9,896 research outputs found
Adaptive MIMO Fuzzy Compensate Fuzzy Sliding Mode Algorithm: Applied to Second Order Nonlinear System.
This research is focused on proposed adaptive fuzzy sliding mode algorithms with the adaptation laws
derived in the Lyapunov sense. The stability of the closed-loop system is proved mathematically based on
the Lyapunov method. Adaptive MIMO fuzzy compensate fuzzy sliding mode method design a MIMO fuzzy
system to compensate for the model uncertainties of the system, and chattering also solved by linear
saturation method. Since there is no tuning method to adjust the premise part of fuzzy rules so we
presented a scheme to online tune consequence part of fuzzy rules. Classical sliding mode control is
robust to control model uncertainties and external disturbances. A sliding mode method with a switching
control low guarantees the stability of the certain and/or uncertain system, but the addition of the switching
control low introduces chattering into the system. One way to reduce or eliminate chattering is to insert a
boundary layer method inside of a boundary layer around the sliding surface. Classical sliding mode
control method has difficulty in handling unstructured model uncertainties. One can overcome this problem
by combining a sliding mode controller and artificial intelligence (e.g. fuzzy logic). To approximate a timevarying
nonlinear dynamic system, a fuzzy system requires a large amount of fuzzy rule base. This large
number of fuzzy rules will cause a high computation load. The addition of an adaptive law to a fuzzy sliding
mode controller to online tune the parameters of the fuzzy rules in use will ensure a moderate
computational load. The adaptive laws in this algorithm are designed based on the Lyapunov stability
theorem. Asymptotic stability of the closed loop system is also proved in the sense of Lyapunov
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Novel fuzzy logic controllers with self-tuning capability
Two controllers which extend the PD+I fuzzy logic controller to deal with the plant having time varying nonlinear dynamics are proposed. The adaptation ability of the first self tuning PD+I fuzzy logic controller (STPD+I_31) is achieved by adjusting the output scaling factor automatically thereby contributing to significant improvement in performance. Second controller (STPD+I_9) is the simplified version of STPD+I_31 which is designed under the imposed constraint that allows only minimum number of rules in the rule bases. The proposed controllers are compared with two classical nonlinear controllers: the pole placement self tuning PID controller and sliding mode controller. All the controllers are applied to the two-links revolute robot for the tracking control. The tracking performance of STPD+I_31 and STPD+I_9 are much better than the pole placement self tuning PID controller during high speed motions while the performance are comparable at low and medium speed. In addition, STPD+I_31 and STPD+I_9 outperform sliding mode controller using same method of comparison study
A comparative performance analysis based on artificial intelligence techniques applied to three-phase induction motor drives
In this work, we introduced a new robust hybrid control to an induction motor (IM), based on the theory of fuzzy logic and variable structure with sliding-mode control (SMC). As the variations of both control system parameters and operating conditions occur, the conventional control methods may not be satisfied further. Fuzzy tuning schemes are employed to improve control performance and to reduce chattering in the sliding mode. The combination of these two theories has given high performance and fast dynamic response with no overshoot. As it is very robust, it is insensitive to process parameters variation and external disturbances
Sliding Mode Control and Vision-Based Line Tracking for Quadrotors
This thesis describes the design of Sliding Mode Control applied to quadrotor UAV flight. This is a nonlinear control technique in which a discontinuous control signal is applied to drive the so-called sliding variable to zero, which defines the sliding surface. The sliding variable should be designed in such a way that approaching the sliding surface is beneficial to tracking the reference signals. The advantages of Sliding Mode Control are that the need for simplifying the underlying dynamical model through linearization is avoided, it is robust and adaptive, and works even if the system to be controlled is highly nonlinear or has model uncertainties. Sliding Mode Control has one major issue associated with it, namely the chattering phenomena in the control inputs, which is undesirable. This can be tackled by approximating the discontinuous sign function in the control input with a approximated continuous function, or by applying techniques such as adaptive fuzzy gain scheduling. As with other control methods, Sliding Mode Control requires tuning of the control parameters to obtain an optimal performance. In this work, genetic algorithms were investigated as a way to tune the controller parameters. The findings of this thesis were combined with the design of a line tracking algorithm in order to enter the MathWorks Minidrone Competition.This thesis describes the design of Sliding Mode Control applied to quadrotor UAV flight. This is a nonlinear control technique in which a discontinuous control signal is applied to drive the so-called sliding variable to zero, which defines the sliding surface. The sliding variable should be designed in such a way that approaching the sliding surface is beneficial to tracking the reference signals. The advantages of Sliding Mode Control are that the need for simplifying the underlying dynamical model through linearization is avoided, it is robust and adaptive, and works even if the system to be controlled is highly nonlinear or has model uncertainties. Sliding Mode Control has one major issue associated with it, namely the chattering phenomena in the control inputs, which is undesirable. This can be tackled by approximating the discontinuous sign function in the control input with a approximated continuous function, or by applying techniques such as adaptive fuzzy gain scheduling. As with other control methods, Sliding Mode Control requires tuning of the control parameters to obtain an optimal performance. In this work, genetic algorithms were investigated as a way to tune the controller parameters. The findings of this thesis were combined with the design of a line tracking algorithm in order to enter the MathWorks Minidrone Competition
Sliding Mode Control and Vision-Based Line Tracking for Quadrotors
This thesis describes the design of Sliding Mode Control applied to quadrotor UAV flight. This is a nonlinear control technique in which a discontinuous control signal is applied to drive the so-called sliding variable to zero, which defines the sliding surface. The sliding variable should be designed in such a way that approaching the sliding surface is beneficial to tracking the reference signals. The advantages of Sliding Mode Control are that the need for simplifying the underlying dynamical model through linearization is avoided, it is robust and adaptive, and works even if the system to be controlled is highly nonlinear or has model uncertainties. Sliding Mode Control has one major issue associated with it, namely the chattering phenomena in the control inputs, which is undesirable. This can be tackled by approximating the discontinuous sign function in the control input with a approximated continuous function, or by applying techniques such as adaptive fuzzy gain scheduling. As with other control methods, Sliding Mode Control requires tuning of the control parameters to obtain an optimal performance. In this work, genetic algorithms were investigated as a way to tune the controller parameters. The findings of this thesis were combined with the design of a line tracking algorithm in order to enter the MathWorks Minidrone Competition.This thesis describes the design of Sliding Mode Control applied to quadrotor UAV flight. This is a nonlinear control technique in which a discontinuous control signal is applied to drive the so-called sliding variable to zero, which defines the sliding surface. The sliding variable should be designed in such a way that approaching the sliding surface is beneficial to tracking the reference signals. The advantages of Sliding Mode Control are that the need for simplifying the underlying dynamical model through linearization is avoided, it is robust and adaptive, and works even if the system to be controlled is highly nonlinear or has model uncertainties. Sliding Mode Control has one major issue associated with it, namely the chattering phenomena in the control inputs, which is undesirable. This can be tackled by approximating the discontinuous sign function in the control input with a approximated continuous function, or by applying techniques such as adaptive fuzzy gain scheduling. As with other control methods, Sliding Mode Control requires tuning of the control parameters to obtain an optimal performance. In this work, genetic algorithms were investigated as a way to tune the controller parameters. The findings of this thesis were combined with the design of a line tracking algorithm in order to enter the MathWorks Minidrone Competition
Sliding Mode Control and Vision-Based Line Tracking for Quadrotors
This thesis describes the design of Sliding Mode Control applied to quadrotor UAV flight. This is a
nonlinear control technique in which a discontinuous control signal is applied to drive the so-called
sliding variable to zero, which defines the sliding surface. The sliding variable should be designed in
such a way that approaching the sliding surface is beneficial to tracking the reference signals. The
advantages of Sliding Mode Control are that the need for simplifying the underlying dynamical
model through linearization is avoided, it is robust and adaptive, and works even if the system to be
controlled is highly nonlinear or has model uncertainties. Sliding Mode Control has one major issue
associated with it, namely the chattering phenomena in the control inputs, which is undesirable.
This can be tackled by approximating the discontinuous sign function in the control input with a
approximated continuous function, or by applying techniques such as adaptive fuzzy gain scheduling.
As with other control methods, Sliding Mode Control requires tuning of the control parameters
to obtain an optimal performance. In this work, genetic algorithms were investigated as a way to
tune the controller parameters. The findings of this thesis were combined with the design of a line
tracking algorithm in order to enter the MathWorks Minidrone Competition.This thesis describes the design of Sliding Mode Control applied to quadrotor UAV flight. This is a
nonlinear control technique in which a discontinuous control signal is applied to drive the so-called
sliding variable to zero, which defines the sliding surface. The sliding variable should be designed in
such a way that approaching the sliding surface is beneficial to tracking the reference signals. The
advantages of Sliding Mode Control are that the need for simplifying the underlying dynamical
model through linearization is avoided, it is robust and adaptive, and works even if the system to be
controlled is highly nonlinear or has model uncertainties. Sliding Mode Control has one major issue
associated with it, namely the chattering phenomena in the control inputs, which is undesirable.
This can be tackled by approximating the discontinuous sign function in the control input with a
approximated continuous function, or by applying techniques such as adaptive fuzzy gain scheduling.
As with other control methods, Sliding Mode Control requires tuning of the control parameters
to obtain an optimal performance. In this work, genetic algorithms were investigated as a way to
tune the controller parameters. The findings of this thesis were combined with the design of a line
tracking algorithm in order to enter the MathWorks Minidrone Competition
Fuzzy sliding mode control of an offshore container crane
© 2017 A fuzzy sliding mode control strategy for offshore container cranes is investigated in this study. The offshore operations of loading and unloading containers are performed between a mega container ship, called the mother ship, and a smaller ship, called the mobile harbor (MH), which is equipped with a container crane. The MH is used to transfer the containers, in the open sea, and deliver them to a conventional stevedoring port, thereby minimizing the port congestion and also eliminating the need of expanding outwards. The control objective during the loading and unloading process is to keep the payload in a desired tolerance in harsh conditions of the MH motion. The proposed control strategy combines a fuzzy sliding mode control law and a prediction algorithm based on Kalman filtering for the MH roll angle. Here, the sliding surface is designed to incorporate the desired trolley trajectory while suppressing the sway motion of the payload. To improve the control performance, the discontinuous gain of the sliding control is adjusted with fuzzy logic tuning schemes with respect to the sliding function and its rate of change. Chattering is further reduced by a saturation function. Simulation and experimental results are provided to verify the effectiveness of the proposed control system for offshore container cranes
Self organizing fuzzy sliding mode controller for the position control of a permanent magnet synchronous motor drive
AbstractIn this paper, a self organizing fuzzy sliding mode controller (SOFSMC) which emulates the fuzzy controller with gain auto-tuning is proposed for a permanent magnet synchronous motor (PMSM) drive. The proposed controller is used for the position control of the PMSM drive. The performance and robustness of the control system is tested for nonlinear motor load torque disturbance and parameter variations. It has a novel gain self organizing strategy in response to the transient or tracking responses requirement. To illustrate the performance of the proposed controller, the simulation studies are presented separately for the SOFSMC and the fuzzy controller with gain auto-tuning. The results are compared with each other and discussed in detail. Simulation results showing the effectiveness of the proposed control system are confirmed under the different position changes
Type-2 Fuzzy Hybrid Controller Network for Robotic Systems
Dynamic control, including robotic control, faces both the theoretical challenge of obtaining accurate system models and the practical difficulty of defining uncertain system bounds. To facilitate such challenges, this paper proposes a control system consisting of a novel type of fuzzy neural network and a robust compensator controller. The new fuzzy neural network is implemented by integrating a number of key components embedded in a Type-2 fuzzy cerebellar model articulation controller (CMAC) and a brain emotional learning controller (BELC) network, thereby mimicking an ideal sliding mode controller. The system inputs are fed into the neural network through a Type-2 fuzzy inference system (T2FIS), with the results subsequently piped into sensory and emotional channels which jointly produce the final outputs of the network. That is, the proposed network estimates the nonlinear equations representing the ideal sliding mode controllers using a powerful compensator controller with the support of T2FIS and BELC, guaranteeing robust tracking of the dynamics of the controlled systems. The adaptive dynamic tuning laws of the network are developed by exploiting the popular brain emotional learning rule and the Lyapunov function. The proposed system was applied to a robot manipulator and a mobile robot, demonstrating its efficacy and potential; and a comparative study with alternatives indicates a significant improvement by the proposed system in performing the intelligent dynamic control
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