On the tracking trajectory using optimal control in a quadrotor helicopter: Experimental results

2015 ◽  
Author(s):  
Orlando Garcia ◽  
Omar Santos ◽  
Hugo Romero ◽  
Sergio Salazar
Keyword(s):  
Optimal Control ◽  
Experimental Results ◽  
Quadrotor Helicopter ◽  
Tracking Trajectory

Stabilization and Path Tracking of a Mini Quadrotor Helicopter: Experimental Results

2019 ◽  
Vol 17 (03) ◽  
pp. 485-492 ◽  
Author(s):  
Gabriela Vieira Lima ◽  
Rafael Monteiro Jorge Alves de Souza ◽  
Aniel Silva de Morais ◽  
Luis Claudio Oliveira Lopes ◽  
Guenia Mara Vieira Ladeira
Keyword(s):  
Path Tracking ◽  
Experimental Results ◽  
Quadrotor Helicopter

Experimental results of a control time delay system using optimal control

2011 ◽  
Vol 33 (1) ◽  
pp. 100-113 ◽  
Author(s):  
Omar Santos ◽  
Liliam Rodríguez-Guerrero ◽  
Omar López-Ortega
Keyword(s):  
Optimal Control ◽  
Time Delay ◽  
Delay System ◽  
Experimental Results ◽  
Time Delay System ◽  
Control Time

scholarly journals Causal Inference of Optimal Control Water Level and Inflow in Reservoir Optimal Operation Using Fuzzy Cognitive Map

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2147 ◽  
Author(s):  
Liu ◽  
Zhou ◽  
He ◽  
Lu ◽  
Jia ◽  
...  
Keyword(s):  
Optimal Control ◽  
Causal Inference ◽  
Water Level ◽  
Causal Relationship ◽  
Optimal Operation ◽  
Cognitive Map ◽  
Experimental Results ◽  
Fuzzy Cognitive Map ◽  
Model Complex ◽  
Control Water

Reservoir optimal operation (ROO) has always been a hot issue in the field of water resources management. Analysis of the relationship of optimal control water level and inflow is conducive to understanding and solving ROO under deterministic inflow conditions. The current research uses a fuzzy cognitive map (FCM) as a tool to effectively model complex systems and then extracts systematic relationship diagrams from the dataset. A new fuzzy cognitive map with offset (FCM-O) is proposed to overcome the causal inference error caused by non-linear mapping of the activation function in a traditional FCM. With the application of inferring the causal relationship between the optimal control water level and inflow of ROO for the Three Gorges Reservoir (TGR), the experimental results show that, compared with FCM in the min data error, FCM-O reduces 11.11% and 7.14% in the training and the testing, respectively. Also, the experimental results of FCM-O are more reasonable than those of FCM. Finally, the following conclusions about the causal inference of optimal control water level and inflow in ROO for TGR are drawn: (1) The optimal control water level in September, October and November needs to be raised as much as possible to raise the water head of power generation, which is mainly affected by the constraints of the maximum operating water level of the reservoir rather than inflow; (2) the optimal control water level in January, February and March is positively affected by the inflow of the adjacent months; (3) the optimal control water level in April is due to the approaching flood season. In order to prevent water discarding, the water level is low and the optimum operation space is small. All of those shows that FCM-O is more competent than FCM in the causal relationship between optimal control water level and inflow in ROO.

A numerical method for solving optimal control problems via Legendre polynomials

2020 ◽  
Vol 37 (8) ◽  
pp. 2735-2759
Author(s):  
Yajing Gu ◽  
Hongyan Yan ◽  
Yuanguo Zhu
Keyword(s):  
Optimal Control ◽  
Duffing Oscillator ◽  
Control Variable ◽  
Experimental Results ◽  
State Variables ◽  
Constant Matrix ◽  
Content Type ◽  
Unconstrained Optimization Problem ◽  
Decision Variables ◽  
Derivatives Of

Purpose The purpose of this paper is to propose an iterative Legendre technique to deal with a continuous optimal control problem (OCP). Design/methodology/approach For the system in the considered problem, the control variable is a function of the state variables and their derivatives. State variables in the problem are approximated by Legendre expansions as functions of time t. A constant matrix is given to express the derivatives of state variables. Therefore, control variables can be described as functions of time t. After that, the OCP is converted to an unconstrained optimization problem whose decision variables are the unknown coefficients in the Legendre expansions. Findings The convergence of the proposed algorithm is proved. Experimental results, which contain the controlled Duffing oscillator problem demonstrate that the proposed technique is faster than existing methods. Originality/value Experimental results, which contained the controlled Duffing oscillator problem demonstrate that the proposed technique can be faster while securing exactness.

Optimal control of a turbocharged direct injection diesel engine by direct method optimization

2018 ◽  
Vol 20 (6) ◽  
pp. 640-652 ◽  
Author(s):  
Jose Manuel Luján ◽  
Carlos Guardiola ◽  
Benjamín Pla ◽  
Alberto Reig
Keyword(s):  
Optimal Control ◽  
Fuel Consumption ◽  
Control Strategy ◽  
Fuel Efficiency ◽  
Optimization Method ◽  
Operating Conditions ◽  
Experimental Results ◽  
Pollutant Emissions ◽  
Engine Fuel ◽  
Optimal Control Strategy

This work studies the effect and performance of an optimal control strategy on engine fuel efficiency and pollutant emissions. An accurate mean value control-oriented engine model has been developed and experimental validation on a wide range of operating conditions was carried out. A direct optimization method based on Euler’s collocation scheme is used in combination with the above model in order to address the optimal control of the engine. This optimization method provides the optimal trajectories of engine controls (fueling rate, exhaust gas recirculation valve position, variable turbine geometry position and start of injection) to reproduce a predefined route (speed trajectory including variable road grade), minimizing fuel consumption with limited [Formula: see text] emissions and a low soot stamp. This optimization procedure is performed for a set of different [Formula: see text] emission limits in order to analyze the trade-off between optimal fuel consumption and minimum emissions. Optimal control strategies are validated in an engine test bench and compared against engine factory calibration. Experimental results show that significant improvements in both fuel efficiency and emissions reduction can be achieved with optimal control strategy. Fuel savings at about 4% and less than half of the factory [Formula: see text] emissions were measured in the actual engine, while soot generation was still low. Experimental results and optimal control trajectories are thoroughly analyzed, identifying the different strategies that allowed those performance improvements.

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