Performance Analysis of Mars-Powered Descent-Based Landing in a Constrained Optimization Control Framework

It is imperative to find new places other than Earth for the survival of human beings. Mars could be the alternative to Earth in the future for us to live. In this context, many missions have been performed to examine the planet Mars. For such missions, planetary precision landing is a major challenge for the precise landing on Mars. Mars landing consists of different phases (hypersonic entry, parachute descent, terminal descent comprising gravity turn, and powered descent). However, the focus of this work is the powered descent phase of landing. Firstly, the main objective of this study is to minimize the landing error during the powered descend landing phase. The second objective involves constrained optimization in a predictive control framework for landing at non-cooperative sites. Different control algorithms like PID and LQR have been developed for the stated problem; however, the predictive control algorithm with constraint handling’s ability has not been explored much. This research discusses the Model Predictive Control algorithm for the powered descent phase of landing. Model Predictive Control (MPC) considers input/output constraints in the calculation of the control law and thus it is very useful for the stated problem as shown in the results. The main novelty of this work is the implementation of Explicit MPC, which gives comparatively less computational time than MPC. A comparison is done among MPC variants in terms of feasibility, constraints handling, and computational time. Moreover, other conventional control algorithms like PID and LQR are compared with the proposed predictive algorithm. These control algorithms are implemented on quadrotor UAV (which emulates the dynamics of a planetary lander) to verify the feasibility through simulations in MATLAB.

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An Economic Model Predictive Control Approach for Wind Power Smoothing and Tower Load Mitigation

Volume 2: Control and Optimization of Connected and Automated Ground Vehicles; Dynamic Systems and Control Education; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Energy Systems; Estimation and Identification; Intelligent Transportation and Vehicles; Manufacturing; Mechatronics; Modeling and Control of IC Engines and Aftertreatment Systems; Modeling and Control of IC Engines and Powertrain Systems; Modeling and Management of Power Systems ◽

10.1115/dscc2018-9032 ◽

2018 ◽

Author(s):

Mohamed M. Alhneaish ◽

Mohamed L. Shaltout ◽

Sayed M. Metwalli

Keyword(s):

Optimal Control ◽

Optimal Control Problem ◽

Model Predictive Control ◽

Wind Power ◽

Predictive Control ◽

Economic Model ◽

Control Algorithm ◽

Fatigue Load ◽

Economic Model Predictive Control ◽

Control Framework

An economic model predictive control framework is presented in this study for an integrated wind turbine and flywheel energy storage system. The control objective is to smooth wind power output and mitigate tower fatigue load. The optimal control problem within the model predictive control framework has been formulated as a convex optimal control problem with linear dynamics and convex constraints that can be solved globally. The performance of the proposed control algorithm is compared to that of a standard wind turbine controller. The effect of the proposed control actions on the fatigue loads acting on the tower and blades is studied. The simulation results, with various wind scenarios, showed the ability of the proposed control algorithm to achieve the aforementioned objectives in terms of smoothing output power and mitigating tower fatigue load at the cost of a minimal reduction of the wind energy harvested.

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Passive Model Predictive Control on a Two-Body Self-Referenced Point Absorber Wave Energy Converter

Energies ◽

10.3390/en14061731 ◽

2021 ◽

Vol 14 (6) ◽

pp. 1731

Author(s):

Dan Montoya ◽

Elisabetta Tedeschi ◽

Luca Castellini ◽

Tiago Martins

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Wave Energy ◽

Power Flow ◽

Linear Constraints ◽

Wave Energy Converter ◽

Control Technique ◽

Computational Time ◽

Energy Converter ◽

Point Absorber

Wave energy is nowadays one of the most promising renewable energy sources; however, wave energy technology has not reached the fully-commercial stage, yet. One key aspect to achieve this goal is to identify an effective control strategy for each selected Wave Energy Converter (WEC), in order to extract the maximum energy from the waves, while respecting the physical constraints of the device. Model Predictive Control (MPC) can inherently satisfy these requirements. Generally, MPC is formulated as a quadratic programming problem with linear constraints (e.g., on position, speed and Power Take-Off (PTO) force). Since, in the most general case, this control technique requires bidirectional power flow between the PTO system and the grid, it has similar characteristics as reactive control. This means that, under some operating conditions, the energy losses may be equivalent, or even larger, than the energy yielded. As many WECs are designed to only allow unidirectional power flow, it is necessary to set nonlinear constraints. This makes the optimization problem significantly more expensive in terms of computational time. This work proposes two MPC control strategies applied to a two-body point absorber that address this issue from two different perspectives: (a) adapting the MPC formulation to passive loading strategy; and (b) adapting linear constraints in the MPC in order to only allow an unidirectional power flow. The results show that the two alternative proposals have similar performance in terms of computational time compared to the regular MPC and obtain considerably more power than the linear passive control, thus proving to be a good option for unidirectional PTO systems.

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Model predictive control—Based distributed control algorithm for bidirectional interlinking converter in hybrid microgrids

International Transactions on Electrical Energy Systems ◽

10.1002/2050-7038.12817 ◽

2021 ◽

Author(s):

Syed Umaid Ali ◽

Muhammad Aamir ◽

Atif Raza Jafri ◽

Umashankar Subramaniam ◽

Faheem Haroon ◽

...

Keyword(s):

Model Predictive Control ◽

Distributed Control ◽

Predictive Control ◽

Control Algorithm ◽

Distributed Control Algorithm

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Model predictive control-based control algorithm for a target-chaser maneuvering situation

Advanced Robotics ◽

10.1080/01691864.2021.1955002 ◽

2021 ◽

pp. 1-12

Author(s):

A. S. M. Gilimalage ◽

S. Kimura

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Control Algorithm

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Incorporating model predictive control with fuzzy approximation for robot manipulation under remote center of motion constraint

Complex & Intelligent Systems ◽

10.1007/s40747-021-00418-6 ◽

2021 ◽

Author(s):

Hang Su ◽

Junhao Zhang ◽

Ziyu She ◽

Xin Zhang ◽

Ke Fan ◽

...

Keyword(s):

Minimally Invasive Surgery ◽

Minimally Invasive ◽

Model Predictive Control ◽

Predictive Control ◽

Robot Manipulator ◽

Robot Assisted ◽

External Disturbances ◽

Fuzzy Approximation ◽

Control Framework ◽

Motion Constraint

AbstractRemote center of motion (RCM) constraint has attracted many research interests as one of the key challenges for robot-assisted minimally invasive surgery (RAMIS). Although it has been addressed by many studies, few of them treated the motion constraint with an independent workspace solution, which means they rely on the kinematics of the robot manipulator. This makes it difficult to replicate the solutions on other manipulators, which limits their population. In this paper, we propose a novel control framework by incorporating model predictive control (MPC) with the fuzzy approximation to improve the accuracy under the motion constraint. The fuzzy approximation is introduced to manage the kinematic uncertainties existing in the MPC control. Finally, simulations were performed and analyzed to validate the proposed algorithm. By comparison, the results prove that the proposed algorithm achieved success and satisfying performance in the presence of external disturbances.

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