290 research outputs found
H∞ Robust T-S Fuzzy Design for Uncertain Nonlinear Systems with State Delays Based on Sliding Mode Control
This paper presents the fuzzy design of sliding mode control (SMC) for nonlinear systems with state delay, which can be represented by a Takagi-Sugeno (TS) model with uncertainties. There exist the parameter uncertainties in both the state and input matrices, as well as the unmatched external disturbance. The key feature of this work is the integration of SMC method with H∞ technique such that the robust asymptotically stability with a prescribed disturbance attenuation level γ can be achieved. A sufficient condition for the existence of the desired SMC is obtained by solving a set of linear matrix inequalities (LMIs). The reachability of the specified switching surface is proven. Simulation results show the validity of the proposed method
Review and Analysis on Main Technology of Exoskeletal Robot System for Upper Limbs Rehabilitation
Major function of exoskeletal robot system for upper limbs rehabilitation is to assist patient to carry out upper limbs’ rehabilitation training. Main technology of exoskeletal robot system for upper limbs rehabilitation includes design of mechanical structure of exoskeletal robot, design of control system of exoskeletal robot and implemention of data and information transmission between exoskeletal robot and upper limbs of human body. Currently implemention of data and information transmission rely mainly on methods of acquiring sEMG signal and force feedback. Reviewing and analyzing the specific technical development and deficiency in field of exoskeletal robot system for upper limbs rehabilitation will be important way in improving and upgrading the technology in future
Adaptive dynamic CMAC neural control of nonlinear chaotic systems with L2 tracking performance
[[abstract]]The advantage of using cerebellar model articulation control (CMAC) network has been well documented in many applications. However, the structure of a CMAC network which will influence the learning performance is difficult to select. This paper proposes a dynamic structure CMAC network (DSCN) which the network structure can grow or prune systematically and their parameters can be adjusted automatically. Then, an adaptive dynamic CMAC neural control (ADCNC) system which is composed of a computation controller and a robust compensator is proposed via second-order sliding-mode approach. The computation controller containing a DSCN identifier is the principal controller and the robust compensator is designed to achieve L2 tracking performance with a desired attenuation level. Moreover, a proportional–integral (PI)-type adaptation learning algorithm is derived to speed up the convergence of the tracking error in the sense of Lyapunov function and Barbalat’s lemma, thus the system stability can be guaranteed. Finally, the proposed ADCNC system is applied to control a chaotic system. The simulation results are demonstrated that the proposed ADCNC scheme can achieve a favorable control performance even under the variations of system parameters and initial point.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子
Adaptive Backstepping Control for Fractional-Order Nonlinear Systems with External Disturbance and Uncertain Parameters Using Smooth Control
In this paper, we consider controlling a class of single-input-single-output
(SISO) commensurate fractional-order nonlinear systems with parametric
uncertainty and external disturbance. Based on backstepping approach, an
adaptive controller is proposed with adaptive laws that are used to estimate
the unknown system parameters and the bound of unknown disturbance. Instead of
using discontinuous functions such as the function, an
auxiliary function is employed to obtain a smooth control input that is still
able to achieve perfect tracking in the presence of bounded disturbances.
Indeed, global boundedness of all closed-loop signals and asymptotic perfect
tracking of fractional-order system output to a given reference trajectory are
proved by using fractional directed Lyapunov method. To verify the
effectiveness of the proposed control method, simulation examples are
presented.Comment: Accepted by the IEEE Transactions on Systems, Man and Cybernetics:
Systems with Minor Revision
An Improved Fuzzy Brain Emotional Learning Model Network Controller for Humanoid Robots
The brain emotional learning (BEL) system was inspired by the biological amygdala-orbitofrontal model to mimic the high speed of the emotional learning mechanism in the mammalian brain, which has been successfully applied in many real-world applications. Despite of its success, such system often suffers from slow convergence for online humanoid robotic control. This paper presents an improved fuzzy BEL model (iFBEL) neural network by integrating a fuzzy neural network (FNN) to a conventional BEL, in an effort to better support humanoid robots. In particular, the system inputs are passed into a sensory and emotional channels that jointly produce the final outputs of the network. The non-linear approximation ability of the iFBEL is achieved by taking the BEL network as the emotional channel. The proposed iFBEL works with a robust controller in generating the hand and gait motion of a humanoid robot. The updating rules of the iFBEL-based controller are composed of two parts, including a sensory channel followed by the updating rules of the conventional BEL model, and the updating rules of the FNN and the robust controller which are derived from the "Lyapunov" function. The experiments on a three-joint robot manipulator and a six-joint biped robot demonstrated the superiority of the proposed system in reference to a conventional proportional-integral-derivative controller and a fuzzy cerebellar model articulation controller, based on the more accurate and faster control performance of the proposed iFBEL
Design of Sliding Mode PID Controller with Improved reaching laws for Nonlinear Systems
In this thesis, advanced design technique in sliding mode control (SMC) is
presented with focus on PID (Proportional-Integral-Derivative) type Sliding
surfaces based Sliding mode control with improved power rate exponential
reaching law for Non-linear systems using Modified Particle Swarm Optimization
(MPSO). To handle large non-linearities directly, sliding mode controller based
on PID-type sliding surface has been designed in this work, where Integral term
ensures fast finite convergence time. The controller parameter for various
modified structures can be estimated using Modified PSO, which is used as an
offline optimization technique. Various reaching law were implemented leading
to the proposed improved exponential power rate reaching law, which also
improves the finite convergence time. To implement the proposed algorithm,
nonlinear mathematical model has to be decrypted without linearizing, and used
for the simulation purposes. Their performance is studied using simulations to
prove the proposed behavior. The problem of chattering has been overcome by
using boundary method and also second order sliding mode method. PI-type
sliding surface based second order sliding mode controller with PD surface
based SMC compensation is also proposed and implemented. The proposed
algorithms have been analyzed using Lyapunov stability criteria. The robustness
of the method is provided using simulation results including disturbance and
10% variation in system parameters. Finally process control based hardware is
implemented (conical tank system)
Sliding mode control of a 2-DOF manipulator with random base vibration based on modified exponential reaching law
To solve the precise position control problem of a two degree of freedom (2-DOF) manipulator with random base vibration, a sliding mode control method based on modified exponential reaching law is studied. The dynamic model of manipulator is established by using the second kind Lagrange equation. The nonlinear term generated by base random vibration is presented as external disturbance term. Based on dynamic models, the sliding mode control using improved exponential reaching law is applied in the manipulator system. It is verified by the simulation result that the control method can effectively suppress the influence of base random vibration, and bring the manipulator from a given initial state to a prescribed terminal state rapidly and precisely
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