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PhD Defense
Thursday 17/12/2015
Some Contributions to Nonlinear Adaptive Control of PKMsFrom Design to Real-Time Experiments
Antoine Ferreira Professeur Univ. d’Orléans RapporteurNacer Kouider M’sirdi Professeur LSIS, AMU, Marseille RapporteurMohamed Bouri Maître de conf. EPFL ExaminateurSébastien Krut CR CNRS LIRMM ExaminateurFrançois Pierrot DR CNRS LIRMM Direct. de thèseAhmed Chemori CR CNRS LIRMM Co-encadrant
Jury
presented by: Moussab Bennehar
Moussab BenneharPhD defense
• State of the art
• Dynamic modelling of parallel manipulators
• Proposed control solutions
Solution 1: Enhanced Model-Based Adaptive Control
Solution 2: Extended L1 Adaptive Control
How to deal with the internal forces issue ?
• Real-time experiments and results
• Conclusion and future work
Outline
Moussab BenneharPhD defense
State of the Art
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Classification Non-adaptive Adaptive
Moussab BenneharPhD defense
Main existing control schemes for PKMs
Non-model-based Model-based Non-model-based Model-based
Non-adaptive schemes Adaptive schemes
PD with grav. comp. [Chifu10; Kelly97]
Computed torque and derivatives [Asgari15;
Sartori12; Zhang07]
Sliding mode [Jafarinasab11]
Predictive [Vivas03]
H-infinity [Becerra-
Vargas12; Rachedi15]
MRAC-based control schemes [Nguyen et al.,
1993]
Control schemes based on artificial networks [Li09]
Control schemes based on computed torque [Shang12]
Control schemes based on passivity [Honegger00;
Sartori15; Shang10]
PID control [Cheng03]
Nonlinear PID [Su04]
Fuzzy logic [Fang99;
Begon95]
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Classification Non-adaptive Adaptive
1
Moussab BenneharPhD defense
No real-time adjustment of the parameters is required.Require relatively less computation time than adaptive schemes.Less control design parameters to be tuned.
May provide poor performance in the presence of large uncertainties.Large disturbances may lead to closed-loop instability.Cannot be used to estimate the parameters of the robot.Large uncertainties / disturbances may result in high-gain feedback.
Main existing control schemes for PKMs
Non-model-based Model-based
Non-adaptive schemes
PD with grav. comp. [Chifu10; Kelly97]
Computed torque and derivatives [Asgari15;
Sartori12; Zhang07]
Sliding mode [Jafarinasab11]
Predictive [Vivas03]
H-infinity [Becerra-
Vargas12; Rachedi15]
PID control [Cheng03]
Nonlinear PID [Su04]
Fuzzy logic [Fang99;
Begon95]
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Classification Non-adaptive Adaptive
2
Moussab BenneharPhD defense
Uncertainties and disturbances are estimated in real-time.Result in a better closed-loop performance if adequately tuned.Do not require accurate values of the robot’s parameters.
Require complex real-time computations.Large number of control design parameters (one parameter per estimation).Convergence of the parameters not always guaranteed.Estimated parameters may oscillate heavily leading to instabilities.
Main existing control schemes for PKMs
Non-model-based Model-based
Adaptive schemes
MRAC-based control schemes [Nguyen et al.,
1993]
Control schemes based on artificial networks [Li09]
Control schemes based on computed torque [Shang12]
Control schemes based on passivity [Honegger00;
Sartori15; Shang10]
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Classification Non-adaptive Adaptive
3
Moussab BenneharPhD defense
Dynamic Modeling of Parallel Robots
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Dynamic modeling Reformulation
Moussab BenneharPhD defense
Dynamic Modelling of PKMs
Simplifying hypotheses [Codourey98]:
Rotational inertia of the forearms neglected.
Mass of the forearms split up into two parts.
Friction effects are neglected.
Joint space inverse dynamic model
Inverse kinematics (IK)
Inverse differential kinematics (IDK)
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Dynamic modeling Reformulation
4
Moussab BenneharPhD defense
Reformulation of the Robot’s Dynamics for Adaptive Control
RegressorVector of parameters
to be estimated
Useful if all parameters are to
be estimated
If only a set of the parameters is to be
estimated Vector of known parameters
Vector of parameters to be estimated
Affine in the parameters formulation
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Dynamic modeling Reformulation
5
Moussab BenneharPhD defense
Proposed Control Solutions
State of the Art Modeling Proposed Solutions Experiments Conclusion
Overview Solution 1 Solution 2 Redundancy
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Experiments Conclusion
Proposed Control Solutions
Contribution 2
RISE-Based Adaptive Control
Contribution 1
DCAL with nonlinear
feedback gains
Contribution 4
L1 adaptive control with adaptive FF
Contribution 3
L1 adaptive control with nominal FF
Solution 1: Enhanced Model-Based Adaptive Control
Solution 2: Extended L1 Adaptive Control
Solu
tions
Cont
ribu
tions
Appl
icat
ions
Overview on proposed solutions
Overview Solution 1 Solution 2 Redundancy
6
Moussab BenneharPhD defense
Solution 1: Enhanced Model-Based Adaptive
Control
State of the Art Modeling Proposed Solutions Experiments Conclusion
Overview Solution 1 Solution 2 Redundancy
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Experiments Conclusion
Proposed Control Solutions
Contribution 2
RISE-Based Adaptive Control
Contribution 1
DCAL with nonlinear
feedback gains
Contribution 4
L1 adaptive control with adaptive FF
Contribution 3
L1 adaptive control with nominal FF
Solution 1: Enhanced Model-Based Adaptive Control
Solution 2: Extended L1 Adaptive Control
Solu
tions
Cont
ribu
tions
Appl
icat
ions
Overview on proposed solutions
Overview Solution 1 Solution 2 Redundancy
7
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Experiments Conclusion
Desired Compensation Adaptation Law (DCAL) [Sadegh90]
Control law
Linear feedback term Auxiliary termModel-based adaptive feedforward
Parameters adaptation rule :
Overview Solution 1 Solution 2 Redundancy
8
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Experiments Conclusion
Proposed contribution
Main drawbacks
A feedback loop with constant gains.
Poor performance for nonlinear systems.
Sensitive to external disturbances.
Poor performance for high accelerations.
Limited tuning capabilities.
Desired Compensation Adaptation Law (DCAL) [Sadegh90]
Main advantages
Desired trajectories in the regressor.
No inversion of the mass matrix
required.
Reduced noise effect.
Reduced computing time.
Replace constant linear gains in the feedback loop by nonlinear ones
Overview Solution 1 Solution 2 Redundancy
9
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Experiments Conclusion
DCAL with nonlinear feedback gains [Bennehar14ab]
Nonlinear feedback gains
Large error large gain.Small error small gain.
Original control law
Proposed control law
[Bennehar14a] M. Bennehar, A. Chemori, and F. Pierrot. A New Extension of Direct Compensation Adaptive Control and Its Real-Time Application to Redundantly Actuated PKMs.
In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS14), pages 1670-1675, Chicago, Illinois, USA, September 2014.
[Bennehar14b] M. Bennehar, A. Chemori, and F. Pierrot. A New Revised Desired Compensation Adaptive Control for Enhanced Tracking: Application to RA-PKMs. Advanced
Robotics, 2015. [Submitted]
Error and combined error:
Overview Solution 1 Solution 2 Redundancy
10
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Experiments Conclusion
Task-space trajectory generator
IK/IDKParallel robot
Model-based adaptive
feedforward
DCAL with nonlinear feedback gains [Bennehar14ab]
Overview Solution 1 Solution 2 Redundancy
11
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Experiments Conclusion
Proposed Control Solutions
Contribution 2
RISE-Based Adaptive Control
Contribution 1
DCAL with nonlinear
feedback gains
Contribution 4
L1 adaptive control with adaptive FF
Contribution 3
L1 adaptive control with nominal FF
Solution 1: Enhanced Model-Based Adaptive Control
Solution 2: Extended L1 Adaptive Control
Solu
tions
Cont
ribu
tions
Appl
icat
ions
Overview on proposed solutions
Overview Solution 1 Solution 2 Redundancy
12
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Experiments Conclusion
Robust Integral of the Sign of the Error (RISE) [Xian04]
What is RISE ?
Robust Integral of the Sign of the Error
Non-model based feedback control strategy
Features a unique signum function
RISE control law
Overview Solution 1 Solution 2 Redundancy
13
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Experiments Conclusion
Proposed contribution
Main drawbacks
May lead to high-gain or high-frequency feedback.Poor performance in presence of large disturbances.Low performance in the case of hard nonlinearities
Main advantages
Stability of the system guaranteed. High order nonlinearities taken into account.MIMO systems supported.Large class of general disturbances assimilated.Very reasonable Hypotheses.
Augment RISE control with a model-based adaptive feedforward
Robust Integral of the Sign of the Error (RISE) [Xian04]
Overview Solution 1 Solution 2 Redundancy
14
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Experiments Conclusion
[Bennehar14c] M. Bennehar, A. Chemori, and F. Pierrot. A Novel RISE-Based Adaptive Feedforward Controller for Redundantly Actuated Parallel Manipulators. In IEEE/RSJ
International Conference on Intelligent Robots and Systems (IROS14), pages 2389-2394, Chicago, Illinois, USA, September 2014.
[Bennehar14d] M. Bennehar, A. Chemori, M. Bouri, L.F Jenni and F. Pierrot. A New Adaptive RISE-Based Control for Parallel Robots: Design, Stability Analysis and Experiments.
International Journal of Control. [Submitted]
RISE-based adaptive control [Bennehar14cd]
Proposed control law
Parameters adaptation rule
Model-based adaptive feedforward
Overview Solution 1 Solution 2 Redundancy
15
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Experiments Conclusion
Task-space trajectory generator
IK/IDKParallel robot
Model-based adaptive
feedforward
RISE-based adaptive control [Bennehar14cd]
Overview Solution 1 Solution 2 Redundancy
16
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Experiments Conclusion
Proposed Control Solutions
Contribution 2
RISE-Based Adaptive Control
Contribution 1
DCAL with nonlinear
feedback gains
Contribution 4
L1 adaptive control with adaptive FF
Contribution 3
L1 adaptive control with nominal FF
Solution 1: Enhanced Model-Based Adaptive Control
Solution 2: Extended L1 Adaptive Control
Solu
tions
Cont
ribu
tions
Appl
icat
ions
Overview on proposed solutions
Overview Solution 1 Solution 2 Redundancy
17
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Experiments Conclusion
Main Limitations of Conventional Adaptive Control
Overview Solution 1 Solution 2 Redundancy
High adaptation gain High gain/frequency feedback.1
System:
Adaptation gain:
18
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Experiments Conclusion
Main Limitations of Conventional Adaptive Control
Overview Solution 1 Solution 2 Redundancy
High adaptation gain High gain/frequency feedback.2
System:
Adaptation gain:
19
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Experiments Conclusion
Main Limitations of Conventional Adaptive Control
Solution
L1 Adaptive Control [Hovakimyan06]
Decoupled robustness and adaptation
High adaptation gain High gain/frequency feedback.
Adequate initialization of the parameters.
Persistence excitation of the parameters.
Specifications are specified only asymptotically.
Uncertainties may lie outside the actuators’ bandwidth.
1
2
3
4
5
Overview Solution 1 Solution 2 Redundancy
20
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Experiments Conclusion
Background on L1 Adaptive Control [Hovakimyan06]
Inspired from direct Model Reference Adaptive Control (MRAC)
Overview Solution 1 Solution 2 Redundancy
21
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Experiments Conclusion
state predictor
low pass filter
projection operator
Background on L1 Adaptive Control [Hovakimyan06]
Inspired from direct Model Reference Adaptive Control (MRAC)
Overview Solution 1 Solution 2 Redundancy
22
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Experiments Conclusion
Application of L1 Adaptive Control to PKMs [bennehar15e]
Tracking Error
Control Law
Adaptation Laws
Inverse dynamic model
Low-pass Filter Estimated Parameters
Error Dynamics
[bennehar15e] M. Bennehar, A. Chemori, and F. Pierrot. L 1 Adaptive Control of Parallel Kinematic Manipulators: Design and Real-Time Experiments. In IEEE International
Conference on Robotics and Automation (ICRA15), pages 1587-1592, Seattle, Washington, USA, May 2015.
Overview Solution 1 Solution 2 Redundancy
23
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Experiments Conclusion
state predictor
low pass filter
projection operator
Application of L1 Adaptive Control to PKMs [bennehar15e]
Overview Solution 1 Solution 2 Redundancy
24
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Experiments Conclusion
Proposed contribution
Main drawbacks
The estimated parameters lose their physical meaning.All uncertainties are considered with unknown structure.Do not take advantage of the knowledge about the dynamics.
Main advantages
Decoupled estimation and adaptation loops.
Specified performance ∀ 𝑡≥0.
Parameters boundedness.
No dynamic model is required.
Augment the L1 adaptive control with a model-based feedforward to further improve the tracking performance
Application of L1 Adaptive Control to PKMs [bennehar15e]
Overview Solution 1 Solution 2 Redundancy
25
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Experiments Conclusion
Joint Space Inverse Dynamic Model
L1 Adaptive Control Law
Adaptation Laws
Proposed Control Law
Augmented L1 adaptive control with nominal model-based FF [bennehar15f]
State-feedback Term
Adaptive Term
Model-based feedforward
[bennehar15f] M. Bennehar, A. Chemori, and F. Pierrot. Feedforward Augmented L 1 Adaptive Controller for Parallel Kinematic Manipulators with Improved Tracking. IEEE
Robotics and Automation Letters (RA-L). [Submitted]
Overview Solution 1 Solution 2 Redundancy
26
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Experiments Conclusion
Augmented L1 adaptive control with nominal model-based FF [bennehar15f]
state predictor
low pass filter
projection operator
Model-based feedforward
Overview Solution 1 Solution 2 Redundancy
27
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Experiments Conclusion
Proposed Control Solutions
Contribution 2
RISE-Based Adaptive Control
Contribution 1
DCAL with nonlinear
feedback gains
Contribution 4
L1 adaptive control with adaptive FF
Contribution 3
L1 adaptive control with nominal FF
Solution 1: Enhanced Model-Based Adaptive Control
Solution 2: Extended L1 Adaptive Control
Solu
tions
Cont
ribu
tions
Appl
icat
ions
Overview on proposed solutions
Overview Solution 1 Solution 2 Redundancy
28
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Experiments Conclusion
Proposed contribution
Main drawbacks
Do not consider variations/uncertainties in
the model-based feedforward.
May lead to poor performance if the
dynamic model is not accurate.
Main advantages
Inherits the advantages of standard L1 adaptive control.Compensates for modeling nonlinearities and disturbances separately.May lead to better performance than standard L1-AC if accurate model.May reduce control effort.
Endow the additional feedforward term with adaptation capabilities
Augmented L1 adaptive control with nominal model-based FF [bennehar15f]
Overview Solution 1 Solution 2 Redundancy
29
Moussab BenneharPhD defense
Extended L1 adaptive control with adaptive model-based FF [bennehar15g]
Original L1 adaptive control law
Estimated uncertainties
Proposed extended L1 adaptive control law
Same as L1-ac
Model-based adaptive feedforward
Compensation of modeled uncertainties
[bennehar15g] M. Bennehar, A. Chemori, F. Pierrot and V. Creuze. Extended Model-Based Feedforward Compensation in L1 Adaptive Control for Mechanical Manipulators:
Design and Experiments. Frontiers in Robotics and AI.
State of the Art Modeling Proposed Solutions Experiments Conclusion
Overview Solution 1 Solution 2 Redundancy
30
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Experiments Conclusion
Task-space trajectory generator
Parallel robot
State predictor
Adaptation laws
Control law with low-pass filter
IK/IDK
Model-based adaptive
feedforward
Extended L1 adaptive control with adaptive model-based FF [bennehar15e]
Overview Solution 1 Solution 2 Redundancy
31
Model-based adaptive feedforward
Moussab BenneharPhD defense
Actuation Redundancy and Internal Forces
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Overview Solution 1 Solution 2 Redundancy
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Experiments Conclusion
How to Deal with the Internal Forces Issue ?
2 DOFs and 3 actuators
Redundantly Actuated PKM
Redundantly actuated PKMs’ inputs contain antagonistic forces.
These forces create internal pre-stress.
They deteriorate performance, create vibrations and harm the robot.
These forces can be reduced using the projector [Muller11]:
The proposed control input becomes:
Identity matrix
Overview Solution 1 Solution 2 Redundancy
32
Moussab BenneharPhD defense
Real-Time Experiments and Results
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Platforms Results of Solution 1 Results of Solution 2
Moussab BenneharPhD defense
Experimental Platforms
Non-redundant Redundant
Veloce: 4 DOFs
Delta: 3 DOFs
Dual-V: 3 DOFs
Arrow: 4 DOFs
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Platforms Results of Solution 1 Results of Solution 2
33
Moussab BenneharPhD defense
Non-redundant platforms
Delta robot Veloce robot
3 degrees of freedom (3T).
3 direct drive actuators.
20 Nm of maximum torque per actuator.
4 degrees of freedom (3T1R).
4 direct drive actuators.
127 Nm of max. torque per actuator.
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Platforms Results of Solution 1 Results of Solution 2
34
Moussab BenneharPhD defense
Redundant platforms
Dual-V robot ARROW robot
3 degrees of freedom (3T).
4 direct drive actuators.
127 Nm of max. torque per actuator
4 degrees of freedom (3T1R).
6 linear actuators.
2500 N of max. force per actuator
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Platforms Results of Solution 1 Results of Solution 2
35
Moussab BenneharPhD defense
Original DCAL vs DCAL with nonlinear feedback
gains
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Platforms Results of Solution 1 Results of Solution 2
Moussab BenneharPhD defense
Trac
king
err
ors
Nominal Case RobustnessEs
timat
ed P
aram
eter
s
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Platforms Results of Solution 1 Results of Solution 2
36
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Platforms Results of Solution 1 Results of Solution 2
37
Moussab BenneharPhD defense
Original RISE vs Adaptive RISE
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Platforms Results of Solution 1 Results of Solution 2
Moussab BenneharPhD defense
Trac
king
err
ors
Nominal Case Robustness
Estim
ated
Par
amet
ers
RISE Adaptive RISERISE Adaptive RISE
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Platforms Results of Solution 1 Results of Solution 2
38
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Platforms Results of Solution 1 Results of Solution 2
39
Moussab BenneharPhD defense
Original RISE vs Adaptive RISE
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Platforms Results of Solution 1 Results of Solution 2
Moussab BenneharPhD defense
Trac
king
err
ors
Estim
ated
Par
amet
ers
Nominal Case Robustness
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Platforms Results of Solution 1 Results of Solution 2
40
Moussab BenneharPhD defense
L1 Adaptive Control vs PD Control
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Platforms Results of Solution 1 Results of Solution 2
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Platforms Results of Solution 1 Results of Solution 2
41
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Platforms Results of Solution 1 Results of Solution 2
42
Moussab BenneharPhD defense
Original L1-AC vs Augmented L1-AC with nominal feedforward
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Platforms Results of Solution 1 Results of Solution 2
Moussab BenneharPhD defense
Trac
king
err
ors
Estim
ated
Par
amet
ers
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Platforms Results of Solution 1 Results of Solution 2
43
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Platforms Results of Solution 1 Results of Solution 2
44
Moussab BenneharPhD defense
Original L1-AC vs Extended L1-AC with adaptive feedforward
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Platforms Results of Solution 1 Results of Solution 2
Moussab BenneharPhD defense
Trac
king
err
ors
Estim
ated
Par
amet
ers
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Platforms Results of Solution 1 Results of Solution 2
47
Moussab BenneharPhD defense
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Platforms Results of Solution 1 Results of Solution 2
48
Moussab BenneharPhD defense
Conclusions and Future Work
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Conclusion Future Work Publuications
Moussab BenneharPhD defense
Tackled problemControl of parallel manipulators for high-speed trajectory tracking.
Main challengesComplex and highly nonlinear behavior.Uncertain / time-varying dynamics.Redundant actuation.
Proposed SolutionsSolution 1: Enhanced model-based adaptive controlSolution 2: Extended L1 adaptive control
ValidationReal-time experiments on available platforms:
Non redundant: Delta, Veloce.Redundant: Dual-V, Arrow.
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Conclusion Future Work Publications
49
Moussab BenneharPhD defense
Investigate the use of other nonlinear gains for the proposed extended DCAL
controller.
The use of other adaptation terms and laws in combination of RISE control.
Implement and compare all developed controllers on one platform (Arrow for
instance).
Evaluate the performance of L1 adaptive control based methods for payload
changing scenarios.
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Conclusion Future Work Publications
50
Moussab BenneharPhD defense
List of PublicationsJournal papers1. M. Bennehar, A. Chemori, M. Bouri, L.F Jenni and F. Pierrot. Adaptive RISE-based Control of the 3-DOFs Delta Parallel Robot. Submitted to International Journal of Control. 2. M. Bennehar, A. Chemori, and F. Pierrot. A New Revised Desired Compensation Adaptive Control for Enhanced Tracking: Application to RA-PKMs. Submitted to Advanced Robotics.3. M. Bennehar, A. Chemori, F. Pierrot and V. Creuze. Extended Model-Based Feedforward Compensation in L1 Adaptive Control for Mechanical Manipulators: Design and Experiments. Frontiers in Robotics and AI, 2015. To appear4. M. Bennehar, A. Chemori, S. Krut, and F. Pierrot. Control of Redundantly Actuated PKMs for Closed-Shape Trajectories Tracking with Real-Time Experiments. Transactions on Systems, Signals and Devices, 2015. To appear.
International conferences1. M. Bennehar, A. Chemori, and F. Pierrot. Feedforward AugmentedL1 Adaptive Controller for Parallel Kinematic Manipulators with Improved Tracking. Submitted to IEEE International Conference on Robotics and Automation (ICRA’16).2. M. Bennehar, A. Chemori, and F. Pierrot. L1 Adaptive Control of Parallel Kinematic Manipulators: Design and Real-Time Experiments. In IEEE International Conference on Robotics and Automation (ICRA’15), pages 1587-1592, Seattle, Washington, USA, May 2015.3. M. Bennehar, A. Chemori, and F. Pierrot. A Novel RISE-Based Adaptive Feedforward Controller for Redundantly Actuated Parallel Manipulators. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’14), pages 2389-2394, Chicago, Illinois, USA, September 2014.4. M. Bennehar, A. Chemori, and F. Pierrot. A New Extension of Direct Compensation Adaptive Control and Its Real-Time Application to Redundantly Actuated PKMs. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’14), pages 1670-1675, Chicago, Illinois, USA, September 2014.5. M. Bennehar, A. Chemori, S. Krut, and F. Pierrot. Continuous Closed-Form Trajectories Generation and Control of Redundantly Actuated Parallel Kinematic Manipulators. In Multi-Conference on Systems, Signals and Devices (SSD’14), Barcelona, Spain, 2014.
State of the Art Modeling Proposed Solutions Redundancy Experiments Conclusion
Conclusion Future Work Publications
51
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