Fuzzy Compensator Research Articles

Adaptive Dynamic Surface Control of MEMS Gyroscope Sensor Using Fuzzy Compensator

A novel adaptive method for the micro-electro mechanical systems (MEMSs) gyroscope based on a dynamic surface control and combined with the approach of adaptive fuzzy and sliding mode control (SMC) was shown in this paper. The first-order filter was introduced to the conventional adaptive backstepping technique in the dynamic surface control, which not only maintains the advantage of original backstepping technique, but also reduces the number of parameters and avoids the problem of parameter expansion. The adaptive fuzzy logic system is employed to approximate the dynamic characteristics of the gyroscope; the SMC is a kind of compensation for the error of the fuzzy approximation, which reduced the chattering phenomenon in adaptive control significantly. The proposed control scheme has improved the dynamic characteristics of the gyroscope, reducing the chattering of inputs and improving the timeliness and effectiveness of tracking. Simulation results indicate that the proposed control scheme has superior performance compared with conventional SMC.

Direct Adaptive Fuzzy Sliding Mode Control for Under-actuated Uncertain Systems

The development of the control algorithms for under-actuated systems is important. Decoupled sliding mode control has been successfully employed to control under-actuated systems in a decoupling manner with the use of sliding mode control. However, in such a control scheme, the system functions must be known. If there are uncertainties in those functions, the control performance may not be satisfactory.In this paper, the direct adaptive fuzzy sliding mode control is employed to control a class of under-actuated uncertain systems which can be regarded as a combination of several subsystems with one same control input. By using the hierarchical sliding control approach, a sliding control law is derived so as to make every subsystem stabilized at the same time. But, since the system considered is assumed to be uncertain, the sliding control law cannot be readily facilitated. Therefore, in the study, based on Lyapunov stable theory a fuzzy compensator is proposed to approximate the uncertain part of the sliding control law. From those simulations, it can be concluded that the proposed compensator can indeed cope with system uncertainties. Besides, it can be found that the proposed compensator also provide good robustness properties.

Incorporating Human Perception With the Motion Washout Filter Using Fuzzy Logic Control

Nowadays, classical washout filters are extensively used in commercial motion simulators. Even though there are several advantages for classical washout filters, such as short processing time, simplicity and ease of adjustment, they have several shortcomings. The main disadvantage is the fixed scheme and parameters of the classical washout filter cause inflexibility of the structure and thus the resulting simulator fails to suit all circumstances. Moreover, it is a conservative approach and the platform cannot be fully exploited. The aim of this research is to present a fuzzy logic approach and take the human perception error into account in the classical motion cueing algorithm, in order to improve both the physical limits of restitution and realistic human sensations. The fuzzy compensator signal is applied to adjust the filtered signals on the longitudinal and rotational channels online, as well as the tilt coordination to minimize the vestibular sensation error below the human perception threshold. The results indicate that the proposed fuzzy logic controllers significantly minimize the drawbacks of having fixed parameters and conservativeness in the classical washout filter. In addition, the performance of motion cueing algorithm and human perception for most occasions is improved.

Indoor Intelligent Mobile Robot Localization Using Fuzzy Compensation and Kalman Filter to Fuse the Data of Gyroscope and Magnetometer

This paper develops an indoor intelligent service mobile robot that has multiple functions, can recognize and grip the target object, avoid obstacles, and accurately localize via relative position. The locating method of the robot uses the output values of the sensor module, which includes data from a gyroscope and a magnetometer, to correct the current rotation direction angle of the robot. An angle correction method can be divided into three parts. The first part calculates the angle values obtained from the gyroscope and the magnetometer that are installed on the robot. The second part explores the error characteristics between the sensor module and the actual rotation direction angle of the robot. The third part uses the error characteristic data to design the fuzzy rule base and the Kalman filter to eliminate errors and to get a more accurate orientation angle. These errors can be described as either regular or irregular. The former can be eliminated by fuzzy algorithm compensation, and the latter can be eliminated by the Kalman filter. The contribution of this paper is to propose an error correction method between the calculus rotation angle determined by the sensor and the actual rotation angle of the robot such that the three moving paths, i.e., specified, actual, and calculus paths, have more accurate approximation. The experimental results demonstrate that the combination of fuzzy compensation and the Kalman filter is an accurate correction method.

Uncertain Robotic Manipulator System’s Tracking Control Based on Fuzzy Adaptive Method

The paper puts forward a control scheme by a computed torque controller plus a fuzzy compensator concerning the issue of tracking control of uncertain robotic manipulator. Computed torque controller is used for the nominal system and the fuzzy compensator is used to control the uncertain system. The parameters of fuzzy compensator are adaptively adjusted based on Lynapunov stability theory. The proposed scheme can guarantee the asymptotical stabilization of close-loop system. Simulation results are included to demonstrate the effectiveness and feasibility of our developed method.

GSA-based optimal backstepping controller with a fuzzy compensator for robust control of an autonomous quadrotor UAV

Purpose – The purpose of this paper is to propose a new approach for robust control of an autonomous quadrotor unmanned aerial vehicle (UAV) in automatic take-off, hovering and landing mission and also to improve the stabilizing performance of the quadrotor with inherent time-varying disturbance. Design/methodology/approach – First, the dynamic model of the aerial vehicle is mathematically formulated. Then, a combination of a nonlinear backstepping scheme with the intelligent fuzzy system as a new key idea to generate a robust controller is designed for the stabilization and altitude tracking of the vehicle. For the problem of determining the backstepping control parameters, a new heuristic algorithm, namely, Gravitational Search Algorithm has been used. Findings – The control law design utilizes the backstepping control methodology that uses Lyapunov function which can guarantee the stability of the nominal model system, whereas the intelligent system is used as a compensator to attenuate the effects cau...

Microcontroller-Based B-Spline Neural Position Control for Voice Coil Motors

Since the system dynamic of a voice coil motor (VCM) is difficult to obtain, this paper uses a B-spline neural network to online approximate an unknown nonlinear term in the system dynamics of a VCM by tuning its interior parameters. Meanwhile, a B-spline neural position control (BNPC) system, which is composed of a position controller and a fuzzy compensator, is proposed. A proportional-integral adaptation law is derived to speed up the convergence of tracking error in the sense of the projection algorithm and Lyapunov stability theorem. Finally, the proposed BNPC system is implemented on a 32-b microcontroller. The experimental results show that the proposed BNPC system is robust against payload variations with high accuracy motion response.

Optimized Sensorless Antivibration Control for Semiactive Suspensions With Cosimulation Analysis

This paper proposes an optimized sensorless control methodology to design semiactive controllers for vehicle vibration control. The semiactive suspension model including filtered feedback scheme and actuator dynamics is first investigated. Furthermore, to estimate the immeasurable states for feedback control, an observer considering practical applications is introduced, while the exponential convergence and the stability of the overall system are guaranteed. A direct fuzzy compensation scheme with genetic optimization is used to release the constraint requiring, in advance, the precise upper bound of system uncertainties in the control gain design. With the cosimulation approach, the multibody virtual prototype of a sedan with the proposed system is tested under various road conditions in a near real environment. The cosimulation data and results for a full-car semiactive suspension system are provided to validate the effectiveness of the proposed control system.

Adaptive fuzzy fractional-order sliding mode controller for a class of dynamical systems with uncertainty

A sliding mode control (SMC) strategy is proposed for a second-order non-linear system using fractional calculus and adaptive fuzzy compensator, named fuzzy fractional-order SMC (FFOSMC). The major contribution of this research includes the formulation of a robust fractional-order controller based on a novel non-integer sliding manifold. The proposed controller utilizes reduced discontinuous switching gain using a fuzzy logic system. A fractional-order adaptation law is derived to update the fuzzy logic system, which has more degrees of freedom to achieve the desired performance. The stability of the closed-loop system is guaranteed using fractional-order Lyapunov theory. The parameters of the proposed control method are optimized using Simulink response optimization. Finally, the performance of the FFOSMC is fully investigated and compared with a conventional SMC controller and an integral SMC controller. From simulation results, it is concluded that the proposed algorithm is effective and more robust.

Enhancement the Maneuverability of Tele-Hydraulic System Using Fuzzy Friction Compensator

One degree of freedom (DOF) tele-hydraulic manipulation using force reflecting bilateral control will be investigated. The primary objective in the development of a simple one DOF experimental rig was to allow the investigation of different tele-control strategies prior to their implementation on the eight DOF tele-hydraulic excavator system. Because of the friction nonlinearity characteristic in hydraulic system, high steady state error and overshoot have occurred in the position response as a typical imperfect implementation of associated control dynamic. In this situation, there is a necessity to be able to effectively utilizing the intelligent friction compensated controller. This fuzzy compensator combined with fuzzy controller will be implemented to enhance the maneuverability performance. Experiments were carried out and the experimental results illustrated that the above compensated intelligent framework can improve the bilateral performance

Implementation of an Object-Grasping Robot Arm Using Stereo Vision Measurement and Fuzzy Control

In this paper, a method using a stereo vision device and fuzzy control to guide a robot arm to grasp a target object is proposed. The robot arm has five degrees of freedom including a gripper and four joints. The stereo vision device located beside the arm captures images of the target and the gripper. Image processing techniques such as color space transformation, morphologic operation, and 3-D position measurement are used to identify the target object and the gripper from the captured images and estimate their relative positions. Based on the estimated positions of the gripper and the target, the gripper can approach and grasp the target using inverse kinematics. However, since the robot arm’s accuracy of movement may be affected by gearbox backlash or hardware uncertainty, the gripper might not approach the desired position with precision using only inverse kinematics. Therefore, a fuzzy compensation method is added to correct any position errors between the gripper and target such that the gripper can grasp the target. Using the proposed method, the stereo vision device can not only locate the target object but also trace the position of the robot arm until the target object is grasped. Finally, some experiments are conducted to demonstrate successful implementation of the proposed method on the robot arm control.

Self-constructing wavelet neural network algorithm for nonlinear control of large structures

An adaptive control algorithm is presented for nonlinear vibration control of large structures subjected to dynamic loading. It is based on integration of a self-constructing wavelet neural network (SCWNN) developed specifically for structural system identification with an adaptive fuzzy sliding mode control approach. The algorithm is particularly suitable when the physical properties such as the stiffnesses and damping ratios of the structural system are unknown or partially known which is the case when a structure is subjected to an extreme dynamic event such as an earthquake as the structural properties change during the event. SCWNN is developed for functional approximation of the nonlinear behavior of large structures using neural networks and wavelets. In contrast to earlier work, the identification and control are processed simultaneously which makes the resulting adaptive control more applicable to real life situations. A two-part growing and pruning criterion is developed to construct the hidden layer in the neural network automatically. A fuzzy compensation controller is developed to reduce the chattering phenomenon. The robustness of the proposed algorithm is achieved by deriving a set of adaptive laws for determining the unknown parameters of wavelet neural networks using two Lyapunov functions. No offline training of neural network is necessary for the system identification process. In addition, the earthquake signals are considered as unidentified. This is particularly important for on-line vibration control of large civil structures since the external dynamic loading due to earthquake is not available in advance. The model is applied to vibration control of a continuous cast-in-place prestressed concrete box-girder bridge benchmark problem seismically excited highway.

각도 오프셋의 퍼지보상을 통한 외바퀴 이동 로봇의 균형제어

In this paper, a fuzzy control method is used for balancing a single-wheel robot. A single-wheel robot controlled by the PD control method becomes easily unstable since the flywheel tends to lean against one direction. In the previous research, we have used the gain scheduling method. To remedy this problem, in this paper, a fuzzy compensation technique is proposed to compensate for the balancing angle. The fuzzy control method compensates offset values at the balancing angle to prevent the gimbal from falling against one direction. Experimental studies of the balancing control performance of a single-wheel mobile robot validate the proposed control method.

Optimized Adaptive Motion Control Through an SoPC Implementation for Linear Induction Motor Drives

This paper proposes an optimized adaptive tracking control for a linear induction motor (LIM) drive taking into account the unknown end effects, payload, and uncertainties including the friction force. The dynamic model of a field-oriented LIM drive with the primary end effect is first investigated. On the basis of the backstepping control design, a sliding mode controller embedded with a practical fuzzy compensator is developed to confront the lumped uncertainties of the LIM drive. Moreover, to overcome the obstacle of the unknown bound of the lumped uncertainties in the overall system, an adaptive mechanism based on the sense of the Lyapunov stability theorem is derived to online adjust the fuzzy compensation gains. Moreover, to achieve the robust tracking performance in the presence of the difficulty arisen from the LIM drive accompanied with uncertain nonlinearities and/or external disturbances, the soft-computing technique is adopted for optimizing the designed controller parameters. A system-on-programmable-chip is employed to implement the developed system for the reason of raising the benefits of fast-prototyping and high-performance in industrial applications. The high performance of the proposed control scheme is validated through comparative experimental results.

이동 로봇 군집 제어를 위한 퍼지 보상 PID제어기

In this paper, a fuzzy compensated PID control system is proposed for formation control of mobile robots. The control system consists of a kinematic controller based on the leader-follower approach and a dynamic controller to handle dynamics effects of mobile robots. To maintain the desired formation of mobile robots, the dynamic controller is equipped with a PID controller; however, the PID controller has poor performance in nonlinear and changing environments. In order to improve these problem, we applied the additional fuzzy compensator. Finally, the proposed control system has been evaluated through computer simulation to demonstrate the improved results.

Design of a Multi Objective Fuzzy- Proportional Compensator for a Power-split Hybrid Electric Vehicle

In this research the model of the Toyota Prius vehicle as a power-split Hybrid Electric Vehicle is chosen from ADVISOR and the information required about a vehicle and drive cycle are extracted from it. In order to have better energy management, a fuzzy compensator is used beside the planetary gear set, which is responsible for sharing power between energy sources. This compensator helps the planetary gear set on supplying the requested speed and torque from final drive and better sharing power between energy sources considering how the requested power instantaneous changes are. Also to provide the requested speed and torque in a better way a P controller is used. At the end, the results of implementation of this controller are compared to those of the primary model. The results show that the designed fuzzy rule based compensator is able to decrease the driving costs.

Energy Management in Series-Parallel Hybrid Electric Vehicles Using Fuzzy Logic Controller

This paper describes and introduces a nonlinear model for series-parallel Toyota Prius hybrid electric vehicle (HEV). Required information of driving cycle has been extracted with using Advisor simulation software. Due to optimum power split, we proposed a fuzzy logic compensator that has the duty to split power between energy sources. This compensator help set of planetary gears in order to achieve that goal in accordance to instantaneous power changing. The simulation results of the control strategy, it turns out that our proposed approach raises the efficiency of costs.

A Fuzzy PI Speed Controller based on Feedback Compensation Strategy for PMSM

in order to solve the problem of robustness or anti-disturbance of the traditional PI speed controller in the permanent magnet synchronous motor. A fuzzy PI speed controller based on load torque feedback compensation is proposed for the permanent magnet synchronous motor. The combination of fuzzy PI control strategy and load feedback compensation method can enhance the robustness and disturbance rejection of the speed loop. According to the validated results of simulation and experiments, by using this PMSM speed controller, the robustness of the system speed control was enhanced markedly, and the capacity of anti-disturbance was also improved significantly.

Fuzzy robust nonlinear control approach for electro-hydraulic flight motion simulator

A fuzzy robust nonlinear controller for hydraulic rotary actuators in flight motion simulators is proposed. Compared with other three-order models of hydraulic rotary actuators, the proposed controller based on first-order nonlinear model is more easily applied in practice, whose control law is relatively simple. It not only does not need high-order derivative of desired command, but also does not require the feedback signals of velocity, acceleration and jerk of hydraulic rotary actuators. Another advantage is that it does not rely on any information of friction, inertia force and external disturbing force/torque, which are always difficult to resolve in flight motion simulators. Due to the special composite vane seals of rectangular cross-section and goalpost shape used in hydraulic rotary actuators, the leakage model is more complicated than that of traditional linear hydraulic cylinders. Adaptive multi-input single-output (MISO) fuzzy compensators are introduced to estimate nonlinear uncertain functions about leakage and bulk modulus. Meanwhile, the decomposition of the uncertainties is used to reduce the total number of fuzzy rules. Different from other adaptive fuzzy compensators, a discontinuous projection mapping is employed to guarantee the estimation process to be bounded. Furthermore, with a sufficient number of fuzzy rules, the controller theoretically can guarantee asymptotic tracking performance in the presence of the above uncertainties, which is very important for high-accuracy tracking control of flight motion simulators. Comparative experimental results demonstrate the effectiveness of the proposed algorithm, which can guarantee transient performance and better final accurate tracking in the presence of uncertain nonlinearities and parametric uncertainties.

Kinematics Analysis of Serial-Parallel Hybrid Humanoid Robot in Reaching Movement

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00260005/07.jpg"" width=""300"" />Serial-parallel hybrid robot</div> As the demand to the practical operation ability of humanoid robots improving, serial-parallel hybrid structure has started to be used in the research of humanoid robot. However, analytical solutions of the forward and inverse kinematics are hard to obtain due to the redundancy of humanoid robot and the complex topology of hybrid structure. Besides, humanoid robots should have the capability to complete complicated operation in the low structured task workspace. So, it should have not only the humanoid shape but also the behavior that adapts to the direct interaction with people. In this work, a novel 4-DOF hybrid humanoid robot was taken as an example, the forward kinematics equation was derived, the theory of screws and lie group and lie algebra were applied to calculate the end velocity. A numerical calculation method based on neurophysiology sensorimotor transformation model and fuzzy logic compensator was proposed to solve the inverse kinematics in reaching movement. The proposed strategy can make the robot execute reaching movements with human-like posture. Simulation and test results validate the effectiveness respectively. </span>