scholarly journals Leaderless Maneuver Guidance and Event-Triggered Formation Control for Distributed Multi-Space-Robot Systems

2021 ◽  
Author(s):  
Xuan Wang ◽  
Xing Chu ◽  
Yunhe Meng ◽  
Guoguang wen ◽  
Qian Jiang
Keyword(s):  
Formation Control ◽  
Linear Quadratic Regulator ◽  
Space Robot ◽  
Target Point ◽  
Linear Quadratic ◽  
Guidance Control ◽  
Control Framework ◽  
Robot Systems ◽  
Event Times ◽  
Event Triggered

Abstract In this paper, the distributed displacement-based formation and leaderless maneuver guidance control problems of multi-space-robot systems are investigated by introducing event-triggered control update mechanisms. A distributed formation and leaderless maneuver guidance control framework is first constructed, which includes two parallel controllers, namely, an improved linear quadratic regulator and a distributed consensus-based formation controller. By applying this control framework, the desired formation configuration of multi-space-robot systems can be achieved and the center of leaderless formation can converge to the target point globally. Second, a pull-based event triggering mechanism is introduced to the distributed formation controller, which makes the control update independent of the events of neighboring robots, avoids unnecessary control updates, and saves the extremely limited energy of space robots. Furthermore, the potential Zeno behaviors have been excluded by proving a positive lower bound for the inter-event times. Finally, numerical simulation verifies the effectiveness of the theoretical results.

Least Squares Tracking on the Euclidean Group

2000 ◽  
Author(s):  
Youngmo Han ◽  
F. C. Park
Keyword(s):  
Least Squares ◽  
Motion Control ◽  
Obstacle Avoidance ◽  
Analytic Solution ◽  
Linear Quadratic Regulator ◽  
Euclidean Group ◽  
Linear Quadratic ◽  
Complex Assembly ◽  
Compliant Motion ◽  
Control Framework

Abstract A large class of problems in robotics, e.g., tracking with obstacle avoidance, compliant motion control, and complex assembly, can be formulated as a least-squares tracking problem on the Euclidean group subject to constraints on the state and/or control. In this paper we develop a general, mathematically rigorous optimal control framework for this class of problems, and derive a simple closed-form analytic solution. Our formalism can be viewed as generalization to the Euclidean group of the linear quadratic regulator (LQR) subject to state equality constraints. Examples from force-guided complex assembly and tracking with obstacle avoidance are given.

scholarly journals Active and Reactive Power Control for Wind Turbines Based DFIG Using LQR Controller with Optimal Gain-Scheduling

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Ashraf Radaideh ◽  
Mu’men Bodoor ◽  
Ayman Al-Quraan
Keyword(s):  
Wind Speed ◽  
Wind Turbines ◽  
Reactive Power ◽  
Linear Quadratic Regulator ◽  
Gain Scheduling ◽  
Optimal Controller ◽  
Linear Quadratic ◽  
Active And Reactive Power ◽  
Control Framework ◽  
Whale Optimization

This paper proposes an optimal gain-scheduling for linear quadratic regulator (LQR) control framework to improve the performance of wind turbines based Doubly Fed Induction Generator (DFIG). Active and reactive power decoupling is performed using the field-oriented vector control which is used to simplify DFIG’s nonlinearity and derive a compact linearized state-space model. The performance of the optimal controller represented by a linear quadratic regulator is further enhanced using the whale optimization algorithm in a multiobjective optimization environment. Adaptiveness against wind speed variation is achieved in an offline training process at a discretized wind speed domain. Lookup tables are used to store the optimal controller parameter and called upon during the online implementation. The control framework further integrates the effects of pitch angle control mechanism for active power ancillary services and possible improvements on reactive power support. The results of the proposed control framework improve the overall performance of the system compared to the conventional PI controller. Comparison is performed using the MATLAB Simulink platform.

Event-triggered formation control of n-link networked stochastic robotic manipulators

2022 ◽  
pp. 095965182110678
Author(s):  
Ali Azarbahram ◽  
Naser Pariz ◽  
Mohammad-Bagher Naghibi-Sistani ◽  
Reihaneh Kardehi Moghaddam
Keyword(s):  
Formation Control ◽  
Robotic Manipulators ◽  
Robotic Manipulator ◽  
Multi Agent Systems ◽  
Environmental Loads ◽  
Control Framework ◽  
Non Linear ◽  
Control Event ◽  
Multi Agent ◽  
Event Triggered

This article proposes an event-triggered control framework to satisfy the tracking formation performance for a group of uncertain non-linear n-link robotic manipulators. The robotic manipulators are configured as a multi-agent system and they communicate over a directed graph (digraph). Furthermore, the non-linear robotic manipulator-multi-agent systems are subject to stochastic environmental loads. By introducing extra virtual controllers in the final step of the backstepping design, a total number of n event-triggering mechanisms are introduced independently for each link of all the robotic manipulator agents to update the control inputs in a fully distributed manner. More precisely, the actuator of each link of a particular agent is capable of being updated independent of other link actuator updates. A rigorous proof of the convergence of all the closed-loop signals in probability is then given and the Zeno phenomenon is excluded for the control event-triggered architectures. The simulation experiments finally quantify the effectiveness of proposed approach in terms of reducing the number of control updates and handling the stochastic environmental loads.

Bipedal walking and push recovery with a stepping strategy based on time-projection control

2019 ◽  
Vol 38 (5) ◽  
pp. 587-611 ◽  
Author(s):  
Salman Faraji ◽  
Hamed Razavi ◽  
Auke J. Ijspeert
Keyword(s):  
Linear Quadratic Regulator ◽  
Joint Space ◽  
Bipedal Walking ◽  
Linear Quadratic ◽  
Tracking Errors ◽  
Biped Robots ◽  
Push Recovery ◽  
Control Framework ◽  
Time Projection ◽  
Series Elastic Actuators

In this paper, we present a simple control framework for online push recovery on biped robots with dynamic stepping properties. Owing to relatively heavy legs in our humanoid robot COMAN, we use a linear model called 3LP, which is composed of three pendulums to take swing and torso dynamics into account. Based on 3LP equations, we formulate discrete linear quadratic regulator (LQR) controllers and use a particular time-projection method to adjust footstep locations during the motion continuously. This process, which is based on pelvis and swing foot tracking errors, naturally considers swing dynamics and leads to leg-retraction properties. Suggested adjustments are added to the Cartesian 3LP gaits and converted into joint-space trajectories through inverse kinematics. Fixed and adaptive foot lift strategies are also used to ensure enough ground clearance in perturbed walking conditions. The proposed control architecture is robust, yet uses very simple state estimation and basic position tracking. We rely on series elastic actuators to absorb impacts while introducing simple laws to compensate for spring compressions. Extensive experiments on COMAN (real) and Atlas (simulated) robots demonstrate the functionality of different control blocks and prove the effectiveness of time-projection in extreme push recovery scenarios. We also show self-produced and emergent walking gaits when the robot is subject to continuous dragging forces. These gaits feature dynamic walking robustness with minimal reliance on the ankles and avoiding any active zero moment point (ZMP) control. The proposed architecture is therefore generic, computationally very fast and yet with no critical parameter to tune.

A Study on Linear Quadratic Regulator with the Desired Low-Pass Property

2013 ◽  
Vol 133 (12) ◽  
pp. 2167-2175 ◽  
Author(s):  
Katsuhiko Fuwa ◽  
Satoshi Murayama ◽  
Tatsuo Narikiyo
Keyword(s):  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Low Pass

scholarly journals Neural Network Based Contact Force Control Algorithm for Walking Robots

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 287
Author(s):  
Byeongjin Kim ◽  
Soohyun Kim
Keyword(s):  
Neural Network ◽  
Contact Force ◽  
Force Control ◽  
Control Algorithm ◽  
Position Control ◽  
Linear Quadratic Regulator ◽  
Walking Robots ◽  
Test Model ◽  
Linear Quadratic ◽  
Contact Force Control

Walking algorithms using push-off improve moving efficiency and disturbance rejection performance. However, the algorithm based on classical contact force control requires an exact model or a Force/Torque sensor. This paper proposes a novel contact force control algorithm based on neural networks. The proposed model is adapted to a linear quadratic regulator for position control and balance. The results demonstrate that this neural network-based model can accurately generate force and effectively reduce errors without requiring a sensor. The effectiveness of the algorithm is assessed with the realistic test model. Compared to the Jacobian-based calculation, our algorithm significantly improves the accuracy of the force control. One step simulation was used to analyze the robustness of the algorithm. In summary, this walking control algorithm generates a push-off force with precision and enables it to reject disturbance rapidly.

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