Redefinition of the robot motion-control problem

Sweet, L. M.; Good, M C

doi:10.1109/mcs.1985.1104955

Cited by 156 publications

(42 citation statements)

References 7 publications

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“…After carrying out the review of the literature associated with design of controllers for the trajectory tracking task in differential drive WMRs, it was found that, generally, this task has been solved in three directions: (a) by only using the kinematic or dynamic model of the mechanical structure , (b) by employing the kinematic/dynamic model of the mechanical structure along with the dynamics of the actuators [85][86][87][88][89][90][91][92][93][94][95][96][97][98][99][100][101][102][103], and (c) by considering the kinematic model of the mechanical structure along with the dynamics of the actuators and power stage [104,105]. In the last direction, contributions that have carried out interesting efforts are [106][107][108][109][110][111][112][113][114].…”

Section: Discussion and Contributionmentioning

confidence: 99%

“…However, it allows designing controllers that solve the trajectory tracking problem more effectively. This can be observed when the WMR is moving at high speeds or when its mass is variable [89]. Motivated by these facts, Anupoju et al [90] designed three adaptive controllers, the first one for the kinematic model of the mechanical structure, the second one for the dynamic model of that same mechanical structure, and the third one for the actuators.…”

Section: Dynamic Modelmentioning

confidence: 99%

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Tracking Control for Mobile Robots Considering the Dynamics of All Their Subsystems: Experimental Implementation

et al. 2017

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The trajectory tracking task in a wheeled mobile robot (WMR) is solved by proposing a three-level hierarchical controller that considers the mathematical model of the mechanical structure (differential drive WMR), actuators (DC motors), and power stage (DC/DC Buck power converters). The highest hierarchical level is a kinematic control for the mechanical structure; the medium level includes two controllers based on differential flatness for the actuators; and the lowest hierarchical level consists of two average controllers also based on differential flatness for the power stage. In order to experimentally validate the feasibility of the proposed control scheme, the hierarchical controller is implemented via a Σ-Δ-modulator in a differential drive WMR prototype that we have built. Such an implementation is achieved by using MATLAB-Simulink and the real-time interface ControlDesk together with a DS1104 board. The experimental results show the effectiveness and robustness of the proposed control scheme.

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Section: Discussion and Contributionmentioning

confidence: 99%

Section: Dynamic Modelmentioning

confidence: 99%

Tracking Control for Mobile Robots Considering the Dynamics of All Their Subsystems: Experimental Implementation

et al. 2017

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“…The control schemes proposed for the rigid manipulators are limited in their applicability to real robots [13]. Compared with rigid robots, number of degrees of freedom in the flexible-joint robots becomes twice as number of control actions while the matching property between nonlinearities and inputs is lost [10] [12].…”

Section: Introductionmentioning

confidence: 99%

Interval Type-2 Fuzzy PD Tracking Control of Flexible-Joint Robots

Zirkohi¹,

Izadpanah²

2017

JSEA

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This paper develops a novel interval type-2 fuzzy Proportional-Derivative (PD) control scheme for electrically driven flexible-joint robots using the direct method of Lyapunov. The controller has a simple design in a decentralized structure. Compared to the previous controllers reported for the flexible-joint robots which use two control loops, it has a simpler structure using only one control loop. It guarantees stability and provides a good tracking performance. The controller considers the whole robotic system including the manipulator and motors by applying the voltage control strategy. Stability analysis is presented and the effectiveness of the proposed control approach is demonstrated by simulations using a three link flexible-joint robot driven by permanent magnet DC motors. Simulation results show that the interval type-2 fuzzy PD controller can handle external disturbance better than the type-1 fuzzy PD controller. In addition, it spends less control effort than the type-1 in order to deal with disturbance.

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“…Tasks involving fast motion and/or hard contact with the environment are expected to induce deflections in the robot components, eventually exciting an oscillatory behavior. There are two sources of vibration in robot manipulators: joint flexibility, due to the elasticity of motion transmission elements such as harmonic drives, belts, or long shafts [26], and link flezibility, introduced by a long reach and slender/lightweight construction of the arm [6,17]. In order to be able to counteract the negative effects of flexibility, advanced robot control systems should be designed on the basis of a more complete dynamic model of the robot (see, e.g.…”

Section: Introductionmentioning

confidence: 99%

Trajectory control of flexible manipulators

Luca

1998

Lecture Notes in Control and Information Sciences

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We present some feedback control techniques recently developed for the exact solution of trajectory tracking problems for manipulators with flexible elements. Two classes are considered: i) robots with rigid links but with elastic transmissions, in which flexibility is concentrated at the joints, and ii) robots with lightweight and/or long arms, where flexibility is distributed along the links. For robots with elastic joints, we introduce a generalized inversion algorithm for the synthesis of a dynamic feedback control law that gives input-output decoupling and full state linearization. For robots with flexible links, the end-effector trajectory tracking problem is solved based on the iterative computation of the link deformations associated with the desired output motion, combined with a state trajectory regulator. For both robot models, the control design is performed directly on the second-order dynamic equations. I n t r o d u c t i o nModeling robot manipulators as rigid mechanical systems is an idealization that becomes unrealistic when higher performance is requested. Tasks involving fast motion and/or hard contact with the environment are expected to induce deflections in the robot components, eventually exciting an oscillatory behavior. There are two sources of vibration in robot manipulators: joint flexibility, due to the elasticity of motion transmission elements such as harmonic drives, belts, or long shafts [26], and link flezibility, introduced by a long reach and slender/lightweight construction of the arm [6,17]. In order to be able to counteract the negative effects of flexibility, advanced robot control systems should be designed on the basis of a more complete dynamic model of the robot (see, e.g. [27] and [5])In robotic systems with flexible elements, output trajectories are typically defined beyond the structural flexibility, i.e. in terms of link motion for robots with elastic joints or at the level of manipulator tip for robots with link flexibility. We address in this chapter the stable and accurate reproduction of such trajectories using model-based state feedback control. Standard tools for solving trajectory tracking problems in nonlinear systems, such as feedback linearization, input-output decoupling, or inversion control (see, e.g. [19]), are not sufficient in these cases, so that the application of more advanced control techniques should be investigated.

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Redefinition of the robot motion-control problem

Cited by 156 publications

References 7 publications

Tracking Control for Mobile Robots Considering the Dynamics of All Their Subsystems: Experimental Implementation

Tracking Control for Mobile Robots Considering the Dynamics of All Their Subsystems: Experimental Implementation

Interval Type-2 Fuzzy PD Tracking Control of Flexible-Joint Robots

Trajectory control of flexible manipulators

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