Minimization of Power Losses in Cooperating Manipulators

1992 ◽  
Vol 114 (2) ◽  
pp. 213-219 ◽  
Author(s):  
M. Nahon ◽  
J. Angeles
Keyword(s):  
Linear Equations ◽  
Optimal Solution ◽  
Internal Force ◽  
Power Losses ◽  
Performance Indices ◽  
Linear Quadratic ◽  
Quadratic Optimization Problem ◽  
Minimum Power ◽  
Underdetermined System ◽  
Cooperating Manipulators

The control of multiple manipulators handling a common object entails the solution of an underdetermined system of linear equations which represents the system’s dynamics. In order to choose an optimal solution to this problem, various approaches have been proposed: minimum internal force and minimum power, among others. The present work investigates an approach for minimizing the power losses in these systems. It is shown that the power imparted to the manipulator/payload system cannot be optimized once the system’s motion is prescribed. However, assuming certain loss characteristics for the dc servomotors commonly used on robotic manipulators, it is shown that the minimization of power losses can be cast as a linear-quadratic optimization problem. Local and global performance indices are introduced to allow comparison of the minimum power loss and the minimum internal force approaches. An example of two Puma 560 robots handling a common payload is shown to demonstrate the proposed technique.

Solving of the Modified Filter Algebraic Riccati Equation for H-infinity fault detection filtering

2017 ◽  
Vol 9 (1) ◽  
pp. 57-77
Author(s):  
Zsolt Horváth ◽  
András Edelmayer
Keyword(s):  
Fault Detection ◽  
Riccati Equation ◽  
Optimization Problem ◽  
Computational Cost ◽  
Optimal Solution ◽  
Algebraic Riccati Equation ◽  
Linear Matrix ◽  
Optimization Approach ◽  
Linear Quadratic ◽  
Quadratic Optimization Problem

AbstractThe objective of this paper is solving of the Modified Filter Algebraic Riccati Equation (MFARE) for calculating of the filter gain. The results are used for model-based fault detection filtering of faults in the air path of diesel engines. The Hinfinity optimization approach requires the solution of a linear-quadratic optimization problem that leads to the solution of MFARE. In our paper two basic concepts for solving MFARE are examined, namely the analytically implemented gamma-iteration and casting the problem as a convex optimization problem based on Linear Matrix Inequalities (LMIs).The algorithms are implemented in MATLAB. Each algorithm has to ensure the condition for a global convergence and also has to deliver an optimal solution. Not at least, the computational cost has to be as small as possible.

scholarly journals Optimal linear quadratic control for wireless sensor and actuator networks with random delays and packet dropouts

2018 ◽  
Vol 14 (6) ◽  
pp. 155014771877956
Author(s):  
Zhuwei Wang ◽  
Lihan Liu ◽  
Chao Fang ◽  
Xiaodong Wang ◽  
Pengbo Si ◽  
...  
Keyword(s):  
Frequency Control ◽  
Optimal Solution ◽  
Load Frequency Control ◽  
Wireless Sensor ◽  
Linear Quadratic Control ◽  
Linear Quadratic ◽  
Relay Nodes ◽  
Packet Dropouts ◽  
Frequency Control System ◽  
Optimal Linear

In this article, the optimal linear quadratic control problem is considered for the wireless sensor and actuator network with stochastic network-induced delays and packet dropouts. Considering the event-driven relay nodes, the optimal solution is obtained, which is a function of the current plant state and all past control signals. It is shown that the optimal control law is the same for all locations of the controller placement. Since the perfect plant state information is available at the sensor, the optimal controller should be collocated with the sensor. In addition, some issues such as the plant state noise and suboptimal solution are also discussed. The performance of the proposed scheme is investigated by an application of the load frequency control system in power grid.

Construction Method of Constraint Matrices Corresponded by an Optimal Solution

2014 ◽  
Vol 513-517 ◽  
pp. 1617-1620
Author(s):  
Xiao Liu ◽  
Wei Li ◽  
Peng Zhen Liu
Keyword(s):  
Linear Programming ◽  
Programming Problem ◽  
Linear Programming Problem ◽  
Linear Equations ◽  
Optimal Solution ◽  
Construction Method ◽  
Interval Linear Programming ◽  
Interval Linear

Through deep analysis of the solvability, which is based on interval linear equations and inequalities systems, for a given optimal solution to interval linear programming problem, we propose the construction method of constraint matrices corresponded by the optimal solution in this paper.

Advanced Robust Optimization Approach for Design Optimization With Interval Uncertainty Using Sequential Quadratic Programming

2013 ◽  
Author(s):  
Jianhua Zhou ◽  
Mian Li
Keyword(s):  
Robust Optimization ◽  
Linear Equations ◽  
Variable Parameter ◽  
Optimal Solution ◽  
Interval Uncertainty ◽  
Computational Time ◽  
Optimization Approach ◽  
Engineering Optimization ◽  
Loop Optimization ◽  
New Approach

Uncertainty is inevitable in real world. It has to be taken into consideration, especially in engineering optimization; otherwise the obtained optimal solution may become infeasible. Robust optimization (RO) approaches have been proposed to deal with this issue. Most existing RO algorithms use double-looped structures in which a large amount of computational efforts have been spent in the inner loop optimization to determine the robustness of candidate solutions. In this paper, an advanced approach is presented where no optimization run is required to be performed for robustness evaluations in the inner loop. Instead, a concept of Utopian point is proposed and the corresponding maximum variable/parameter variation will be obtained by just solving a set of linear equations. The obtained robust optimal solution from the new approach may be conservative, but the deviation from the true robust optimal solution is very small given the significant improvement in the computational efficiency. Six numerical and engineering examples are tested to show the applicability and efficiency of the proposed approach, whose solutions and computational time are compared with those from a similar but double-looped approach, SQP-RO, proposed previously.

Robust fault detection of dynamic systems via genetic algorithms

1997 ◽  
Vol 211 (5) ◽  
pp. 357-364 ◽  
Author(s):  
R J Patton ◽  
J Chen ◽  
G P Liu
Keyword(s):  
Genetic Algorithm ◽  
Fault Detection ◽  
Flight Control ◽  
Optimal Solution ◽  
Inequality Constraints ◽  
Performance Indices ◽  
Frequency Distributions ◽  
Modelling Uncertainty ◽  
Detection Systems ◽  
Robust Fault Detection

This paper presents a new approach to the design of robust fault detection systems via a genetic algorithm. To achieve robustness, a number of performance indices are introduced, which are expressed in the frequency domain to account for the frequency distributions of incipient faults, noise and modelling uncertainty. All objectives are then reformulated into a set of inequality constraints on performance indices. A genetic algorithm is thus used to search an optimal solution to satisfy these inequality constraints. The approach developed is applied to a flight control system example and results show that incipient sensor faults can be detected reliably in the presence of modelling uncertainty.

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