AMOS DP Research Cruise 2016: Academic Full-Scale Testing of Experimental Dynamic Positioning Control Algorithms Onboard R/V Gunnerus

Author(s):  
Roger Skjetne ◽  
Mikkel E. N. Sørensen ◽  
Morten Breivik ◽  
Svenn A. T. Værnø ◽  
Astrid H. Brodtkorb ◽  
...  

In order to validate relevant dynamic positioning (DP) control algorithms in a realistic environment, a full-scale DP test campaign, the AMOS DP Research Cruise 2016 (ADPRC’16), was organized in a collaboration between the NTNU Centre for Autonomous Marine Operations and Systems (NTNU AMOS) and the company Kongsberg Maritime onboard the research vessel (R/V) Gunnerus. To the authors’ best knowledge, closed-loop DP feedback control algorithms have never been tested full-scale on a ship in an academic research experiment before. However, we have now achieved this by coding our algorithms into a test-module of the DP system, as prepared by Kongsberg Maritime. Among the tested algorithms is an output feedback control law with both good transient and steady-state performance. In another experiment, different adaptive backstepping control laws for DP were tested to compare and contrast their performance and properties. A hybrid state observer with a performance monitoring function proposed to switch between two observers, choosing the best one at any time instant, was also part of the test scope. For this, necessary measurements (including acceleration measurements) were logged to be able to rerun and validate the observer algorithms in post-processing. Finally, several experiments were done to test a pseudo-derivative feedback control law for DP. The feedback mechanism was tested with and without a feedforward disturbance rejection term, called acceleration feedforward. This paper reports the experimental setup, test program, and an overview of results from the ADPRC’16 campaign.

Author(s):  
Chuong H. Nguyen ◽  
Alexander Leonessa

A simulation study to control the motion of a human arm using muscle excitations as inputs is presented to validate a recently developed adaptive output feedback controller for a class of unknown multi-input multi-output (MIMO) systems. The main contribution of this paper is to extend the results of Nguyen and Leonessa (2014, “Adaptive Predictor-Based Output Feedback Control for a Class of Unknown MIMO Linear Systems,” ASME Paper No. DSCC2014-6214; 2014, “Adaptive Predictor-Based Output Feedback Control for a Class of Unknown MIMO Linear Systems: Experimental Results,” ASME Paper No. DSCC2014-6217; and 2015, “Adaptive Predictor-Based Output Feedback Control for a Class of Unknown MIMO Systems: Experimental Results,” American Control Conference, pp. 3515–3521) by combining a recently developed fast adaptation technique and a new controller structure to derive a simple approach for a class of high relative degree uncertain systems. Specifically, the presented control approach relies on three components: a predictor, a reference model, and a controller. The predictor is designed to predict the systems output for any admissible control input. A full state feedback control law is then derived to control the predictor output to approach the reference system. The control law avoids the recursive step-by-step design of backstepping and remains simple regardless of the system relative degree. Ultimately, the control objective of driving the actual system output to track the desired trajectory is achieved by showing that the system output, the predictor output, and the reference system trajectories all converge to each other. Thelen and Millard musculotendon models (Thelen, D. G., 2003, “Adjustment of Muscle Mechanics Model Parameters to Simulate Dynamic Contractions in Older Adults,” ASME J. Biomech. Eng., 125(1), pp. 70–77; Millard, M, Uchida, T, Seth, A, and Delp, Scott L., 2013, “Flexing Computational Muscle: Modeling and Simulation of Musculotendon Dynamics,” ASME J. Biomech. Eng., 135(2), p. 021005) are used to validate the proposed controller fast tracking performance and robustness.


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