Application Study on Iterative Learning Control of High Speed Motions for Parallel Robotic Manipulator

Abdellatif, Houssem; Feldt, Matthias; Heimann, Bodo

doi:10.1109/cca.2006.285866

Cited by 8 publications

(8 citation statements)

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“…The solution of this norm-optimal problem leads to the following update law for the control input [5] Uk+1(t) =Uk(t)-(BTK(t)B+R(t)f 1 BTK(t) (11) x A (Xk +1 (t) -Xk(t)) +R-1 (t)BT �k+1(t). The matrix gain K(t) in (11) can be calculated in descending sample order as solution of the discrete matrix Riccati equation…”

Section: A Pid-type Ilcmentioning

confidence: 99%

“…For the system under consideration ek(t) is defined as the carriage position error ek(t) = ZCd, k(t) -ZC,k(t). Then the update law for PID-type ILC can be stated in the following form [10], [11] t Uk+1(t) = Uk(t) + <l>lek(t) + <l> 2 ek(t+ 1) +<1>3 L ek( V) . (8) v=1…”

Section: A Pid-type Ilcmentioning

confidence: 99%

“…The evaluation of equation (13), which depends on the control error of the previous trial, can be performed after the end of every trial. Only the equation for the input update (11), which involves both feedback and feedforward actions, has to be evaluated during each sample time step.…”

Section: K(t) =Atk(t + I)a+ctq(t + I)c _atk(t + I)bmentioning

confidence: 99%

See 2 more Smart Citations

ILC for a fast linear axis driven by pneumatic muscle actuators

Schindele

Aschemann

2011

2011 IEEE International Conference on Mechatronics

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Iterative learning control is a popular method for accurate trajectory tracking of systems that repeat the same motion many times. This paper presents two different approaches of iterative learning control for a novel linear axis. Its guided carriage is driven by a nonlinear mechanism consisting of two pulley tackles with a pair of pneumatic muscle actuators arranged at both sides. This innovative drive concept allows for an increased workspace as well as higher carriage velocities as compared to a direct actuation. The proposed control has a cascade structure. The internal pressure of each pneumatic muscle is controlled by a fast underlying control loop. Hence, the control design for the outer control loop can be simplified by considering these controlled muscle pressures as ideal control inputs. For the outer control loop a PID·type iterative learning control and, alternatively, a norm-optimal iterative learning control of the carriage position is proposed. Experimental results from an implementation on a test rig show an excellent control performance.

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Section: A Pid-type Ilcmentioning

confidence: 99%

Section: A Pid-type Ilcmentioning

confidence: 99%

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ILC for a fast linear axis driven by pneumatic muscle actuators

Schindele

Aschemann

2011

2011 IEEE International Conference on Mechatronics

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“…Although robotic operations and manufacturing such as welding process and injection molding are obvious examples of situations in which a machine or process executes a given trajectory over and over, there are numerous other problems that can be viewed from the framework of repetitive operations. Application studies and practical implementations on iterative learning control have been carried out for parallel robotic manipulators [24], gantry robots, [25], and direct-driven robots, [26].…”

Section: Introductionmentioning

confidence: 99%

Learning-based Modified Transpose Jacobian control of robotic manipulators

Karimi

Moosavian

2008

2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics

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The Modified Transpose Jacobian (MTJ) algorithm has been proposed, based on an approximated feedback linearization approach, in which there is no need to a priori knowledge of the plant dynamics. In this paper, a Learning-based Modified Transpose Jacobian (LMTJ) control algorithm is presented for trajectory tracking of robotic manipulators in an iterative operation mode. In this new control method, the MTJ control scheme is integrated into a learning procedure; as such, the trajectory tracking converges very fast and the bettering performance is achieved after a few iterations. Unlike conventional learning techniques, control gains selection in LMTJ is easily performed. The LMTJ method possesses both MTJ and learning capabilities with a simple control structure. Obtained results show the merits of the new proposed LMTJ controller that its performance is comparable to that of modelbased algorithms in terms of tracking error elimination, even in the presence of nonidentical initializations and un-repetitive disturbances.

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“…In [1] three different approaches; heuristic, modelbased, and frequency-response based method are discussed when deriving the linear ILC algorithm with the structure u k+1 = u k + QLe k . The heuristic ILC design is very similar to the one used in this paper.…”

Section: A Previous Work On Ilc Algorithms For Mimo Systemsmentioning

confidence: 99%

ILC applied to a flexible two-link robot model using sensor-fusion-based estimates

Wallén

Gunnarsson

Henriksson

et al. 2009

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) Held Jointly With 2009 28th Chinese Control Conference

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Estimates from an extended Kalman filter (EKF) is used in an Iterative Learning Control (ILC) algorithm applied to a realistic two-link robot model with flexible joints. The angles seen from the arm side of the joints (arm angles) are estimated by an EKF in two ways: 1) using measurements of angles seen from the motor side of the joints (motor angles), which normally are the only measurements available in commercial industrial robot systems, 2) using both motor-angle and tool-acceleration measurements. The estimates are then used in an ILC algorithm. The results show that the actual arm angles are clearly improved compared to when only motor angles are used in the ILC update, even though model errors are introduced.Keywords: Iterative learning control; Robotics; Sensor fusion ILC applied to a flexible two-link robot model using sensor-fusion-based estimates Johanna Wallén, Svante Gunnarsson, Robert Henriksson, Stig Moberg and Mikael NorrlöfAbstract-Estimates from an extended Kalman filter (EKF) is used in an Iterative Learning Control (ILC) algorithm applied to a realistic two-link robot model with flexible joints. The angles seen from the arm side of the joints (arm angles) are estimated by an EKF in two ways: 1) using measurements of angles seen from the motor side of the joints (motor angles), which normally are the only measurements available in commercial industrial robot systems, 2) using both motorangle and tool-acceleration measurements. The estimates are then used in an ILC algorithm. The results show that the actual arm angles are clearly improved compared to when only motor angles are used in the ILC update, even though model errors are introduced.

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Application Study on Iterative Learning Control of High Speed Motions for Parallel Robotic Manipulator

Cited by 8 publications

References 0 publications

ILC for a fast linear axis driven by pneumatic muscle actuators

ILC for a fast linear axis driven by pneumatic muscle actuators

Learning-based Modified Transpose Jacobian control of robotic manipulators

ILC applied to a flexible two-link robot model using sensor-fusion-based estimates

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