Constrained stochastic predictive control of linear systems with uncertain communication

2021 ◽  
Vol 69 (9) ◽  
pp. 771-781
Author(s):  
Jannik Hahn ◽  
Olaf Stursberg
Keyword(s):  
Predictive Control ◽  
Communication Channel ◽  
Terminal State ◽  
Networked Control System ◽  
Input Constraints ◽  
Control Performance ◽  
Control Approach ◽  
Control Scheme ◽  
Control Of Linear Systems ◽  
The Given

Abstract This paper proposes a scheme of model predictive control for single-loop networked control system (NCS) with probabilistically modeled communication channel and disturbances. Uncertainties of the communication network are projected onto a tailored probability for the satisfaction of state and input constraints. The proposed receding horizon control scheme uses a probabilistic terminal state and set to establish a balance between control performance and state probability distribution, while satisfying the given constraints. In addition to describing the control approach, its properties are discussed, and it is illustrated by an example.

scholarly journals Coordinated path-following control for networked unmanned surface vehicles

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142093057
Author(s):  
Dong-Liang Chen ◽  
Guo-Ping Liu ◽  
Ru-Bo Zhang ◽  
Xingru Qu
Keyword(s):  
Predictive Control ◽  
Controller Design ◽  
Control Strategies ◽  
Path Following ◽  
Control Performance ◽  
Negative Effects ◽  
Unmanned Surface Vehicles ◽  
Path Following Control ◽  
Control Scheme ◽  
Networked Predictive Control

In this article, the coordinated path-following control problem for networked unmanned surface vehicles is investigated. The communication network brings time delays and packet dropouts to the fleet, which will have negative effects on the control performance of the fleet. To attenuate the negative effects, a novel networked predictive control scheme is proposed. By introducing the predictive error into the control scheme, the proposed control strategy admits some advantages compared with existing networked predictive control strategies, for example, a degree of robustness to disturbances, lower requirements for the computing capacity of the onboard processors, high flexibility in controller design, and so on. Conditions that guarantee the control performance of the overall system are derived in the theoretical analysis. At last, experiments on hovercraft test beds are implemented to verify the effectiveness of the proposed control scheme.

scholarly journals Model Based Robust Predictive Control of Ship Roll/Yaw Motions with Input Constraints

2020 ◽  
Vol 10 (10) ◽  
pp. 3377
Author(s):  
Zhongjia Jin ◽  
Sheng Liu ◽  
Lincheng Jin ◽  
Wei Chen ◽  
Weilin Yang
Keyword(s):  
Predictive Control ◽  
State Feedback ◽  
State Feedback Control ◽  
Linear Matrix ◽  
Control Input ◽  
Input Constraints ◽  
Control Performance ◽  
Parameter Uncertainties ◽  
Yaw Control ◽  
Control Gain

A robust H∞-type state feedback model predictive control (H∞-SFMPC) with input constraints is proposed to optimize the control performance during the ship sailing. Specifically, the approach employed in this paper is able to optimize the closed-loop performance with respect to an H∞-type cost function which predicts the system performance based on the actual model instead of the ideal model. As a result, the effect caused by disturbances is attenuated. The state feedback control gain for the control input of the rudder-fin joint roll/yaw control system is obtained by solving a constrained convex optimization problem in terms of linear matrix inequalities. Simulations are carried out, which reveal that the proposed approach has outstanding control performance. Furthermore, it is found that the approach also has significant robustness with respect to parameter uncertainties.

Determination of uncertain parameters of a two-axis gimbal and motion tracking via Fuzzy logic control approach

2020 ◽  
Vol 39 (5) ◽  
pp. 6565-6577
Author(s):  
Roya Jahanandish ◽  
Amir Khosravifard ◽  
Ramin Vatankhah
Keyword(s):  
Inverse Problem ◽  
Fuzzy Control ◽  
Measurement Errors ◽  
Motion Tracking ◽  
Controller Design ◽  
Uncertain Parameters ◽  
Control Performance ◽  
Unknown Parameters ◽  
Control Approach ◽  
Control Scheme

This paper proposes a new method to improve fuzzy control performance accuracy in the stabilization of the two-axis gimbal system. To this end, due to the fact that the knowledge of the accurate behavior of the system plays a substantial role in fuzzy control performance, all the uncertain parameters of the dynamic model such as friction, mass imbalance and moments of inertia are estimated prior to the controller design and without imposing any computational burden on the control scheme. To estimate the uncertainties and disturbances which exist in the dynamic equations, an identification process formulated as an inverse problem is utilized, and the Gauss– Newton method is adopted for the optimization process. Regarding the severe sensitivity of inverse problems to measurement errors, this undesirable effect is reduced by using a proper smoothing technique. In order to increase the accuracy of the final results, a novel procedure for calculation of the sensitivity coefficients of the inverse problem is proposed. This procedure is based on the direct differentiation of the governing equations with respect to the unknown parameters. At the end, simulation results are presented to confirm the effectiveness of the proposed scheme.

scholarly journals An Approach to Distributed Min-Max Model Predictive Control of Linear Systems with Parametric Uncertainty

2015 ◽  
Vol 13 (1-2) ◽  
pp. 2-9
Author(s):  
Alexandra Grancharova ◽  
Sorin Olaru
Keyword(s):  
Predictive Control ◽  
Uncertain Systems ◽  
Optimization Problem ◽  
Closed Loop ◽  
Parametric Uncertainty ◽  
Uncertain System ◽  
Input Constraints ◽  
Dual Decomposition ◽  
Suggested Approach ◽  
Control Of Linear Systems

Abstract In this paper, a suboptimal approach to distributed closed-loop min-max MPC for uncertain systems consisting of polytopic subsystems with coupled dynamics subject to both state and input constraints is proposed. The approach applies the dynamic dual decomposition method and reformulates the original centralized min-max MPC problem into a distributed optimization problem. The suggested approach is illustrated on a simulation example of an uncertain system consisting of two interconnected polytopic subsystems.

scholarly journals Consensus of Multi-agent Systems with Input Constraints Based on Distributed Predictive Control Scheme

2020 ◽  
Vol 62 (3) ◽  
pp. 1335-1349
Author(s):  
Xiaolong Liang ◽  
Yueqi Hou ◽  
Lyulong He ◽  
Jiaqiang Zhang ◽  
Jie Zhu ◽  
...  
Keyword(s):  
Predictive Control ◽  
Input Constraints ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Control Scheme ◽  
Multi Agent

scholarly journals Simplified Motion Control of a Vehicle manipulator for the Coordinated Mobile Manipulation

2020 ◽  
Vol 70 (1) ◽  
pp. 72-81 ◽  
Author(s):  
Swati Mishra ◽  
Santhakumar Mohan ◽  
Santosh Kumar Vishvakarma
Keyword(s):  
Motion Control ◽  
Experimental Testing ◽  
Kinematic Control ◽  
Control Approach ◽  
Operational Space ◽  
Control Scheme ◽  
Manipulator Arm ◽  
The Given ◽  
Pseudo Inverse ◽  
Kinematic Motion

This paper considers a resolved kinematic motion control approach for controlling a spatial serial manipulator arm that is mounted on a vehicle base. The end-effector’s motion of the manipulator is controlled by a novel kinematic control scheme, and the performance is compared with the well-known operational-space control scheme. The proposed control scheme aims to track the given operational-space (end-effector) motion trajectory with the help of resolved configuration-space motion without using the Jacobian matrix inverse or pseudo inverse. The experimental testing results show that the suggested control scheme is as close to the conventional operational-space kinematic control scheme.

scholarly journals LMI-Based Model Predictive Control for Underactuated Surface Vessels with Input Constraints

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Lutao Liu ◽  
Zhilin Liu ◽  
Jun Zhang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Linear Matrix ◽  
Control Input ◽  
Input Constraints ◽  
State Dependent ◽  
Operating Region ◽  
Surface Vessels ◽  
Underactuated Surface Vessels ◽  
The Given

A nonlinear model predictive control (MPC) is proposed for underactuated surface vessel (USV) with constrained inputs. Aimed at the special structure of USV, a state-dependent coefficient (SDC) under the given USV is constructed in terms of diffeomorphism and state-dependent Riccati equation (SDRE) theory. Based on linear matrix inequalities (LMIs), the states of the USV are steered into an operating region around zero. When the states reach the region, the control law is switched to stabilize the system. And the constrained control input of the considered system is solved by convex optimization based on MPC involving LMIs. The simulation results verified the effectiveness of the proposed method. It is shown that, based on LMIs, it is easy to get the MPC for the USV with input constraints.

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