This paper presents a method to compute optimal control strategies of discrete large scale nonlinear systems by using hierarchical fuzzy systems. The method is based on the decomposition principle of the global system...
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This paper presents a method to compute optimal control strategies of discrete large scale nonlinear systems by using hierarchical fuzzy systems. The method is based on the decomposition principle of the global system into interconnected subsystems becoming easier to study. Then, the differential dynamic programming procedure is applied in order to obtain the rule basis. After that, we construct limpid-hierarchical Mamdani fuzzy system in order to compute optimal control laws, for each subsystem. Simulation results of a rotary crane show that the proposed method yields to satisfactory performances. The robustness of the proposed approach is verified.
We address the problem of designing decentralized laws to control a team of general fully actuated manipulators which synchronize their movements while following the same desired trajectory. To this effect we investig...
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We address the problem of designing decentralized laws to control a team of general fully actuated manipulators which synchronize their movements while following the same desired trajectory. To this effect we investigate an adaptive control of an identical multiple manipulators, with unknown inertia parameters, tracking a common trajectory. It is also assumed that all the robots in the synchronization system have the same number of joints and equivalent joint work spaces, i.e. any possible configuration of a given robot in the system can be achieved by any other robot in the system. Adaptive controls are derived for group of manipulators using a consensus algorithm. This consensus algorithm is applied on the group level to estimate the time-varying group trajectory information in a distributed manner. The proposed strategy only requires local neighbor-to-neighbor information exchange between manipulators and does not assume the existence of any explicit leaders in the team. The interaction topology of a network of agents is represented using an indirected graph. The coordination strategy is to let each manipulator track its desired trajectory while synchronizing its motion with the others manipulator's motions so that differential position errors amongst robots converge to zero.
In this paper, we present a dynamic redundancy resolution technique for mobile manipulators using a position fuzzy controller. First, the dynamic model of the mobile manipulator in feasible motion space is given. Next...
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ISBN:
(纸本)9781424443451
In this paper, we present a dynamic redundancy resolution technique for mobile manipulators using a position fuzzy controller. First, the dynamic model of the mobile manipulator in feasible motion space is given. Next, a control algorithm is proposed which resolve the redundancy by decoupling the motion of the system into the end-effector motion in the task space and an internal motion in the null space and controls them in prioritized basis with priority given to the primary task. In order to achieve a good end-effector trajectory tracking, a position fuzzy controller is used to provide the torques needed to move the end-effector along the desired trajectory. Simulation results are given to illustrate the coordination of two subsystems in executing the desired trajectories.
In this paper, we present a dynamic redundancy resolution technique for mobile manipulators using a position fuzzy controller. First, the dynamic model of the mobile manipulator in feasible motion space is given. Next...
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In this paper, we present a dynamic redundancy resolution technique for mobile manipulators using a position fuzzy controller. First, the dynamic model of the mobile manipulator in feasible motion space is given. Next, a control algorithm is proposed which resolve the redundancy by decoupling the motion of the system into the end-effector motion in the task space and an internal motion in the null space and controls them in prioritized basis with priority given to the primary task. In order to achieve a good end-effector trajectory tracking, a position fuzzy controller is used to provide the torques needed to move the end-effector along the desired trajectory. Simulation results are given to illustrate the coordination of two subsystems in executing the desired trajectories.
The paper deals with the controllability study of a subclass of juggling systems, named linear juggler. It comes that despite the simple representation of such systems, their controllability is in general not easy to ...
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The paper deals with the controllability study of a subclass of juggling systems, named linear juggler. It comes that despite the simple representation of such systems, their controllability is in general not easy to establish, since they remain highly nonlinear dynamical systems. In this paper we are interested in the controllability properties of one degree of freedom linear juggling system.
Obstacle avoidance and path planning are the most important problems in mobile robots. Besides, finding methods for controlling and decreasing the real robot error in path have been another target for researchers inve...
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Obstacle avoidance and path planning are the most important problems in mobile robots. Besides, finding methods for controlling and decreasing the real robot error in path have been another target for researchers investigation. Usual methods have two separated parts, research target and path planning with obstacle avoidance. In this context, a fuzzy logic controller has been constructed in order to train an intelligent robot. Genetic algorithms are used to optimize the consequences of a Sugeno fuzzy logic optimal controller for the mobile robot navigation. This is to search a target in an environment with and without obstacles. Simulation results verify successful applications of method for real motion situations. An application have been effected in the Khepera robot demonstrating. The Khepera robot can go to the goal and avoid the obstacles successfully.
An adaptive fuzzy controller is constructed from a collection of fuzzy IF-THEN rules whose parameters are changed or adapted according to adaptive laws for the purpose of controlling a nonlinear plant to track a refer...
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An adaptive fuzzy controller is constructed from a collection of fuzzy IF-THEN rules whose parameters are changed or adapted according to adaptive laws for the purpose of controlling a nonlinear plant to track a reference trajectory. The fuzzy adaptive control is composed in two classes: direct and indirect one. The indirect fuzzy adaptive controller will be designed through two steps. The first one consists in the construction of a controller based on linguistic description about the unknown plant from human expert. To guaranty stability, a supervisory controller is added. The second step adjusts, on line, the parameters of the fuzzy controller based on the gradient descend. To ensure the robustness, fuzzy controller parameters are adjusted, on line, by a robust gradient descent method. Simulation results show the effectiveness of proposed algorithms in tracking of the inverse pendulum output.
A wide bandwidth CMOS realization of high performance dual output second generation (CCIIplusmn) and third generation (CCIIIplusmn) current conveyors are presented. Both second and third generation current conveyors h...
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A wide bandwidth CMOS realization of high performance dual output second generation (CCIIplusmn) and third generation (CCIIIplusmn) current conveyors are presented. Both second and third generation current conveyors have the advantages of a wide current and voltage bandwidths, controlled intrinsic resistances at port X, Y and Z. We have developed an optimization program, thanks to Heuristic methodology that is used for optimal sizing regarding static and dynamic performance. PSPICE simulation show that the current and voltage bandwidths are respectively 1.5806 GHz and 6.506 GHz, and 454.11 Omega as R X input port resistance for a control current of 100 muA. CCIIplusmn is a useful building block for analog circuits, especially for application requiring dual outputs. The proposed circuit is able of operating at voltage supply (plusmn2.5V), with reduced power consumption. A frequency characterization of the proposed CCIIplusmn is given, this CCIIplusmn is characterized by low values for its parasitic input resistance (454.11 Omega). In this paper, we present a versatile multi-input multi-output controlled filter configuration. Either a three-input single output or single-input double-output universal filter can be realized. The proposed circuits offer the following advantageous features: realization of current-mode low-pass and band-pass filter responses from the same configuration, employing only three current conveyors and four grounded passive elements. In order to implement a current controlled Radiofrequency active filter, we use a translinear configuration for both second and third generation current conveyors. The proposed implementation exhibiting a current controlled intrinsic resistances at port X, Y and Z allow a control of the cut-off frequency. The cut-off frequency of the low pass filter can be attend 1.64 GHz and the central frequency of the band pass filter can be attend 1.13 GHz. Simulation results are in good accordance with theoretical ones. PSPICE
This paper presents an optimal fuzzy logic controller design using optimization techniques. The fuzzy logic controller (FLC) has been developed and implemented for the motion of the robot from an initial position towa...
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This paper presents an optimal fuzzy logic controller design using optimization techniques. The fuzzy logic controller (FLC) has been developed and implemented for the motion of the robot from an initial position towards another desired position, taking into account the kinematic constraints. First, we have carried out a simulation of a fuzzy logic based controller which determines the speed values of each driving wheel, while seeking the goal. Secondly, an optimization of this controller has been realized using gradient method and genetic algorithms. A comparison between these methods has been effected. Not only simulation results are shown in this paper, but the ldquoreal-timerdquo implementation has been realized onto the mini robot Khepera II. Simulation results verify successfully the application of the proposed method to real motion situations.
This paper presents a design method of robust fuzzy PID controller for discrete-time uncertain nonlinear systems. The Takagi-Sugeno fuzzy model with parameter uncertainties is employed to describe discrete-time uncert...
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This paper presents a design method of robust fuzzy PID controller for discrete-time uncertain nonlinear systems. The Takagi-Sugeno fuzzy model with parameter uncertainties is employed to describe discrete-time uncertain nonlinear system with bounded uncertainties. A robust pole placement called pole coloring is used to control each local model. controller parameters are determined through cost function minimization by an optimization procedure. Closed-loop system's poles are chosen according to desired performance. The global controller is obtained by local control inference. Finally, two examples are treated to illustrate the effectiveness of the proposed design method.
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