Adaptive Generalized Predictive Controller and Cartesian Force Control for Robot Arm Using Dynamics and Geometric Identification

2018 ◽  
Vol 30 (6) ◽  
pp. 927-942 ◽  
Author(s):  
Shohei Hagane ◽  
Liz Katherine Rincon Ardila ◽  
Takuma Katsumata ◽  
Vincent Bonnet ◽  
Philippe Fraisse ◽  
...  
Keyword(s):  
Predictive Control ◽  
High Performance ◽  
Wall Painting ◽  
Robot Arm ◽  
Inertial Parameters ◽  
Control Scheme ◽  
Predictive Controller ◽  
Adaptive Predictive Control ◽  
Predictive Controllers ◽  
Contact Tasks

In realistic situations such as human-robot interactions or contact tasks, robots must have the capacity to adapt accordingly to their environment, other processes and systems. Adaptive model based controllers, that requires accurate dynamic and geometric robot’s information, can be used. Accurate estimations of the inertial and geometric parameters of the robot and end-effector are essential for the controller to demonstrate a high performance. However, the identification of these parameters can be time-consuming and complex. Thus, in this paper, a framework based on an adaptive predictive control scheme and a fast dynamic and geometric identification process is proposed. This approach was demonstrated using a KUKA lightweight robot (LWR) in the performance of a force-controlled wall-painting task. In this study, the performances of a generalized predictive control (GPC), adaptive proportional derivative gravity compensation, and adaptive GPC (AGPC) were compared. The results revealed that predictive controllers are more suitable than adaptive PD controllers with gravitational compensation, owing to the use of well-identified geometric and inertial parameters.

scholarly journals Time-Varying Scheme for Noncentralized Model Predictive Control of Large-Scale Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Alfredo Núñez ◽  
Carlos Ocampo-Martinez ◽  
José María Maestre ◽  
Bart De Schutter
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Large Scale ◽  
Reduction Methods ◽  
Predictive Controller ◽  
Online Methods ◽  
Water Tanks ◽  
Predictive Controllers ◽  
Decentralized Model

The noncentralized model predictive control (NC-MPC) framework in this paper refers to any distributed, hierarchical, or decentralized model predictive controller (or a combination of them) the structure of which can change over time and the control actions of which are not obtained based on a centralized computation. Within this framework, we propose suitable online methods to decide which information is shared and how this information is used between the different local predictive controllers operating in a decentralized, distributed, and/or hierarchical way. Evaluating all the possible structures of the NC-MPC controller leads to a combinatorial optimization problem. Therefore, we also propose heuristic reduction methods, to keep the number of NC-MPC problems tractable to be solved. To show the benefits of the proposed framework, a case study of a set of coupled water tanks is presented.

An Adaptive–Predictive control scheme with dynamic Hysteresis Modulation applied to a DC–DC buck converter

2020 ◽  
Vol 105 ◽  
pp. 240-255
Author(s):  
Nubia Ilia Ponce de León Puig ◽  
Dimitar Bozalakov ◽  
Leonardo Acho ◽  
Lieven Vandevelde ◽  
José Rodellar
Keyword(s):  
Predictive Control ◽  
Buck Converter ◽  
Dynamic Hysteresis ◽  
Control Scheme ◽  
Adaptive Predictive Control

Spherical Modeling and Nonlinear Model Predictive Control of Electroporation

2011 ◽  
Author(s):  
Jingang Yi ◽  
Jianbo Li ◽  
Hao Lin
Keyword(s):  
Pore Size ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Transmembrane Potential ◽  
Dynamic Models ◽  
Effective Means ◽  
Spherical Model ◽  
Biological Research ◽  
Control Scheme ◽  
Predictive Controller

Electroporation is an effective means to deliver molecules into the cellular cytoplasm, and has been widely applied in both biological research and clinical applications. The process however suffers from low viability and efficiency. In this work, we present a multi-pulse feedback control scheme for enhancing viability and efficiency of electroporation process. We first present a spherical model for cell transmembrane potential (TMP) dynamics to describe the spatial distribution of electric potential on cell membrane. Then we analyze the models and discuss the bifurcation property of the system. Based on the dynamic models, we design a nonlinear model predictive controller (NMPC) to track pore size profiles in electroporation. We demonstrate that by using NMPC, the pore size can be precisely regulated to targeted values for drug delivery applications. The control design is illustrated through simulation examples.

scholarly journals High performance decoupled active and reactive power control for three-phase grid-tied inverters using model predictive control

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Mohamed Azab
Keyword(s):  
Model Predictive Control ◽  
Cost Function ◽  
Predictive Control ◽  
High Performance ◽  
Reactive Power ◽  
Grid Voltage ◽  
Active And Reactive Power ◽  
Weight Factors ◽  
Three Phase ◽  
Control Scheme

AbstractFinite control set-model predictive control (FCS-MPC) is employed in this paper to control the operation of a three-phase grid-connected string inverter based on a direct PQ control scheme. The main objective is to achieve high-performance decoupled control of the active and reactive powers injected to the grid from distributed energy resources (DER).The FCS-MPC scheme instantaneously searches for and applies the optimum inverter switching state that can achieve certain goals, such as minimum deviation between reference and actual power; so that both power components (P and Q) are well controlled to their reference values.In addition, an effective method to attenuate undesired cross coupling between the P and Q control loops, which occurs only during transient operation, is investigated. The proposed method is based on the variation of the weight factors of the terms of the FCS-MPC cost function, so a higher weight factor is assigned to the cost function term that is exposed to greater disturbance. Empirical formulae of optimum weight factors as functions of the reference active and reactive power signals are proposed and mathematically derived. The investigated FCS-MPC control scheme is incorporated with the LVRT function to support the grid voltage in fulfilling and accomplishing the up-to-date grid codes. The LVRT algorithm is based on a modification of the references of active and reactive powers as functions of the instantaneous grid voltage such that suitable values of P and Q are injected to the grid during voltage sag.The performance of the elaborated FCS-MPC PQ scheme is studied under various operating scenarios, including steady-state and transient conditions. Results demonstrate the validity and effectiveness of the proposed scheme with regard to the achievement of high-performance operation and quick response of grid-tied inverters during normal and fault modes.

scholarly journals Tracking Model Predictive Control for Docking Maneuvers of a CubeSat with a Big Spacecraft

Aerospace ◽  
2021 ◽  
Vol 8 (8) ◽  
pp. 197
Author(s):  
Fabrizio Stesina
Keyword(s):  
Predictive Control ◽  
High Performance ◽  
Complex Model ◽  
Model Predictive Controller ◽  
Environmental Disturbances ◽  
Small Satellites ◽  
Safety Constraints ◽  
Predictive Controller ◽  
Tracking Model ◽  
External Inspection

The release and retrieval of a CubeSat from a big spacecraft is useful for the external inspection and monitoring of the big spacecraft. However, docking maneuvers during the retrieval are challenging since safety constraints and high performance must be achieved, considering the small dimensions and the actual small satellites technology. The trajectory control is crucial to have a soft, accurate, quick, and propellant saving docking. The present paper deals with the design of a tracking model predictive controller (TMPC) tuned to achieve the stringent docking requirements for the retrieval of a CubeSat within the cargo bay of a large cooperative vehicle. The performance of the TMPC is verified using a complex model that includes non-linearities, uncertainties of the CubeSat parameters, and environmental disturbances. Moreover, 300 Monte Carlo runs demonstrate the robustness of the TMPC solution.

Model predictive control based on chaos particle swarm optimization for nonlinear processes with constraints

Kybernetes ◽  
2014 ◽  
Vol 43 (9/10) ◽  
pp. 1469-1482 ◽  
Author(s):  
Adel Taeib ◽  
Moêz Soltani ◽  
Abdelkader Chaari
Keyword(s):  
Particle Swarm Optimization ◽  
Predictive Control ◽  
Fuzzy Controller ◽  
Nonlinear Process ◽  
Model Predictive Controller ◽  
Swarm Optimization ◽  
Content Type ◽  
Control Scheme ◽  
Predictive Controller ◽  
New Type

Purpose – The purpose of this paper is to propose a new type of predictive fuzzy controller. The desired nonlinear system behavior is described by a set of Takagi-Sugeno (T-S) model. However, due to the complexity of the real processes, obtaining a high quality control with a short settle time, a periodical step response and zero steady-state error is often a difficult task. Indeed, conventional model predictive control (MPC) attempts to minimize a quadratic cost over an extended control horizon. Then, the MPC is insufficient to adapt to changes in system dynamics which have characteristics of complex constraints. In addition, it is shown that the clustering algorithm is sensitive to random initialization and may affect the quality of obtaining predictive fuzzy controller. In order to overcome these problems, chaos particle swarm optimization (CPSO) is used to perform model predictive controller for nonlinear process with constraints. The practicality and effectiveness of the identification and control scheme is demonstrated by simulation results involving simulations of a continuous stirred-tank reactor. Design/methodology/approach – A new type of predictive fuzzy controller. The proposed algorithm based on CPSO is used to perform model predictive controller for nonlinear process with constraints. Findings – The results obtained using this the approach were comparable with other modeling approaches reported in the literature. The proposed control scheme has been show favorable results either in the absence or in the presence of disturbance compared with the other techniques. It confirms the usefulness and robustness of the proposed controller. Originality/value – This paper presents an intelligent model predictive controller MPC based on CPSO (MPC-CPSO) for T-S fuzzy modeling with constraints.

Analysis the Effect of Undesirable Factors on Stability of Model Predictive Controller

2008 ◽  
Author(s):  
Shadi Ansarpanahi ◽  
Samsul Bahari Mohd Noor
Keyword(s):  
Predictive Control ◽  
Time Delays ◽  
Control Algorithm ◽  
Computer Control ◽  
Phase System ◽  
Control Scheme ◽  
Predictive Controller ◽  
Noise Error ◽  
The Stability ◽  
Drive Model

MPC also known as moving or receding horizon control, is a feedback control scheme that has originated in industry as a real-time computer control algorithm to solve linear and nonlinear multi-variable problems that have constraints and time delays. Since disturbances can drive model predictive control into non-convexity and instability this problem has attracted many researchers. The stability studies in this paper are illustrated in presence of colored noise, error in delay estimation, unstable and non-minimum phase system by means of numerical example. The simulation is carried out using an example, which is the main contribution of the paper.

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