Fuzzy Tuning Parameters Research Articles

PMBLDC motor (PMBLDCM) with non-linear controller usage in electric propulsion of electric vehicles to control rotor speed (RS) and torque (EMT)

In present days existing vehicles are replaced with electric vehicles (EVs) with usage of PMBLDCM motors because of simplicity in design, rough usage, responding speed, and high efficiency. Due to the usage of dynamic loads in electric vehicles with PMBLDCM motor controlling speed and electromagnetic torque were major problems. These problems along with other problems faced in electric propulsion system (EPS) of electric vehicles (EVs) can be solved with the usage of the proposed non-linear controller (ANF-SMC) along with the usage of PMBLDCM motor for speed regulation and fuzzy parameter tuning based on motor performance under variable speed conditions. The proposed controller is designed by the usage of simulation software along fuzzy logic. To validate the proposed non-linear control system performance results compared with others exhibiting one linear and three non-linear controllers. Simulation results proved that with various loaded conditions proposed system performs well in all aspects when compared to four existing controllers.

Robust longitudinal motion control of underground mining electric vehicles based on fuzzy parameter tuning sliding mode controller

The desire to reduce costs and increase productivity is driving mining industries to deploy underground mining electric vehicles (UMEVs). Due to the complex, working environments in underground mines, safer and more stable control strategies of UMEVs are required than the conventional control approaches used for electric vehicles (EVs). In this paper, first a UMEV model with bounded system uncertainties is derived. Next, a fuzzy parameter tuning (FPT) technique is used to adjust parameters in the sliding mode controller (SMC) against unexpected external disturbances and unbounded system uncertainties, which significantly improves system stability and robustness. Simulation and experimental results demonstrate that the proposed fuzzy parameter tuning sliding mode controller (FPTSMC) achieves better performance than SMCs with parametric uncertainties.

Data Cube Clustering with Improved DBSCAN based on Fuzzy Logic and Genetic Algorithm

Multi-dimensional data, such as data cube, are constructed based on aggregating data in data warehouses and it requires to analyze with high flexibility. Also, clustering, which is an unsupervised pattern recognition analysis, has significant challenges to perform on data cube. In this paper, two new drafts of density-based clustering methods are designed to recognize unsupervised patterns of the data cube. In the first draft, DBSCAN clustering is hybridized by genetic algorithm and called the Improved DBSCAN (IDBSCAN). The motivation of designing the IDBSCAN optimizes the DBSCAN’s parameters by a meta-heuristic algorithm such as GA. The second draft, which is called the Soft Improved DBSCAN (SIDBSCAN), is designed based on fuzzy tuning parameters of the GA in the IDBSCAN. The fuzzy tuning parameters are performed with two fuzzy groups rules of Mamdani (SIDBSCAN-Mamdani) and Sugeno (SIDBSCAN-Sugeno), separately. These ideas are proposed to present efficient and flexible unsupervised analysis for a data cube by utilizing a meta-heuristic algorithm to optimize DBSCAN’s parameters and increasing the efficiency of the idea by applying dynamic tuning parameters of the algorithm. To evaluate the efficiency, the SIDBSCAN-Mamdani and the SIDBSCAN-Sugeno are compared with the IDBSCAN and the DBSCAN. The experimental results, consisted of 20 times running, indicate that the proposed ideas achieved to their targets.

A semi-automated design of instance-based fuzzy parameter tuning for metaheuristics based on decision tree induction

Two main concepts are established in the literature for the Parameter Setting Problem of metaheuristics: Parameter Tuning Strategies (PTS) and Parameter Control Strategies (PCS). While PTS result in a fixed parameter setting for a set of problem instances, PCS are incorporated into the metaheuristic and adapt parameter values according to instance-specific performance feedback. The idea of Instance-specific Parameter Tuning Strategies (IPTS) is aiming to combine advantages of both tuning and control strategies by enabling the adoption of parameter values tailored to instance-specific characteristics a priori to running the metaheuristic. This requires, however, a significant knowledge about the impact of instance characteristics on heuristic performance. This paper presents an approach that semi-automatically designs the fuzzy logic rule base to obtain instance-specific parameter values by means of decision trees. This enables the user to automate the process of converting insights about instance-specific information and its impact on heuristic performance into a fuzzy rule base IPTS system. The system incorporates the decision maker’s preference about the trade-off between computational time and solution quality.

Fuzzy Adaptive Sliding Mode Control of Large Erecting Mechanism

Sincethe large erecting mechanism has nonlinearities, parameter uncertainties andexternal disturbance, it is difficult to realize a model-based sliding modecontrol design. So a fuzzy adaptive sliding mode control scheme which combinedfuzzy control with sliding mode control is proposed to achieve nonlinearcontrol of the erecting mechanism. This control scheme is mainly use the fuzzysystem to approximate the equivalent control of the sliding mode controllerwithout knowing the system dynamic model. And it also releases thetrial-and-error work of establishing the fuzzy inference rules. The update lawsfor the fuzzy tuning parameters and the switch control parameter are derivedbased on the Lyapunov stability analysis. The simulation results show that comparedto the PID control and conventional sliding mode control, the fuzzy adaptivesliding mode control has nicer robustness and more accurate tracking ability,and the stability of the erecting process has improved. DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.3818

Particle Swarm Optimization (PSO) Based Topology Optimization of Part Design with Fuzzy Parameter Tuning

In order to improve the performance of the Particle Swarm Optimization (PSO) scheme on topology optimization of a part design, a fuzzy parameter tuning system is introduced in this study. Because thecorrelation and effect of constraints in PSO based topology optimization are uncertain and not well defined, a fuzzy logic system is used to adjust the constraints in PSO. After setting up the mathematical model, two illustrative examplesrunning in MATLAB environment are used to compare the performance of three topology optimization schemes: the Solid Isotropic Material with Penalization (SIMP) scheme, the standard PSO based topology optimization scheme and the fuzzy logic based PSO topology optimization scheme. It is found that, fuzzy logic based PSO has better search performance and efficiency than the standard PSO.

Active suppression of pneumatic vibration isolators using adaptive sliding controller with self-tuning fuzzy compensation

This paper develops intelligent control schemes for a diaphragm-type pneumatic vibration isolation system. The active-control schemes are applied to the pneumatic isolator to enhance isolation performances in the low-frequency range where passive techniques usually have difficulties in remaining effective, especially at the resonance frequency. The functional approximation technique (FAT) is integrated with a sliding-mode control (SMC) design to capture unknown system dynamics and release the requirement of mathematical modeling. To deal with approximation error and system dynamics variation, an adaptive fuzzy sliding-mode controller (AFSMC) is employed as a compensator of the FAT-based SMC. Lyapunov stability theory is used not only to ensure the closed-loop stability, but also to formulate the updating laws for weighting coefficients of expansion basis and fuzzy tuning parameters. To validate the proposed method, a composite control scheme using pressure and velocity measurements as feedback signals is implemented. Experimental explorations indicate that isolation performances obtained using the proposed FAT-based sliding control augmented with AFSMC compensation (FA + AFSMC) are evidently better than those of the traditional proportional integral-derivative control and solely AFSMC scheme.

Adaptive Sliding-Mode Control with Self-Tuning Fuzzy Compensation for a Piezoelectric-Actuated X-Y-Z Table

This paper aims to develop and implement intelligent control for a X-Y-Z three-dimensional table using linear ultrasonic motors as the actuators. Functional approximation technique (FAT) is integrated with the sliding-mode control design to capture the unknown system dynamics and release the requirement of mathematical modeling. In order to deal with the approximation error and the uncertainties of the system, an adaptive fuzzy sliding-mode controller (AFSMC) is used as a compensation of the FAT-based sliding-mode control. Lyapunov stability theory is applied to assure the closed-loop stability and design the updating laws for weighting coefficients of Fourier series and fuzzy tuning parameters. Experimental results indicate that FAT-based sliding-mode control augmented with AFSMC compensation (FA+AFSMC) performs better than the solely FAT-based sliding-mode control.

Adaptive sliding control with self-tuning fuzzy compensation for a piezoelectrically actuated X–Y table

The piezoelectrically actuated system has non-linear and time-varying behaviour, hence it is difficult to establish an accurate dynamic model for a model-based sliding-mode control design. In this study, a model-free adaptive sliding controller is proposed to control the piezoelectrically actuated system. This control strategy uses the functional approximation (FA) technique to establish the unknown function for releasing the model-based requirement of the sliding-mode control. In addition, a fuzzy scheme with online learning ability is introduced to compensate the FA error for improving the control performance and reducing the implementation difficulty. The important advantages of this approach are to achieve the sliding-mode controller design without the system dynamic model requirement and release the trial-and-error work of selecting approximation function. The update laws for the coefficients of the Fourier series functions and the fuzzy tuning parameters are derived from a Lyapunov function to guarantee the control system stability. This proposed controller is implemented on a piezoelectrically actuated X–Y table. To verify the dynamic performance improvement of inducing a fuzzy compensation in this model-free controller, the dynamic responses of the proposed controller are compared with those of the adaptive sliding controller without fuzzy compensation.

Visually Aided Force Control with Fuzzy Parameter Tuning

AbstractVision and force sensors provide rich information which can enable robots to execute complex tasks. However, the integration of these two different types of sensors is not a trivial task. This paper provides a novel technique for the integration of vision and force information. Visual servoing and explicit force control techniques are applied in the task frame formalism. Disadvantages of the constant parameter controllers are addressed and a solution based on fuzzy tuning rules is proposed. The solution is an example of shared control strategy. Experimental results conducted on a 2 DOF direct drive SCARA robot for the manipulation of a sample object are presented both for fixed parameter and fuzzy-tuned controller and results are compared.

A new model-free adaptive sliding controller for active suspension system

Active suspension systems are designed to provide better ride comfort and handling capability in the automotive industry. Since the active suspension system has nonlinear and time-varying characteristics, it is difficult to establish an accurate dynamic model for designing a model-based controller. Here, a functional approximation (FA) based adaptive sliding controller with fuzzy compensation is proposed for an active suspension system. The FA technique is employed to represent the unknown functions, which releases the model-based requirement of the sliding mode control. In addition, a fuzzy control scheme with online learning ability is employed to compensate for the modeling error of the FA with finite number of terms for reducing the implementation difficulty. To guarantee the control system stability, the update laws of the coefficients in the approximation function and the fuzzy tuning parameters are derived from the Lyapunov theorem. The proposed controller is employed on a quarter-car active suspension system. The simulation results and experimental results show that the proposed controller can suppress the oscillation amplitude of the sprung mass effectively. To evaluate the performance improvement of inducing a fuzzy compensator in this FA adaptive controller, the dynamic responses of the proposed hybrid controller are compared with those of FA-based adaptive sliding controller only.

Adaptive sliding controller with self-tuning fuzzy compensation for vehicle suspension control

Since the hydraulic actuating suspension system has nonlinear and time-varying behavior, it is difficult to establish an accurate dynamic model for a model-based sliding mode control design. Here, a novel model-free adaptive sliding controller is proposed to suppress the position oscillation of the sprung mass in response to road surface variation. This control strategy employs the functional approximation technique to establish the unknown function for releasing the model-based requirement. In addition, a fuzzy scheme with online learning ability is introduced to compensate the functional approximation error for improving the control performance and reducing the implementation difficulty. The important advantages of this approach are to achieve the sliding mode controller design without the system dynamic model requirement and release the trial-and-error work of selecting approximation function. The update laws for the coefficients of the Fourier series functions and the fuzzy tuning parameters are derived from a Lyapunov function to guarantee the control system stability. The experimental results show that the proposed control scheme effectively suppresses the oscillation amplitude of the vehicle sprung mass corresponding to the road surface variation and external uncertainties, and the control performance is better than that of a traditional model-based sliding mode controller.

Functional Based Adaptive and Fuzzy Sliding Controller for Non-Autonomous Active Suspension System

In this paper, an adaptive sliding controller is developed for controlling a vehicle active suspension system. The functional approximation technique is employed to substitute the unknown non-autonomous functions of the suspension system and release the model-based requirement of sliding mode control algorithm. In order to improve the control performance and reduce the implementation problem, a fuzzy strategy with online learning ability is added to compensate the functional approximation error. The update laws of the functional approximation coefficients and the fuzzy tuning parameters are derived from the Lyapunov theorem to guarantee the system stability. The proposed controller is implemented on a quarter-car hydraulic actuating active suspension system test-rig. The experimental results show that the proposed controller suppresses the oscillation amplitude of the suspension system effectively.

Functional approximation-based adaptive sliding control with fuzzy compensation for an active suspension system

Since it is difficult to establish an accurate dynamic model of a physical active suspension system for a model-based control design, various model-free approaches have been introduced in this field of application. This paper proposes a functional approximation-based adaptive sliding controller with fuzzy compensation for a quarter-car active suspension system. The functional approximation technique is employed to represent the unknown function and release the model-based requirement of a sliding mode control. In addition, a self-tuning fuzzy scheme is introduced to compensate the model approximation error for improving the control performance. The update laws for the coefficients of the Fourier series functions and the fuzzy tuning parameters can be derived from the Lyapunov function directly for guaranteeing the system stability. The simulation results show that the proposed control approach can suppress effectively the vibration amplitude of this suspension system under severe external uncertainties. The dynamic performance of this proposed controller is compared with that of the adaptive sliding controller without fuzzy compensation to show the performance improvement of the inducing of fuzzy control loop.