scholarly journals Flexible model predictive control based on multivariable online adjustment mechanism for robust gait generation

2020 ◽  
Vol 17 (1) ◽  
pp. 172988141988729 ◽  
Author(s):  
Sheng Dong ◽  
Zhaohui Yuan ◽  
Xiaojun Yu ◽  
Muhammad Tariq Sadiq ◽  
Jianrui Zhang ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Optimization Problems ◽  
Current System ◽  
Dynamic Balance ◽  
Generation Mechanism ◽  
Gait Generation ◽  
Step Duration ◽  
Flexible Model ◽  
Step Distance

The gait generation algorithm considering both step distance adjustment and step duration adjustment could improve the anti-disturbance ability of the humanoid robot, which is very important to the dynamic balance, but the step duration adjustment often brings non-convex optimization problems. In order to avoid this situation and improve the robustness of the gait generator, a gait generation mechanism based on flexible model predictive control is proposed in this article. Specifically, the step distance adjustment and step duration adjustment are set to be optimization objectives, while the change of pressure center is treated as the optimal input to minimize those objectives. With the current system state being used for online re-optimization, a feedback gait generator is formed to realize the strong stability of variable speed and variable step distance walking of the robot. The main contributions of this work are twofold. First, a gait generation mechanism based on flexible model predictive control is proposed, which avoids the problem of nonlinear optimization. Second, a variety of feasible optimization constraints were considered, they can be used on platforms with different computing resources. Simulations are conducted to verify the effectiveness of the proposed mechanism. Results show that as compared with those considering step adjustment only, the proposed method largely improves the compensation ability of disturbance and shortens the adjustment time.

scholarly journals Knee-stretched walking with toe-off and heel-strike for a position-controlled humanoid robot based on model predictive control

2021 ◽  
Vol 18 (4) ◽  
pp. 172988142110362
Author(s):  
Zelin Huang ◽  
Zhangguo Yu ◽  
Xuechao Chen ◽  
Qingqing Li ◽  
Libo Meng ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Energy Efficient ◽  
Humanoid Robot ◽  
Center Of Mass ◽  
Heel Strike ◽  
Gait Generation ◽  
Double Support ◽  
Transition Moments ◽  
Efficient Gait

Knee-stretched walking is considered to be a human-like and energy-efficient gait. The strategy of extending legs to obtain vertical center of mass trajectory is commonly used to avoid the problem of singularities in knee-stretched gait generation. However, knee-stretched gait generation utilizing this strategy with toe-off and heel-strike has kinematics conflicts at transition moments between single support and double support phases. In this article, a knee-stretched walking generation with toe-off and heel-strike for the position-controlled humanoid robot has been proposed. The position constraints of center of mass have been considered in the gait generation to avoid the kinematics conflicts based on model predictive control. The method has been verified in simulation and validated in experiment.

scholarly journals Nonfragile Robust Model Predictive Control for Uncertain Constrained Systems with Time-Delay Compensation

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Wei Jiang ◽  
Hong-li Wang ◽  
Jing-hui Lu ◽  
Wei-wei Qin ◽  
Guang-bin Cai
Keyword(s):  
Time Delay ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Optimization Problems ◽  
Sufficient Conditions ◽  
Constrained Systems ◽  
Delay Compensation ◽  
Robust Model Predictive Control ◽  
Robust Model ◽  
Time Delay Compensation

This study investigates the problem of asymptotic stabilization for a class of discrete-time linear uncertain time-delayed systems with input constraints. Parametric uncertainty is assumed to be structured, and delay is assumed to be known. In Lyapunov stability theory framework, two synthesis schemes of designing nonfragile robust model predictive control (RMPC) with time-delay compensation are put forward, where the additive and the multiplicative gain perturbations are, respectively, considered. First, by designing appropriate Lyapunov-Krasovskii (L-K) functions, the robust performance index is defined as optimization problems that minimize upper bounds of infinite horizon cost function. Then, to guarantee closed-loop stability, the sufficient conditions for the existence of desired nonfragile RMPC are obtained in terms of linear matrix inequalities (LMIs). Finally, two numerical examples are provided to illustrate the effectiveness of the proposed approaches.

scholarly journals Model Predictive Control for Load Frequency Control with Wind Turbines

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Yi Zhang ◽  
Xiangjie Liu ◽  
Yujia Yan
Keyword(s):  
Model Predictive Control ◽  
Power System ◽  
Predictive Control ◽  
Wind Turbines ◽  
Optimization Problems ◽  
Load Frequency Control ◽  
Distributed Model ◽  
Load Frequency ◽  
The Impact ◽  
A Performance

Reliable load frequency (LFC) control is crucial to the operation and design of modern electric power systems. Considering the LFC problem of a four-area interconnected power system with wind turbines, this paper presents a distributed model predictive control (DMPC) based on coordination scheme. The proposed algorithm solves a series of local optimization problems to minimize a performance objective for each control area. The scheme incorporates the two critical nonlinear constraints, for example, the generation rate constraint (GRC) and the valve limit, into convex optimization problems. Furthermore, the algorithm reduces the impact on the randomness and intermittence of wind turbine effectively. A performance comparison between the proposed controller with and that without the participation of the wind turbines is carried out. Good performance is obtained in the presence of power system nonlinearities due to the governors and turbines constraints and load change disturbances.

Multiagent Coordination Optimization Based Model Predictive Control Strategy With Application to Balanced Resource Allocation

2014 ◽  
Author(s):  
Haopeng Zhang ◽  
Qing Hui
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategy ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Nonlinear Problems ◽  
Pso Algorithm ◽  
Network Resource ◽  
Multiagent Coordination

Model predictive control (MPC) is a heuristic control strategy to find a consequence of best controllers during each finite-horizon regarding to certain performance functions of a dynamic system. MPC involves two main operations: estimation and optimization. Due to high complexity of the performance functions, such as, nonlinear, non-convex, large-scale objective functions, the optimization algorithms for MPC must be capable of handling those problems with both computational efficiency and accuracy. Multiagent coordination optimization (MCO) is a recently developed heuristic algorithm by embedding multiagent coordination into swarm intelligence to accelerate the searching process for the optimal solution in the particle swarm optimization (PSO) algorithm. With only some elementary operations, the MCO algorithm can obtain the best solution extremely fast, which is especially necessary to solve the online optimization problems in MPC. Therefore, in this paper, we propose an MCO based MPC strategy to enhance the performance of the MPC controllers when addressing non-convex large-scale nonlinear problems. Moreover, as an application, the network resource balanced allocation problem is numerically illustrated by the MCO based MPC strategy.

Efficient Lithium-Ion Battery Model Predictive Control Using Differential Flatness-Based Pseudospectral Methods

2015 ◽  
Author(s):  
Ji Liu ◽  
Guang Li ◽  
Hosam K. Fathy
Keyword(s):  
Model Predictive Control ◽  
Lithium Ion Battery ◽  
Predictive Control ◽  
Trajectory Optimization ◽  
Optimization Problems ◽  
Lithium Ion ◽  
Side Reaction ◽  
Particle Model ◽  
Differential Flatness ◽  
Pseudospectral Methods

This paper proposes an efficient nonlinear model predictive control (NMPC) framework to solve nonconvex lithium-ion battery trajectory optimization problems for battery management systems (BMS). It is challenging to solve these problems online due to complexity and nonconvexity. To address these challenges, we combine four established techniques from the control literature. First, we represent the single particle model (SPM) using orthogonal projection techniques. Second, we exploit the differential flatness of Fick’s second law of diffusion to capture all of the dynamics in one electrode using a single scalar trajectory of a “flat output” variable. Third, we optimize the above flat output trajectories using pseudospectral methods. Fourth, we employ the NMPC strategy to solve the battery trajectory optimization problem online. The proposed NMPC framework is demonstrated by solving 2 optimal charging problems accounting for physics-based side reaction constraints and is shown to be twice as computationally efficient as pseudospectral online optimization alone.

scholarly journals A MPC Strategy for the Optimal Management of Microgrids Based on Evolutionary Optimization

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1371 ◽  
Author(s):  
Álvaro Rodríguez del Nozal ◽  
Daniel Gutiérrez Reina ◽  
Lázaro Alvarado-Barrios ◽  
Alejandro Tapia ◽  
Juan Manuel Escaño
Keyword(s):  
Energy Storage ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Optimization Problems ◽  
Storage System ◽  
Energy Storage System ◽  
Evolutionary Strategy ◽  
Operational Cost ◽  
Prediction Horizon ◽  
Photovoltaic Plant

In this paper, a novel model predictive control strategy, with a 24-h prediction horizon, is proposed to reduce the operational cost of microgrids. To overcome the complexity of the optimization problems arising from the operation of the microgrid at each step, an adaptive evolutionary strategy with a satisfactory trade-off between exploration and exploitation capabilities was added to the model predictive control. The proposed strategy was evaluated using a representative microgrid that includes a wind turbine, a photovoltaic plant, a microturbine, a diesel engine, and an energy storage system. The achieved results demonstrate the validity of the proposed approach, outperforming a global scheduling planner-based on a genetic algorithm by 14.2% in terms of operational cost. In addition, the proposed approach also better manages the use of the energy storage system.

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