Output Redefinition Technique Research Articles

Trajectory Planning through Model Inversion of an Underactuated Spatial Gantry Crane Moving in Structured Cluttered Environments

Handling suspended loads in cluttered environments is critical due to the oscillations arising while the load is traveling. Exploiting active control algorithms is often unviable in industrial applications, due to the necessity of installing sensors on the load side, which is expensive and often impractical due to technological limitations. In this light, this paper proposes a trajectory planning method for underactuated, non-flat, non-minimum phase spatial gantry crane moving in structured cluttered environments. The method relies on model inversion. First, the system dynamics is partitioned into actuated and unactuated coordinates and then the load displacements are described as a non-linear separable function of these. The unactuated dynamic is unstable; hence, the displacement, velocity, and acceleration references are modified through the output redefinition technique. Finally, platform trajectory is computed, and the desired displacements of the load are obtained. The effectiveness of the proposed method is assessed through numerical and experimental tests performed on a laboratory testbed composed by an Adept Quattro robot moving a pendulum. The load is moved in a cluttered environment, and collisions are avoided while simultaneously tracking the prescribed trajectory effectively.

Workspace Tracking Control of Two-Flexible-Link Manipulator Using Distributed Control Strategy

A distributed nonlinear control strategy for two-flexible-link manipulators is presented to track a desired trajectory in the robot’s workspace. The inverse dynamics problem is solved by transforming the desired trajectory from the workspace to the joint space using an intermediate space, called virtual space, and then using the quasi-static approach. To solve the nonminimum phase problem, an output redefinition technique is used. This output consists of the motor’s angle augmented with a weighted value of the link’s extremity. The distributed control strategy consists in controlling the last link by assuming that the first link is stable and follows its desired trajectories. The control law is developed to stabilize the error dynamics and to guarantee bounded internal dynamics such that the new output is as close as possible to the tip. The weighted parameter defining the noncollocated output is then selected. The same procedure is applied to control and stabilize the first link. The asymptotical stability is proved using Lyapunov theory. This algorithm is applied to a two-flexible-link manipulator in the horizontal plane, and simulations showed a good tracking of the desired trajectory in the workspace.

Workspace trajectory tracking control for two-flexible-link manipulator through output redefinition

In this paper, a control strategy and stability analysis for two-flexible-link manipulator are presented to track a desired trajectory in the workspace. The inverse dynamics problem is solved using virtual space and the quasi-static approach. Flexible manipulators are non-minimum phase systems when the controlled output is the tip position. To overcome this problem, an output redefinition technique is used. Two steps are presented to control the manipulator’s tip position. First, assuming that the first link is stable, we develop the control law for the last link to stabilise the error dynamics using the feedback linearisation approach. The weighted parameter defining the non-collocated output is selected to guarantee bounded internal dynamics such that the output is as close as possible to the tip. Second, the same strategy is followed for the first link. Simulation results are presented to show good tracking of desired trajectory in the workspace.

Neutral-Network -Based Output-Redefinition Control of an Unmanned Aerial Vehicle

in this thesis, a flight control design of an unmanned aerial vehicle (UAV) using output redefinition based neutral network control technique is presented. The UAV model chosen is a nonlinear non-minimum phase system. The output redefinition technique is used in a way such that the resulting system is minimum phase and can be inverted.NARMA-L2 neural network is trained off-line to identify the forward dynamics of the UAV model with the redefined output, and then inverted to force the real output to approximately track the desired trajectory. The results show that a good tracking performance can be achieved using this control scheme.

Neural network based feedback linearization control of an unmanned aerial vehicle

This paper presents a flight control design for an unmanned aerial vehicle (UAV) using a nonlinear autoregressive moving average (NARMA-L2) neural network based feedback linearization and output redefinition technique. The UAV investigated is non-minimum phase. The output redefinition technique is used in such a way that the resulting system to be inverted is a minimum phase system. The NARMA-L2 neural network is trained off-line for forward dynamics of the UAV model with redefined output and is then inverted to force the real output to approximately track a command input. Simulation results show that the proposed approaches have good performance.

ROBOTIC UNMANNED AERIAL VEHICLE TRAJECTORY TRACKING CONTROL

Control of Unmanned Aerial Vehicles is a difficult task. One of the most challenging problems are nonlinear nature of the vehicle dynamics and the second is related to the nonminimum phase system. There are several methods to design nonlinear controller. One of them is so-called feedback linearization. Even, after successful design of nonlinear controller several important issues like nonminimum phase problem remain. The system with nonminimum phase dynamics needs to have outputs redefined. The output redefinition technique is used in a way such that the resulting system to be inverted is a minimum phase system. The complex calculation related to the system dynamics redefinition make a real-time computation very difficult. The real-time control system needs to be fast and reliable. In order to make a real-time control possible, a neural networks method has been developed and presented. The NARMA-L2 Neural Network is trained off-line to identify the forward dynamics of the UAV model with the redefined output, which is subsequently inverted to force the real output to approximately track a command input. Simulation results show that the proposed neural network method has overall good performance.