Co-ordination and control of distributed spacecraft systems using convex optimization techniques

2002 ◽  
Vol 12 (2-3) ◽  
pp. 207-242 ◽  
Author(s):  
Michael Tillerson ◽  
Gokhan Inalhan ◽  
Jonathan P. How
Keyword(s):  
Convex Optimization ◽  
Optimization Techniques ◽  
And Control

scholarly journals ScottyActivity: Mixed Discrete-Continuous Planning with Convex Optimization

2018 ◽  
Vol 62 ◽  
pp. 579-664 ◽  
Author(s):  
Enrique Fernandez-Gonzalez ◽  
Brian Williams ◽  
Erez Karpas
Keyword(s):  
Convex Optimization ◽  
Continuous Time ◽  
Trajectory Optimization ◽  
State Of The Art ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
State Variables ◽  
Control Variables ◽  
Model Complex ◽  
And Control

The state of the art practice in robotics planning is to script behaviors manually, where each behavior is typically generated using trajectory optimization. However, in order for robots to be able to act robustly and adapt to novel situations, they need to plan these activity sequences autonomously. Since the conditions and effects of these behaviors are tightly coupled through time, state and control variables, many problems require that the tasks of activity planning and trajectory optimization are considered together. There are two key issues underlying effective hybrid activity and trajectory planning: the sufficiently accurate modeling of robot dynamics and the capability of planning over long horizons. Hybrid activity and trajectory planners that employ mixed integer programming within a discrete time formulation are able to accurately model complex dynamics for robot vehicles, but are often restricted to relatively short horizons. On the other hand, current hybrid activity planners that employ continuous time formulations can handle longer horizons but they only allow actions to have continuous effects with constant rate of change, and restrict the allowed state constraints to linear inequalities. This is insufficient for many robotic applications and it greatly limits the expressivity of the problems that these approaches can solve. In this work we present the ScottyActivity planner, that is able to generate practical hybrid activity and motion plans over long horizons by employing recent methods in convex optimization combined with methods for planning with relaxed plan graphs and heuristic forward search. Unlike other continuous time planners, ScottyActivity can solve a broad class of robotic planning problems by supporting convex quadratic constraints on state variables and control variables that are jointly constrained and that affect multiple state variables simultaneously. In order to support planning over long horizons, ScottyActivity does not resort to time, state or control variable discretization. While straightforward formulations of consistency checks are not convex and do not scale, we present an efficient convex formulation, in the form of a Second Order Cone Program (SOCP), that is very fast to solve. We also introduce several new realistic domains that demonstrate the capabilities and scalability of our approach, and their simplified linear versions, that we use to compare with other state of the art planners. This work demonstrates the power of integrating advanced convex optimization techniques with discrete search methods and paves the way for extensions dealing with non-convex disjoint constraints, such as obstacle avoidance.

Optimization in the Design and Control of Robotic Manipulators: A Survey

1989 ◽  
Vol 42 (4) ◽  
pp. 117-128 ◽  
Author(s):  
S. S. Rao ◽  
P. K. Bhatti
Keyword(s):  
Optimal Control ◽  
Robot Manipulator ◽  
Research Effort ◽  
Industrial Robot ◽  
Load Capacity ◽  
Robotic Manipulators ◽  
Optimization Techniques ◽  
Highly Nonlinear ◽  
Manipulator Design ◽  
And Control

Robotics is a relatively new and evolving technology being applied to manufacturing automation and is fast replacing the special-purpose machines or hard automation as it is often called. Demands for higher productivity, better and uniform quality products, and better working environments are primary reasons for its development. An industrial robot is a multifunctional and computer-controlled mechanical manipulator exhibiting a complex and highly nonlinear behavior. Even though most current robots have anthropomorphic configurations, they have far inferior manipulating abilities compared to humans. A great deal of research effort is presently being directed toward improving their overall performance by using optimal mechanical structures and control strategies. The optimal design of robot manipulators can include kinematic performance characteristics such as workspace, accuracy, repeatability, and redundancy. The static load capacity as well as dynamic criteria such as generalized inertia ellipsoid, dynamic manipulability, and vibratory response have also been considered in the design stages. The optimal control problems typically involve trajectory planning, time-optimal control, energy-optimal control, and mixed-optimal control. The constraints in a robot manipulator design problem usually involve link stresses, actuator torques, elastic deformation of links, and collision avoidance. This paper presents a review of the literature on the issues of optimum design and control of robotic manipulators and also the various optimization techniques currently available for application to robotics.

Genetic Algorithm-Based Maximum Likelihood Parameter Estimation for Transit Buses

2001 ◽  
Author(s):  
Jie Xiao ◽  
Bohdan T. Kulakowski
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Dynamic Models ◽  
Estimation Method ◽  
Optimization Techniques ◽  
Model Parameters ◽  
Parameter Estimation Method ◽  
Transit Buses ◽  
Simulation And Control ◽  
And Control

Abstract Vehicle dynamic models include parameters that qualify the dependence of input forces and moments on state and control variables. The accuracy of the model parameter estimates is important for modeling, simulation, and control. In general, the most accurate method for determining values of model parameters is by direct measurement. However, some parameters of vehicle dynamics, such as suspension damping or moments of inertia, are difficult to measure accurately. This study aims at establishing an efficient and accurate parameter estimation method for developing dynamic models for transit buses, such that this method can be easily implemented for simulation and control design purposes. Based on the analysis of robustness, as well as accuracy and efficiency of optimization techniques, a parameter estimation method that integrates Genetic Algorithms and the Maximum Likelihood Estimation is proposed. Choices of output signals and estimation criterion are discussed involving an extensive sensitivity analysis of the predicted output with respect to model parameters. Other experiment-related aspects, such as imperfection of data acquisition, are also considered. Finally, asymptotic Cramer-Rao lower bounds for the covariance of estimated parameters are obtained. Computer simulation results show that the proposed method is superior to gradient-based methods in accuracy, as well as robustness to the initial guesses and measurement uncertainty.

Design and Control of Forming Processes Using Optimization Techniques

1999 ◽  
Author(s):  
O. Ghouati ◽  
H. Lenoir ◽  
J. C. Gelin ◽  
M. Baida
Keyword(s):  
Finite Element ◽  
Sheet Metal ◽  
Thickness Distribution ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Finite Element Code ◽  
Optimal Process ◽  
Shell Elements ◽  
Numerical Examples ◽  
And Control

Abstract The paper deals with the design and control of forming processes. The finite element code used is based on isoparametric shell elements with three or four nodes, the workpiece being considered as a sheet metal. An optimization technique is used in order to achieve the design or the control of the process by determining the optimal process parameters. The criterion used in that purpose can be based on thickness distribution as well as the respect of the final shape desired for the product. Numerical examples are presented as illustration.

Optimization Methods in Continuous Improvement Models

2019 ◽  
Vol 6 (1) ◽  
pp. 46-59
Author(s):  
Brian J. Galli
Keyword(s):  
Continuous Improvement ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Problem Analysis ◽  
Measure Problem ◽  
Continuous Improvement Model ◽  
Current Utilization ◽  
And Control ◽  
Theory Of Constraint

There are numerous processes used to implement quality, such as TQM, 6 Sigma, and Lean. For these quality processes to remain effective, a continuous improvement model is required and implemented from time to time. Some of these models include Define, Measure, Analyse, Improve and Control (DMAIC); Plan, Do, Check, and Act (PDCA); Identify, Measure, Problem Analysis, Remedy, Operationalize, Validate, and Evaluate (IMPROVE); and Theory of Constraint (TOC). Furthermore, continuous improvement tools need to remain effective through the use of optimization techniques to produce the best possible outcomes. This article discusses some of the current utilization of these tools and proposes different optimizing techniques and variations to make robust quality implementation tools.

Sign in / Sign up

Export Citation Format

Share Document