2019
DOI: 10.1109/access.2019.2936309
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Abstract: This article presents the development of a dynamic optimization method for controller tuning. This is proposed because most traditional tuning methods for complex coupled dynamic models are based on experience, and thus lacking accuracy. The case study is a Mobile Manipulator that consists of an anthropomorphic manipulator and a differential mobile platform. The system model has a trajectory generator that includes the coupled kinematic model, the Jacobian model, the coupled dynamic model, and a Proportional-D… Show more

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Cited by 11 publications
(4 citation statements)
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“…Electronics 2019, 8, x FOR PEER REVIEW 9 of 26 Remark 7: Considering that the configuration of the robotic system is redundant (7), the Jacobian matrix J ∈ R × has more unknowns than equations (m < n), with range r = n for each b ∈ R , then (7) represents a subdetermined linear system with general solution. = +…”
Section: Design Of the Control Lawmentioning
confidence: 99%
See 1 more Smart Citation
“…Electronics 2019, 8, x FOR PEER REVIEW 9 of 26 Remark 7: Considering that the configuration of the robotic system is redundant (7), the Jacobian matrix J ∈ R × has more unknowns than equations (m < n), with range r = n for each b ∈ R , then (7) represents a subdetermined linear system with general solution. = +…”
Section: Design Of the Control Lawmentioning
confidence: 99%
“…The proper design of a controller that will govern the actions of each of the robot's effectors can ensure optimal execution of the scheduled task. In this respect, Su, et al in [6] and Prada-Jimenez, et al in [7] propose different ways of controlling mobile manipulators considering their kinematic model as a single robot. In a different way, Hamner, et al in [8] works in the hybridization of locomotion and manipulation, joining a mobile platform and a manipulator in several modes of operation, but treating the mobile part and the arm as different robots.…”
Section: Introductionmentioning
confidence: 99%
“…As an alternative for redundancy resolution, gradientbased optimization has been successfully used in the fields [15], [16]. Gradient-based optimization is mathematical programming that can systematically and efficiently extremize an objective function (e.g., minimization of execution time, joint rotation, and travel length [17], and minimization of energy consumption, tracking error, and total weight of motors [18], minimization of sway of a robot body, forward movement error, and backward foot slipping [19], maximization of load carrying capacity [20], [21]) while satisfying the constraint functions (e.g., displacement, velocity, acceleration, and jerk of joints [22], the range of PD control gain values [23], and the rotation range of a superelastic flexure hinge [24]). An optimization-based method was also implemented to evaluate force capability for redundantly actuated parallel manipulators [25].…”
Section: Introductionmentioning
confidence: 99%
“…For the robotics control, the classical PID methods 37 is still a main part in any controllers and they have been applied widely in industrial applications due to its design simplicity and good performance, particularly in applications, in which the control system parameters are not well known. Thus, in order to overcome the drawbacks of classical PID methods, the researchers have continued to propose the intelligent control algorithms [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53] based on PID technique for robotics control, recently. Wen and Jacob 41 solved the problems of big derivative and integral gains to assure asymptotic stability of control system by a neural PID algorithm.…”
Section: Introductionmentioning
confidence: 99%