Design of Cartesian Trajectories and Model Based Control for Robotic Manipulators

1990 ◽  
Author(s):  
Tuna Balkan ◽  
Bilgin Kaftanoglu
Keyword(s):  
Three Dimensional ◽  
Robotic Manipulators ◽  
Practical Method ◽  
Time Frame ◽  
Industrial Applications ◽  
Open Loop ◽  
Model Based Control ◽  
Variable Position ◽  
Model Based ◽  
Joint Variable

Abstract In this study, design and calculation of Cartesian trajectories for robotic manipulators are discussed with a model-based control system at the joint level. The synthetic generation of the continuous path is described and from a predetermined velocity profile and evaluated path length, the time frame of the motion is computed. In the definition of end-effector orientations, instead of using impractical Euler angles, a practical method suitable to industrial applications is given. The trajectory generation and control algorithms are applied to a computer model of a PUMA type manipulator following a three dimensional path. Open-loop joint variable torques are plotted for the given scenerio. Joint variable position and velocity errors are discussed when certain simplifications are performed on the control law for on-line control of manipulators.

Model-Based Control of Combustion Instabilities

2005 ◽  
Author(s):  
Aimee S. Morgans ◽  
Ann P. Dowling
Keyword(s):  
Transfer Function ◽  
Controller Design ◽  
Open Loop ◽  
Operating Conditions ◽  
Combustion Instabilities ◽  
Lean Premixed Combustion ◽  
Model Based Control ◽  
Model Based ◽  
Loop Transfer Function ◽  
Mathematical Approximation

Model-based control has been successfully implemented on an atmospheric pressure lean premixed combustion rig. The rig incorporated a pressure transducer in the combustor to provide a sensor measurement, with actuation provided by a fuel valve. Controller design was based on experimental measurement of the open loop transfer function. This was achieved using a valve input signal which was the sum of an identification signal and a control signal from an empirical controller to eliminate the non-linear limit cycle. The transfer function was measured for the main instability occurring at a variety of operating conditions, and was found to be fairly similar in all cases. Using Nyquist and H∞-loop shaping techniques, several robust controllers were designed, based on a mathematical approximation to the measured transfer function. These were implemented experimentally on the rig, and were found to stabilise it under a variety of operating conditions, with a greater reduction in the pressure spectrum than had been achieved by the empirical controller.

Redundantly Actuated 3RRR Parallel Planar Manipulator - Numerical Analyses of its Dynamics Sensitivity on Modifications of its Platforms Inertia Parameters

2013 ◽  
Vol 198 ◽  
pp. 33-38 ◽  
Author(s):  
Krzysztof Lipiński
Keyword(s):  
Open Loop ◽  
Numerical Analyses ◽  
Loop Model ◽  
Inverse Dynamic ◽  
Model Based Control ◽  
Model Based ◽  
Inertia Parameters ◽  
Redundantly Actuated ◽  
The Right ◽  
Controlled Object

Below, numerical analyses, as well as dynamics of a complex mechanism, are presented. Two objectives are focused: inverse dynamic model is needed (dedicated to be use in the model predictive controller); an identification method is searched (some trajectory parameters are controlled, when specific trajectory is tracked under an open-loop model-based control), as selected parameters must be identified for the model. A redundantly actuated mechatronic system is considered (in the present case some planar, parallel manipulator). When the redundancies are present, traditional torque estimation techniques can not be used directly (a non-square matrix is present in the equations). Thus, the right Moore-Penrose pseudo-inverse is used to estimate them. To model the mechanism - multibody dynamics is used. Its dynamics equations are nonlinear in respect to the joints position (displacements are significant during the mechanism motion). An open-loop model-based control algorithm is postulated for the system (the subcomponents from the closed-loop controller will not be considered in the present paper). As the real parameters of the controlled object can differ from the ones proposed in the controller, obtained trajectories differ from the requested (open-loop controller is used only). Correlations between the inertia error and the trajectory errors are tested. Sensible trajectory parameters are searched to estimate inertia of the controlled object. At present, analyses are restricted to numerical experiments, only.

scholarly journals Control of functional electrical stimulation for restoration of motor function

2017 ◽  
Vol 30 (3) ◽  
pp. 295-312
Author(s):  
Dejan Popovic
Keyword(s):  
Biological Control ◽  
Electrical Stimulation ◽  
Functional Electrical Stimulation ◽  
Control Method ◽  
Level Structure ◽  
Open Loop ◽  
Model Based Control ◽  
Model Based ◽  
Assistive Systems ◽  
Artificial Control

An injury or disease of the central nervous system (CNS) results in significant limitations in the communication with the environment (e.g., mobility, reaching and grasping). Functional electrical stimulation (FES) externally activates the muscles; thus, can restore several motor functions and reduce other health related problems. This review discusses the major bottleneck in current FES which prevents the wider use and better outcome of the treatment. We present a control method that we continually enhance during more than 30 years in the research and development of assistive systems. The presented control has a multi-level structure where upper levels use finite state control and the lower level implements model based control. We also discuss possible communication channels between the user and the controller of the FES. The artificial controller can be seen as the replica of the biological control. The principle of replication is used to minimize the problems which come from the interplay of biological and artificial control in FES. The biological control relies on an extensive network of neurons sending the output signals to the muscles. The network is being trained though many the trial and error processes in the early childhood, but staying open to changes throughout the life to satisfy the particular needs. The network considers the nonlinear and time variable properties of the motor system and provides adaptation in time and space. The presented artificial control method implements the same strategy but relies on machine classification, heuristics, and simulation of model-based control. The motivation for writing this review comes from the fact that many control algorithms have been presented in the literature by the authors who do not have much experience in rehabilitation engineering and had never tested the operations with patients. Almost all of the FES devices available implement only open-loop, sensory triggered preprogrammed sequences of stimulation. The suggestion is that the improvements in the FES devices need better controllers which consider the overall status of the potential user, various effects that stimulation has on afferent and efferent systems, reflexive responses to the FES and direct responses to the FES by non-stimulated sensory-motor systems, and the greater integration of the biological control.

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