Dynamic control allocation with asymptotic tracking of time-varying control input commands

2005 ◽  
Author(s):  
Yu Luo ◽  
D.B. Doman
Keyword(s):  
Dynamic Control ◽  
Control Allocation ◽  
Control Input ◽  
Time Varying ◽  
Asymptotic Tracking

Dynamic Control Allocation for Tracking Time-Varying Control Demand

2008 ◽  
Vol 31 (4) ◽  
pp. 1150-1157 ◽  
Author(s):  
W. C. Arun Kishore ◽  
S. Sen ◽  
G. Ray ◽  
T. K. Ghoshal
Keyword(s):  
Dynamic Control ◽  
Control Allocation ◽  
Time Varying ◽  
Tracking Time

Multi-UAV Cooperative Transportation Using Dynamic Control Allocation and a Reinforcement Learning Compensator

2021 ◽  
Author(s):  
Shuai Li ◽  
Damiano Zanotto
Keyword(s):  
Reinforcement Learning ◽  
Dynamic Control ◽  
Parallel Robots ◽  
Control Allocation ◽  
Control Input ◽  
Tracking Errors ◽  
Tracking Method ◽  
On Line ◽  
Multi Uav ◽  
Allocation Approach

Abstract This paper proposes a new trajectory tracking method for a 6 degree-of-freedom (DOF) cable-suspended payload controlled by a team of quadrotors. Using the modeling convention of reconfigurable cable-driven parallel robots (RCDPRs) the coupled dynamics of the payload and the quadrotors are derived. Based on this dynamic model, a new dynamic control allocation approach is introduced to optimally distribute the virtual control input (i.e., the wrench to be exerted on the payload) among the cables and generate reference positions for the quadrotors on-line, while avoiding collisions between quadrotors and accounting for cable tension constraints. Furthermore, a new reinforcement-learning (RL) compensator is proposed to reduce tracking errors caused by the constraints in the quadrotors’ thrusts. Numerical simulations are conducted to validate the proposed approach.

Obstacle avoidance control of a human-in-the-loop mobile robot system using harmonic potential fields

Robotica ◽  
2017 ◽  
Vol 36 (4) ◽  
pp. 463-483 ◽  
Author(s):  
C. Ton ◽  
Z. Kan ◽  
S. S. Mehta
Keyword(s):  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Sliding Mode ◽  
Harmonic Potential ◽  
Control Allocation ◽  
Control Input ◽  
Continuous Control ◽  
Control Effort ◽  
Human In The Loop ◽  
Allocation Function

SUMMARYThis paper considers applications where a human agent is navigating a semi-autonomous mobile robot in an environment with obstacles. The human input to the robot can be based on a desired navigation objective, which may not be known to the robot. Additionally, the semi-autonomous robot can be programmed to ensure obstacle avoidance as it navigates the environment. A shared control architecture can be used to appropriately fuse the human and the autonomy inputs to obtain a net control input that drives the robot. In this paper, an adaptive, near-continuous control allocation function is included in the shared controller, which continuously varies the control effort exerted by the human and the autonomy based on the position of the robot relative to obstacles. The developed control allocation function facilitates the human to freely navigate the robot when away from obstacles, and it causes the autonomy control input to progressively dominate as the robot approaches obstacles. A harmonic potential field-based non-linear sliding mode controller is developed to obtain the autonomy control input for obstacle avoidance. In addition, a robust feed-forward term is included in the autonomy control input to maintain stability in the presence of adverse human inputs, which can be critical in applications such as to prevent collision or roll-over of smart wheelchairs due to erroneous human inputs. Lyapunov-based stability analysis is presented to guarantee finite-time stability of the developed shared controller, i.e., the autonomy guarantees obstacle avoidance as the human navigates the robot. Experimental results are provided to validate the performance of the developed shared controller.

scholarly journals A Fault Tolerant Vehicle Stability Control Using Adaptive Control Allocation

2018 ◽  
Author(s):  
Ozan Temiz ◽  
Melih Cakmakci ◽  
Yildiray Yildiz
Keyword(s):  
Adaptive Control ◽  
Fault Tolerant ◽  
Stability Control ◽  
Steering Wheel ◽  
Slip Angle ◽  
Control Allocation ◽  
Control Input ◽  
Allocation Strategy ◽  
Virtual Control ◽  
Yaw Stability

This paper presents an integrated fault-tolerant adaptive control allocation strategy for four wheel frive - four wheel steering ground vehicles to increase yaw stability. Conventionally, control of brakes, motors and steering angles are handled separately. In this study, these actuators are controlled simultaneously using an adaptive control allocation strategy. The overall structure consists of two steps: At the first level, virtual control input consisting of the desired traction force, the desired moment correction and the required lateral force correction to maintain driver’s intention are calculated based on the driver’s steering and throttle input and vehicle’s side slip angle. Then, the allocation module determines the traction forces at each wheel, front steering angle correction and rear steering wheel angle, based on the virtual control input. Proposed strategy is validated using a non-linear three degree of freedom reduced two-track vehicle model and results demonstrate that the vehicle can successfully follow the reference motion while protecting yaw stability, even in the cases of device failure and changed road conditions.

Extremum Seeking for Plants With a Time-Varying Disturbance: Application to Photovoltaic Maximum Power Point Tracking

2018 ◽  
Vol 141 (1) ◽  
Author(s):  
Michelle A. Kehs ◽  
Hosam K. Fathy
Keyword(s):  
Stability Analysis ◽  
Least Squares ◽  
Maximum Power Point Tracking ◽  
Extremum Seeking ◽  
Control Input ◽  
Time Varying ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

This paper presents an extremum seeking controller for photovoltaic maximum power point tracking (MPPT). The controller belongs to the broad family of “perturb and observe” algorithms, where the terminal voltage of a photovoltaic system is adjusted to maximize its output power. One critical challenge with these algorithms is that it can be difficult to distinguish between changes in photovoltaic power resulting from changes in irradiation versus the control input. With regard to this challenge, we develop an extremum seeking algorithm that uses least-squares estimation to explicitly separate the effect of the control input from the effect of time-varying disturbances. While the use of least-squares estimation in the context of extremum seeking is not new, our separation of time-varying effects is. In addition, our formulation retains much of the structure of traditional extremum seeking, thereby allowing us to perform a stability analysis comparable to the existing literature. This stability analysis assumes the time-varying disturbance to be slow, but we test the controller beyond this limit in simulation for photovoltaic MPPT. We compare our controller to two benchmarks (a similar controller that does not separate time-varying effects and a traditional extremum seeking controller), and our controller outperforms both.

Model-Predictive Dynamic Control Allocation Scheme for Reentry Vehicles

2007 ◽  
Vol 30 (1) ◽  
pp. 100-113 ◽  
Author(s):  
Yu Luo ◽  
Andrea Serrani ◽  
Stephen Yurkovich ◽  
Michael W. Oppenheimer ◽  
David B. Doman
Keyword(s):  
Dynamic Control ◽  
Control Allocation ◽  
Allocation Scheme ◽  
Reentry Vehicles
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