Search results
1 – 10 of over 2000Abstract
Purpose
To design effective and practical controllers that use the adaptive fuzzy approaches and are applicable to helicopters.
Design/methodology/approach
Based on Takagi‐Sugeno fuzzy systems, a new direct adaptive fuzzy control scheme is developed for a class of nonlinear multiple‐input‐multiple‐output systems. A simple observer is designed to generate an error signal for the adaptive law. The system states of the system are not required to be available for measurement.
Findings
The overall adaptive scheme guarantees all the signals involved being uniformly bounded in the Lyapunov sense.
Research limitations/implications
The implementation of this research work needs further investigation.
Practical implications
The simplicity of the design algorithm facilitates the application of the design to helicopters by the use of Matlab.
Originality/value
Experimental results of a two degree of freedom helicopter are presented to confirm the usefulness of the proposed new control scheme.
Details
Keywords
mohammad mehdi fateh and Mohaddeseh Amerian
A hydraulic elevator including the hydraulic actuator and cabin is highly nonlinear with many parameters and variables. Its state-space model is in non-companion form and…
Abstract
Purpose
A hydraulic elevator including the hydraulic actuator and cabin is highly nonlinear with many parameters and variables. Its state-space model is in non-companion form and uncertain due to the parametric errors, flexibility of the ropes, friction and external load disturbances. A model-based control cannot perform well while a precise model is not available and all state variables cannot be measured. To overcome the problems, this paper aims to develop a direct adaptive fuzzy control (DAFC) for the hydraulic elevator.
Design/methodology/approach
The controller is an adaptive PD-like Mamdani type fuzzy controller using position error and velocity error as inputs. The design is based on the stability analysis.
Findings
The proposed control can overcome uncertainties, guarantee stability, provide a good tracking performance and operate as active vibration suppression by tracking a smooth trajectory. The controller is not involved in the nonlinearity, uncertainty and vibration of the system due to being free from model. Its performance is superior to a PD-like fuzzy controller due to being adaptive as illustrated by simulations.
Originality/value
The proposed DAFC is applied for the first time on the hydraulic elevator. Compared to classic adaptive fuzzy, it does not require all system states. In addition, it is not limited to the systems, which have the state-space model in companion form and constant input gain, thus is much less computational and easier to implement.
Details
Keywords
Mohammad Mehdi Fateh, Siamak Azargoshasb and Saeed Khorashadizadeh
– Discrete control of robot manipulators with uncertain model is the purpose of this paper.
Abstract
Purpose
Discrete control of robot manipulators with uncertain model is the purpose of this paper.
Design/methodology/approach
The proposed control design is model-free by employing an adaptive fuzzy estimator in the controller for the estimation of uncertainty as unknown function. An adaptive mechanism is proposed in order to overcome uncertainties. Parameters of the fuzzy estimator are adapted to minimize the estimation error using a gradient descent algorithm.
Findings
The proposed model-free discrete control is robust against all uncertainties associated with the model of robotic system including the robot manipulator and actuators, and external disturbances. Stability analysis verifies the proposed control approach. Simulation results show its efficiency in the tracking control.
Originality/value
A novel model-free discrete control approach for electrically driven robot manipulators is proposed. An adaptive fuzzy estimator is used in the controller to overcome uncertainties. The parameters of the estimator are regulated by a gradient descent algorithm. The most gradient descent algorithms have used a known cost function based on the tracking error for adaptation whereas the proposed gradient descent algorithm uses a cost function based on the uncertainty estimation error. Then, the uncertainty estimation error is calculated from the joint position error and its derivative using the closed-loop system.
Details
Keywords
Vasundhara Mahajan, Pramod Agarwal and Hari Om Gupta
The active power filter with two-level inverter needs a high-rating coupling transformer for high-power applications. This complicates the control and system becomes bulky and…
Abstract
Purpose
The active power filter with two-level inverter needs a high-rating coupling transformer for high-power applications. This complicates the control and system becomes bulky and expensive. The purpose of this paper is to motivate the use of multilevel inverter as harmonic filter, which eliminates the coupling transformer and allows direct control of the power circuit. The advancement in artificial intelligence (AI) for computation is explored for controller design.
Design/methodology/approach
The proposed scheme has a five-level cascaded H-bridge multilevel inverter (CHBMLI) as a harmonic filter. The control scheme includes one neural network controller and two fuzzy logic-based controllers for harmonic extraction, dc capacitor voltage balancing, and compensating current adjustment, respectively. The topology is modeled in MATLAB/SIMULINK and implemented using dSPACE DS1103 interface for experimentation.
Findings
The exhaustive simulation and experimental results demonstrate the robustness and effectiveness of the proposed topology and controllers for harmonic minimization for RL/RC load and change in load. The comparison between traditional PI controller and proposed AI-based controller is presented. It indicates that the AI-based controller is fast, dynamic, and adaptive to accommodate the changes in load. The total harmonic distortion obtained by applying AI-based controllers are well within the IEEE519 std. limits.
Originality/value
The simulation of high-power, medium-voltage system is presented and a downscaled prototype is designed and developed for implementation. The laboratory module of CHBMLI-based harmonic filter and AI-based controllers modeled in SIMULINK is executed using dSPACE DS1103 interface through real time workshop.
Details
Keywords
Paraskevi Zacharia, Nikos Aspragathos, Ioannis Mariolis and Evaggelos Dermatas
The purpose of this paper is to present a flexible automation system for the manipulation of fabrics lying on a work table and focuses on the design of a robot control system…
Abstract
Purpose
The purpose of this paper is to present a flexible automation system for the manipulation of fabrics lying on a work table and focuses on the design of a robot control system based on visual servoing and fuzzy logic for handling flexible sheets lying on a table. The main contribution of this paper is that the developed system tolerates deformations that may appear during robot handling of fabrics due to buckling without the need for fabric rigidization.
Design/methodology/approach
The vision system, consisting of two cameras, extracts the features that are necessary for handling the fabric despite possible deformations or occlusion from the robotic arm. An intelligent controller based on visual servoing is implemented enabling the robot to handle a variety of fabrics without the need for a mathematical model or complex mathematical/geometrical computations. To enhance its performance, the conventional fuzzy logic controller is tuned through genetic algorithms and an adaptation mechanism and the respective performance is evaluated. The experiments show that the proposed robotic system is flexible enough to handle various fabrics and robust in handling deformations that may change fabric's shape due to buckling.
Findings
The experiments show that the proposed robotic system is flexible enough to handle various fabrics and robust in handling deformations that may change fabric's shape due to buckling.
Research limitations/implications
It is not possible to cover all the aspects of robot handling of flexible materials in this paper, since there are still several related issues requiring solutions. Considering the future research work, the proposed approach can be extended to sew fabrics with curved edges and correcting the distortions presented during robot handling of fabrics.
Practical implications
The paper includes implications for robot handling a variety of fabrics with low and medium bending rigidity on a working table. The intent of this paper deals with buckling in context of achieving a successful seam tracking and not the correction strategy against folding or wrinkling problems.
Originality/value
This paper fulfils an identified need to study the fabrics' behavior towards robot handling on a working table.
Details
Keywords
Mehran Esmaeili, Hossein Shayeghi, Hamid Mohammad Nejad and Abdollah Younesi
This paper aims to propose an improved reinforcement learning-based fuzzy-PID controller for load frequency control (LFC) of an island microgrid.
Abstract
Purpose
This paper aims to propose an improved reinforcement learning-based fuzzy-PID controller for load frequency control (LFC) of an island microgrid.
Design/methodology/approach
To evaluate the performance of the proposed controller, three different types of controllers including optimal proportional-integral-derivative (PID) controller, optimal fuzzy PID controller and the proposed reinforcement learning-based fuzzy-PID controller are compared. Optimal PID controller and classic fuzzy-PID controller parameters are tuned using Non-dominated Sorting Genetic Algorithm-II algorithm to minimize overshoot, settling time and integral square error over a wide range of load variations. The simulations are carried out using MATLAB/SIMULINK package.
Findings
Simulation results indicated the superiority of the proposed reinforcement learning-based controller over fuzzy-PID and optimal-PID controllers in the same operational conditions.
Originality/value
In this paper, an improved reinforcement learning-based fuzzy-PID controller is proposed for LFC of an island microgrid. The main advantage of the reinforcement learning-based controllers is their hardiness behavior along with uncertainties and parameters variations. Also, they do not need any knowledge about the system under control; thus, they can control any large system with high nonlinearities.
Details
Keywords
Piotr Derugo and Krzysztof Szabat
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
Details
Keywords
The purpose of this paper is to reduce the strain and vibration during robotic machining.
Abstract
Purpose
The purpose of this paper is to reduce the strain and vibration during robotic machining.
Design/methodology/approach
An intelligent approach based on particle swarm optimization (PSO) and adaptive iteration algorithms is proposed to optimize the PD control parameters in accordance with robotic machining state.
Findings
The proposed intelligent approach can significantly reduce robotic machining strain and vibration.
Originality value
The relationship between robotic machining parameters is studied and the dynamics model of robotic machining is established. In view of the complexity of robotic machining process, the PSO and adaptive iteration algorithms are used to optimize the PD control parameters in accordance with robotic machining state. The PSO is used to optimize the PD control parameters during stable-machining state, and the adaptive iteration algorithm is used to optimize the PD control parameters during cut-into state.
Details
Keywords
Chittaranjan Paital, Saroj Kumar, Manoj Kumar Muni, Dayal R. Parhi and Prasant Ranjan Dhal
Smooth and autonomous navigation of mobile robot in a cluttered environment is the main purpose of proposed technique. That includes localization and path planning of mobile…
Abstract
Purpose
Smooth and autonomous navigation of mobile robot in a cluttered environment is the main purpose of proposed technique. That includes localization and path planning of mobile robot. These are important aspects of the mobile robot during autonomous navigation in any workspace. Navigation of mobile robots includes reaching the target from the start point by avoiding obstacles in a static or dynamic environment. Several techniques have already been proposed by the researchers concerning navigational problems of the mobile robot still no one confirms the navigating path is optimal.
Design/methodology/approach
Therefore, the modified grey wolf optimization (GWO) controller is designed for autonomous navigation, which is one of the intelligent techniques for autonomous navigation of wheeled mobile robot (WMR). GWO is a nature-inspired algorithm, which mainly mimics the social hierarchy and hunting behavior of wolf in nature. It is modified to define the optimal positions and better control over the robot. The motion from the source to target in the highly cluttered environment by negotiating obstacles. The controller is authenticated by the approach of V-REP simulation software platform coupled with real-time experiment in the laboratory by using Khepera-III robot.
Findings
During experiments, it is observed that the proposed technique is much efficient in motion control and path planning as the robot reaches its target position without any collision during its movement. Further the simulation through V-REP and real-time experimental results are recorded and compared against each corresponding results, and it can be seen that the results have good agreement as the deviation in the results is approximately 5% which is an acceptable range of deviation in motion planning. Both the results such as path length and time taken to reach the target is recorded and shown in respective tables.
Originality/value
After literature survey, it may be said that most of the approach is implemented on either mathematical convergence or in mobile robot, but real-time experimental authentication is not obtained. With a lack of clear evidence regarding use of MGWO (modified grey wolf optimization) controller for navigation of mobile robots in both the environment, such as in simulation platform and real-time experimental platforms, this work would serve as a guiding link for use of similar approaches in other forms of robots.
Details
Keywords
Chaoquan Li, Xueshan Gao, Qiang Huang, Fuquan Dai, Jie Shao, Yang Bai and Kejie Li
The purpose of this paper is to introduce a high load capacity coaxial couple wheeled robot (CCWR) and investigate a simple structure but effective fuzzy equilibrium controller…
Abstract
Purpose
The purpose of this paper is to introduce a high load capacity coaxial couple wheeled robot (CCWR) and investigate a simple structure but effective fuzzy equilibrium controller based on (Takagi‐Sugeno) T‐S for balance control in wide‐angle range.
Design/methodology/approach
By selecting the robot inclination angle and angular rate as input variables and the DC motors' rotation speed as output variables, a T‐S fuzzy controller (FC) is established.
Findings
Simplified robot dynamic equilibrium equations are feasible; the robot balance in wide‐angle range could be controlled by the T‐S FC. Despite the existence of small vibrations near the equilibrium position, the system can return to equilibrium within 3 s, showing strong robustness.
Practical implications
The robot can achieve self‐balance and pivot around, moreover, it provides a new way for balance control of CCWR in wide‐angle range. And at the same time, the robot can achieve its work in semi‐autonomous and tele‐operated mode.
Originality/value
The paper shows that designing the controller based on static analysis is feasible; simple structure T‐S fuzzy control way is introduced to balance control for CCWR in a wide angle scale; the development target is to provide a kind of robot platform for testing control algorithms or a personal transporter, and the project is supported by the High Technology Research and Development Program of China.
Details