Search

This paper introduces an analog integrated fractional order type-2 fuzzy PID control system. Current approaches frequently depend on energy-intensive embedded digital systems, consuming substantial energy levels ranging from a few μW to mW. To address this limitation we propose a fully analog design offering insights into the potential of analog circuits for powerefficient robust control in complex and uncertain environments. It consists of Gaussian function, min/max, Operational transcoductance amplifier circuits and Resistor-Capacitor networks for the implementation of the fractional-order components. Crafted for operation under a reduced voltage supply (0.6 V), the controller attains minimal power usage (861.8 nW), facilitating uninterrupted, extended-term functioning. Post-layout simulation results confirm the proper operation of the proposed design. The proposed system is designed and simulated using the Cadence IC Suite in a TSMC 90 nm CMOS process. [ABSTRACT FROM AUTHOR]

An effort is made to design the fuzzy proportional-derivative (PD) plus I controller for a nonlinear cruise control system in automobiles, which provides adaptive capability in set-point tracking performance. A cruise control system has been considered as a nonlinear first order plus delay time model to exhibit the control behaviour involved in both conventional proportional-integral-derivative (PID) control and fuzzy control. The paper demonstrates the design of fuzzy PD plus I controller including comparative investigation with control structures like PID, I - PD, and PI - D using Simulink modelling. Real-time implementation has been carried out on a robotic prototype using Arduino UNO micro-controller. Based on the performance measures such as integral absolute error (IAE) and integral squared error (ISE), the proposed fuzzy PD plus I structure shows superior performance on servo and regulatory problems in the cruise control system. This control structure avoids integral windup issues and to suppress the derivative kick in the cruise control system. [ABSTRACT FROM AUTHOR]

Although the proton exchange membrane fuel cell (PEMFC) is still attracting enormous R&D interest because of its high energy density, its commercialization is hampered by many challenges including cutting cost, improving performance and increasing durability. While they could be solved by material selection, the durability of PEMFC is also affected by voltage reversals and fuel starvation. In this paper, PEMFC control sub-systems namely the reaction, thermal, water management and power electronic subsystems are reviewed critically, with special attention on control strategies to avoid fuel starvation. Classical proportional integral and derivative (PID) controllers are commonly used in feedback voltage control and feed-forward current control by manipulating hydrogen and air flow rates. Self-tuning PID controllers or sliding mode controllers adapt to changing dynamics and respond faster. Adaptive controllers (AC) such as load governors and extremum seeking controllers update control action continuously. Model predictive control (MPC) uses a PEMFC model to predict system behavior and update controller action. Recently, artificial intelligence such as neural network control (NNC), fuzzy logic control (FLC) and FLC-PID control have been used in PEMFC system control because they are simpler and cheaper to implement without heavy computational burden of the AC and MPC but produce better results. [ABSTRACT FROM AUTHOR]

The aim of this work is to design robust control algorithms of aerial robots, i.e. quadrotors, for team transportation of a deformable linear object (DLO). The DLO-robot attachment makes the whole system physically coupled by an elastic element, which introduces strong non-linearities in the system dynamics. Sections in quasi-stationary state of the DLO hanging freely from two extreme points can be modeled by catenary curves, so we are able to build a detailed and accurate simulation of the system as the workbench for the evaluation of alternative quadrotor control approaches. DLO transportation is an instance of a follow the leader team strategy, in which the local quadrotor control must cope with the dynamic perturbations due to the DLO linking the quadrotors. The quadrotor team control has two phases, one achieving a spatial configuration with equal energy consumption, the other is to manage the horizontal motion which is the transportation process per se. In this paper we deal with the second control problem, assuming the first solved. We use proportional derivative (PD) controllers for both quadrotor attitude and trajectory control. Offline PD parameter setting carried by metaheuristic optimization can not cope with the perturbations induced by the DLO and environmental conditions, i.e. wind shear. Therefore we propose an adaptive controller for the quadrotors carrying out the DLO transportation task which uses fuzzy modeling of the error in order to modulate the activation of the PD parameters adaptation rules, which are error gradient descent rules. Computational experiments on our system simulation workbench show that our adaptive approach scales well when increasing the number of quadrotors, providing smooth follow-the-leader team strategy navigation behaviors even under strong wind perturbation conditions. We compare our approach with other state-of-theart online and offline approaches. [ABSTRACT FROM AUTHOR]

In the present study, a desired reference trajectory was autonomously tracked by means of a quadrotor unmanned aerial vehicle with a self-tuning fuzzy proportional integral derivative controller. A proportional integral derivative controller and a fuzzy system tuning gains from proportional integral derivative controller are applied to stabilize the quadrotor, to control the attitude and to track the trajectory. Inputs of fuzzy logical controller consist of the speed required for the distance between the current position of unmanned aerial vehicle and the defined reference point and differences between orientation angles and variance in differences. Outputs of fuzzy logical controller consist of the proportional integral derivative coefficients which produce pitch, roll, yaw and height values. The fuzzy proportional integral derivative control algorithm is real-time applied to the quadrotor in MATLAB/Simulink environment. Based on data from experimental studies, although both classical proportional integral derivative controller and self-tuning fuzzy proportional integral derivative controller have accomplished to track a defined trajectory with the aircraft, the self-tuning fuzzy proportional integral derivative controller has been able to control with less errors than the classical proportional integral derivative controller. [ABSTRACT FROM AUTHOR]

This paper introduces an adaptive fuzzy PI controller (AFPIC) for a flexible AC transmission system (FACTS)- based dynamic power filter (DPF) to be used in wave energy conversion systems. The new FACTS device stabilizes the DC-common bus voltage, reduces quality of power troubles, and enhances energy utilization by acting as a bus quantity enhancer (BQE). The design and realization of the proposed FACTS-based DPF and efficient control schemes are fully studied. To validate the efficiency of the proposed BQE FACTS device, a digital simulation model and a laboratory test system are developed in the MATLAB/Simulink/Simpower software environment for comparison. Various experimental test models of the proposed BQE system and dynamic error-based controller structures have been utilized to verify the simulation results. It has been shown that the utilization of the proposed AFPIC with the novel BQE device and multivariable error driven control strategy is very effective to eliminate stochastic wave influences on voltage on the load side and load variations on the source side by decreasing voltage sag and swells. The effectiveness of the BQE is also tested by applying error energy-based performance indices ISE, IAE, and ITAE. [ABSTRACT FROM AUTHOR]

Although most real building structure controllers are in the form of proportional-derivative/proportional-integral-derivative (PD/PID), there have been few published theory results of PD/PID on structural vibration control. In order to minimize the regulation error, a PD/PID control needs relatively large derivative and integral gains. These deteriorate the transient performances of the vibration control. In this paper, a natural combination of industrial PD/PID control with fuzzy compensation is proposed. The main contribution of this paper is that the stability of the fuzzy PD/PID control is proven with standard weight training algorithms. These conditions give explicit selection methods for the gains of the PD/PID control. Experimental studies on a two-story building prototype with the controllers are addressed. The experimental results validate our theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper deals with model reference adaptive system (MRAS) speed estimators based on a secondary flux for linear induction motors (LIMs). The operation of these estimators significantly depends on an adaptation mechanism. Fixed-gain PI controller is the most common adaptation mechanism that may fail to estimate the speed correctly in different conditions, such as variation in machine parameters and noisy environment. Two adaptation mechanisms are proposed to improve LIM drive system performance, particularly at very low speed. The first adaptation mechanism is based on fuzzy theory, and the second is obtained from an LIM mechanical model. Compared with a conventional PI controller, the proposed adaptation mechanisms have low sensitivity to both variations of machine parameters and noise. The optimum parameters of adaptation mechanisms are tuned using an offline method through chaotic optimization algorithm (COA) because no design criterion is given to provide these values. The efficiency of MRAS speed estimator is validated by both numerical simulation and real-time hardware-in-the-loop (HIL) implementations. Results indicate that the proposed adaptation mechanisms improve performance of MRAS speed estimator. [ABSTRACT FROM AUTHOR]

Various control strategies for the air feed to a PEM fuel cell are investigated. Feedback and feedforward control strategies are used simultaneously in order to achieve maximum net power and prevent oxygen starvation. The control objective is to adjust the oxygen excess ratio using compressed air based on external disturbance variations. Maximum power tracking is obtained using the optimal oxygen excess ratio which is also compared to the constant oxygen excess ratio. In the feedforward strategy, a fuzzy logic controller and in the feedback a filtered PID controller is applied. The controller parameters are optimized simultaneously using a self-adaptive differential evolution algorithm. Simulation results show that the proposed technique improves the fuel cell system performance and prevents oxygen starvation by fixing the oxygen ratio at a proper setpoint. [ABSTRACT FROM AUTHOR]

We first analyze the effect of network-induced delay on the stability of networked control systems (NCSs). Then, aiming at stochastic characteristics of the time delay, we introduce a new Smith predictor to remove the exponential function with the time delay in the closed-loop characteristic equation of the NCS. Furthermore, we combine the fuzzy PID algorithm with the fuzzy immune control algorithm and present a fuzzy immune self-adaptive PID algorithm to compensate the influence of the model deviation of the controlled object. At last, a kind of fuzzy immune self-adaptive PID algorithm based on new Smith predictor is presented to apply to the NCS. The simulation research on a DC motor is given to show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this article, an adaptive fuzzy speed controller based on the back-stepping method is designed by considering the existence of parameter variations and load disturbances in the permanent magnet synchronous motor servo system. The adaptive fuzzy logic system is used to approximate the non-linear parts of the servo system on-line. An obvious advantage of this method is that servo system parameters, such as stator resistance, stator inductance, flux, inertia, and load torque, are not necessarily known during controller design. Moreover, the influence of parameter variations and load disturbances is effectively inhibited, and the speed tracking performance is improved. By adopting the Lyapunov method, it is proved that both the speed error and the current error converge exponentially. Simulations and experiments indicate that the designed controller performs with better adaptivity and robustness compared with the proportion integration (PI) controller, thus showing its advantages in practical applications. [ABSTRACT FROM PUBLISHER]

Conventional erecting mechanism is cumbersome and the erecting velocity is slow, so a new hydraulic erecting mechanism is introduced in this paper. But it has two problems. First is the cooperation problem between the erect cylinder and the horizontal cylinder. Second, the nonlinearities and uncertainties exist in the system make the control precision poor. So the combined simulation model is built based on the software AMESim and Simulink. And from this model, the best cooperation time is confirmed. Further, the fuzzy adaptive PID controller is introduced to improve the control performance aiming to the nonlinearities and uncertainties. The fuzzy inference rules and parameters of the controller are also presented in this paper. Experiment results show that the cooperation time which received from the simulation is correct, and the fuzzy adaptive PID controller has nice tracking performance compared to the conventional PID controller and fuzzy controller. [ABSTRACT FROM AUTHOR]

This paper presents a new hyperchaotic system by introducing an additional state variable into Lorenz system. The system's characteristics, including the dissipativity, equilibrium, and Lyapunov exponents, are studied. A controller is developed which consists of an active control termand a variable universe adaptive fuzzy system. By using this controller, the synchronization of the new hyperchaotic systems with uncertain linear part is accomplished according to Lyapunov's direct method. Simulation results illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a novel design method for determining the optimal fuzzy PID-controller parameters of active automobile suspension system using the particle swarm optimization (PSO) reinforcement evolutionary algorithm is presented. This paper demonstrated in detail how to help the PSO with Q-learning cooperation method to search efficiently the optimal fuzzy-PID controller parameters of a suspension system. The design of a fuzzy system can be formulated as a search problem in high-dimensional space where each point represents a rule set, membership functions, and the corresponding system’s behavior. In order to avoid obtaining the local optimum solution, we adopted a pure PSO global exploration method to search fuzzy-PID parameter. Later this paper explored the improved the limitation between suspension and tire deflection in active automobile suspension system with nonlinearity, which needs to be solved ride comfort and road holding ability problems, and so on. These studies presented many ideas to solve these existing problems, but they need much evolution time to obtain the solution. Motivated by above discussions this paper propose a novel algorithm which can decrease the number of evolution generation, and can also evolve the fuzzy system for obtaining a better performance. [Copyright &y& Elsevier]