in terms of performance and energy consumption has been demonstrated.

The proposed hierarchical controller has been implemented practically and an acceptable closed-loop performance has been achieved. To illustrate the efficiency of the proposed method, the closed-loop stability of this method is shown using the Lyapunov theory.

This study aims to promote the anti-disturbance and tracking accuracy of optoelectronic stabilized platforms, which ensure that optical detectors accurately track targets and acquire high-quality images.

An improved active disturbance rejection control (ADRC) strategy based on model-assisted double extended state observers (MDESOs) is proposed in this paper. First, by establishing an auxiliary model, the total disturbances are separated into two parts: inner and external disturbances. Then, MDESOs are designed to estimate the two parts by separately using two parallel ESOs, by which the controlled plant is adjusted to the ideal pure integral series. Simultaneously, combined with the nonlinear state error feedback, an overall control strategy is established.

Compared with the conventional ADRC and proportional derivative, the improved ADRC (IADRC) has stronger robustness and adaptability and effectively reduces the requirements for model accuracy and the gain of the ESO. The error of the auxiliary model is tolerated to exceed 50%, and the parameter values of the MDESOs are reduced by 90%.

The total disturbance rejection rate of the proposed strategy is only 3.11% under multiple disturbances, which indicates that the IADRC strategy significantly promotes anti-disturbance performance.