scholarly journals Minimum-Time Trajectories for Steered Agent With Constraints on Speed, Lateral Acceleration, and Turning Rate

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
William Lewis Scott ◽  
Naomi Ehrich Leonard
Keyword(s):  
Minimum Principle ◽  
Single Agent ◽  
Open Loop ◽  
Minimum Time ◽  
Lateral Acceleration ◽  
Optimal Controls ◽  
Coverage Problem ◽  
Time Optimal ◽  
Multi Agent ◽  
Heading Angle

We present time-optimal trajectories for a steered agent with constraints on speed, lateral acceleration, and turning rate for the problem of reaching a point on the plane in minimum time with free terminal heading angle. Both open-loop and state-feedback forms of optimal controls are derived through application of Pontryagin's minimum principle. We apply our results for the single agent to solve a multi-agent coverage problem in which each agent has constraints on speed, lateral acceleration, and turning rate.

Near-Minimum Time Feedback Controller for Manipulators Using On-Line Time Scaling of Trajectories

1996 ◽  
Vol 118 (2) ◽  
pp. 300-308 ◽  
Author(s):  
A. Kumagai ◽  
D. Kohli ◽  
R. Perez
Keyword(s):  
Feedback Control ◽  
Control Theory ◽  
Open Loop ◽  
Feedback Controller ◽  
Minimum Time ◽  
Control Action ◽  
Time Scaling ◽  
Time Problem ◽  
Time Optimal ◽  
Input Torque

A near-minimum time feedback controller for robotic manipulators with bounded input torques is developed. Since the bang-bang input torque obtained from the timeoptimal control theory leaves little or no room for the extra torque of the feedback control action, it is difficult to combine a minimum time open-loop controller with an additional feedback controller. A simple solution to this problem has been to solve the minimum time problem using arbitrarily reduced torque bounds so that a torque head room is created for the feedback control action. Such a scheme, however, wastes considerable input torque potential and gives significantly larger execution time of the trajectory than the theoretical minimum time calculated from the time-optimal control theory. A stable feedback controller is developed in this paper which applies a time scaling method to move a manipulator in near-minimum time using the allowable input torques efficiently. This new feedback controller algorithm adapts to an uncertain environment and automatically adjusts the desired speed along a specified path to be as fast as possible while avoiding the velocity saturation condition. Numerical examples of the near-minimum time feedback controller are provided using a two-link SCARA manipulator.

scholarly journals An iterative method for time optimal control of dynamic systems

2011 ◽  
Vol 21 (1) ◽  
pp. 5-23 ◽  
Author(s):  
Navvab Kashiri ◽  
Mohammad Ghasemi ◽  
Morteza Dardel
Keyword(s):  
Optimal Control ◽  
Iterative Method ◽  
Dynamic Systems ◽  
Minimum Principle ◽  
Minimum Energy ◽  
Optimal Solution ◽  
Initial Guess ◽  
Open Loop ◽  
Time Optimal Control ◽  
Time Optimal

An iterative method for time optimal control of dynamic systemsAn iterative method for time optimal control of a general type of dynamic systems is proposed, subject to limited control inputs. This method uses the indirect solution of open-loop optimal control problem. The necessary conditions for optimality are derived from Pontryagin's minimum principle and the obtained equations lead to a nonlinear two point boundary value problem (TPBVP). Since there are many difficulties in finding the switching points and in solving the resulted TPBVP, a simple iterative method based on solving the minimum energy solution is proposed. The method does not need finding the switching point so that the resulted TPBVP can be solved by usual algorithms such as shooting and collocation. Also, since the solution of TPBVPs is sensitive to initial guess, a short procedure for making the proper initial guess is introduced. To this end, the accuracy and efficiency of the proposed method is demonstrated using time optimal solution of some systems: harmonic oscillator, robotic arm, double spring-mass problem with coulomb friction and F-8 aircraft.

scholarly journals Application of Minimum-time Optimal Control System in Buck-Boost Bi-linear Converters

2017 ◽  
Vol 7 (4) ◽  
pp. 1753-1758
Author(s):  
S. M. M. Shariatmadar ◽  
S. M. J. Jafarian
Keyword(s):  
Optimal Control ◽  
Control System ◽  
Output Voltage ◽  
Minimum Principle ◽  
Voltage Regulation ◽  
Minimum Time ◽  
Time Optimal Control ◽  
Optimal Switching ◽  
Optimal Control System ◽  
Time Optimal

In this study, the theory of minimum-time optimal control system in buck-boost bi-linear converters is described, so that output voltage regulation is carried out within minimum time. For this purpose, the Pontryagin's Minimum Principle is applied to find optimal switching level applying minimum-time optimal control rules. The results revealed that by utilizing an optimal switching level instead of classical switching patterns, output voltage regulation will be carried out within minimum time. However, transient energy index of increased overvoltage significantly reduces in order to attain minimum time optimal control in reduced output load. The laboratory results were used in order to verify numerical simulations.

scholarly journals Multi-Agent Reinforcement Learning: A Review of Challenges and Applications

2021 ◽  
Vol 11 (11) ◽  
pp. 4948
Author(s):  
Lorenzo Canese ◽  
Gian Carlo Cardarilli ◽  
Luca Di Di Nunzio ◽  
Rocco Fazzolari ◽  
Daniele Giardino ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Mathematical Models ◽  
Learning Algorithms ◽  
Single Agent ◽  
Critical Issues ◽  
Multi Agent ◽  
Pros And Cons ◽  
Application Fields

In this review, we present an analysis of the most used multi-agent reinforcement learning algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the most critical issues that must be taken into account in their extension to multi-agent scenarios. The analyzed algorithms were grouped according to their features. We present a detailed taxonomy of the main multi-agent approaches proposed in the literature, focusing on their related mathematical models. For each algorithm, we describe the possible application fields, while pointing out its pros and cons. The described multi-agent algorithms are compared in terms of the most important characteristics for multi-agent reinforcement learning applications—namely, nonstationarity, scalability, and observability. We also describe the most common benchmark environments used to evaluate the performances of the considered methods.

scholarly journals On-Demand Channel Bonding in Heterogeneous WLANs: A Multi-Agent Deep Reinforcement Learning Approach

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2789 ◽  
Author(s):  
Hang Qi ◽  
Hao Huang ◽  
Zhiqun Hu ◽  
Xiangming Wen ◽  
Zhaoming Lu
Keyword(s):  
Reinforcement Learning ◽  
Transmission Rate ◽  
Single Agent ◽  
Time Of Day ◽  
Action Space ◽  
Traffic Load ◽  
Traffic Demand ◽  
Channel Bonding ◽  
On Demand ◽  
Multi Agent

In order to meet the ever-increasing traffic demand of Wireless Local Area Networks (WLANs), channel bonding is introduced in IEEE 802.11 standards. Although channel bonding effectively increases the transmission rate, the wider channel reduces the number of non-overlapping channels and is more susceptible to interference. Meanwhile, the traffic load differs from one access point (AP) to another and changes significantly depending on the time of day. Therefore, the primary channel and channel bonding bandwidth should be carefully selected to meet traffic demand and guarantee the performance gain. In this paper, we proposed an On-Demand Channel Bonding (O-DCB) algorithm based on Deep Reinforcement Learning (DRL) for heterogeneous WLANs to reduce transmission delay, where the APs have different channel bonding capabilities. In this problem, the state space is continuous and the action space is discrete. However, the size of action space increases exponentially with the number of APs by using single-agent DRL, which severely affects the learning rate. To accelerate learning, Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is used to train O-DCB. Real traffic traces collected from a campus WLAN are used to train and test O-DCB. Simulation results reveal that the proposed algorithm has good convergence and lower delay than other algorithms.

scholarly journals Multi-mobile agent itinerary planning algorithms for data gathering in wireless sensor networks: A review paper

2017 ◽  
Vol 13 (1) ◽  
pp. 155014771668484 ◽  
Author(s):  
Huthiafa Q Qadori ◽  
Zuriati A Zulkarnain ◽  
Zurina Mohd Hanapi ◽  
Shamala Subramaniam
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Mobile Agent ◽  
Mobile Agents ◽  
Single Agent ◽  
Data Gathering ◽  
Optimal Number ◽  
Wireless Sensor ◽  
Itinerary Planning ◽  
Multi Agent

Recently, wireless sensor networks have employed the concept of mobile agent to reduce energy consumption and obtain effective data gathering. Typically, in data gathering based on mobile agent, it is an important and essential step to find out the optimal itinerary planning for the mobile agent. However, single-agent itinerary planning suffers from two primary disadvantages: task delay and large size of mobile agent as the scale of the network is expanded. Thus, using multi-agent itinerary planning overcomes the drawbacks of single-agent itinerary planning. Despite the advantages of multi-agent itinerary planning, finding the optimal number of distributed mobile agents, source nodes grouping, and optimal itinerary of each mobile agent for simultaneous data gathering are still regarded as critical issues in wireless sensor network. Therefore, in this article, the existing algorithms that have been identified in the literature to address the above issues are reviewed. The review shows that most of the algorithms used one parameter to find the optimal number of mobile agents in multi-agent itinerary planning without utilizing other parameters. More importantly, the review showed that theses algorithms did not take into account the security of the data gathered by the mobile agent. Accordingly, we indicated the limitations of each proposed algorithm and new directions are provided for future research.

Coverage Control for Mobile Sensor Networks on a Circle

2014 ◽  
Vol 02 (03) ◽  
pp. 243-248 ◽  
Author(s):  
Cheng Song ◽  
Gang Feng
Keyword(s):  
Sensor Networks ◽  
Mobile Agents ◽  
Mobile Sensor Networks ◽  
Mobile Sensors ◽  
Mobile Sensor ◽  
Coverage Problem ◽  
Control Laws ◽  
Coverage Control ◽  
Order Preservation ◽  
Multi Agent

This paper investigates the coverage problem for mobile sensor networks on a circle. The goal is to minimize the largest distance from any point on the circle to its nearest sensor while preserving the mobile sensors' order. The coverage problem is translated into a multi-agent consensus problem by showing that the largest distance from any point to its nearest sensor is minimized if the counterclockwise distance between each sensor and its right neighbor reaches a consensus. Distributed control laws are also developed to drive the mobile agents to the optimal configuration with order preservation. Simulation results illustrate the effectiveness of the proposed control laws.

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