scholarly journals DIGITAL MODEL PREDICTIVE CONTROL OF THE THREE TANK SYSTEM BASED ON LAGUERRE FUNCTIONS

2019 ◽  
Vol 17 (3) ◽  
pp. 153
Author(s):  
Miodrag Spasić ◽  
Dragan Antić ◽  
Nikola Danković ◽  
Staniša Perić ◽  
Saša S. Nikolić
Keyword(s):  
Nonlinear System ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Discrete Time ◽  
Hydraulic System ◽  
Control Method ◽  
Controller Design ◽  
Laguerre Functions ◽  
Tank System ◽  
Digital Simulations

The application of the model predictive control (MPC) based on discrete-time Laguerre functions is presented in this paper. A nonlinear three-tank hydraulic system is used as an object to which the proposed algorithm is applied. The paper also presents the method of For the verification of the proposed control method, digital simulations are performed using Matlab.linearization of the nonlinear system, as well as the procedure for the controller design.

Nonlinear distributed model predictive control for complex systems: application for hydraulic system

2018 ◽  
Vol 41 (10) ◽  
pp. 2751-2763 ◽  
Author(s):  
Nadia Hajji ◽  
Saber Maraoui ◽  
Larbi Chrifi-Alaoui ◽  
Kais Bouzrara
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Hydraulic System ◽  
Water Levels ◽  
Distributed Model ◽  
Dual Decomposition ◽  
Decomposition Approach ◽  
Distributed Model Predictive Control ◽  
Nonlinear Predictive Control ◽  
Tank System

In this paper, a nonlinear distributed model predictive control based on dual decomposition approach is proposed for complex system. The global system can be decomposed into several subsystems and each one will be managed by its own controller. To design the nonlinear predictive control in a distributed fashion, an analytical solution is proposed. The latter is based on the approximation of the error using its expansion of Taylor series. The proposed approach is implemented on the three tank system to control the water levels. Simulation results demonstrate the effectiveness of the proposed approach.

A tube-based robust MPC for duty-cycled rotation needle steering systems with bounded disturbances

2021 ◽  
pp. 014233122110430
Author(s):  
Zhi Qi ◽  
Qianyue Luo ◽  
Hui Zhang
Keyword(s):  
Model Predictive Control ◽  
Linear Model ◽  
Predictive Control ◽  
Control Method ◽  
Controller Design ◽  
Control Approach ◽  
Bounded State ◽  
Non Linear ◽  
System States ◽  
Trajectory Tracking Controller

In this paper, we aim to design the trajectory tracking controller for variable curvature duty-cycled rotation flexible needles with a tube-based model predictive control approach. A non-linear model is adopted according to the kinematic characteristics of the flexible needle and a bicycle method. The modeling error is assumed to be an unknown but bounded disturbance. The non-linear model is transformed to a discrete time form for the benefit of predictive controller design. From the application perspective, the flexible needle system states and control inputs are bounded within a robust invariant set when subject to disturbance. Then, the tube-based model predictive control is designed for the system with bounded state vector and inputs. Finally, the simulation experiments are carried out with tube-based model predictive control and proportional integral derivative controller based on the particle swarm optimisation method. The simulation results show that the tube-based model predictive control method is more robust and it leads to much smaller tracking errors in different scenarios.

Stabilization of uncertain nonlinear discrete-time switched systems with state delays: A constrained robust model predictive control approach

2019 ◽  
Vol 25 (14) ◽  
pp. 2079-2090 ◽  
Author(s):  
Maryam Aminsafaee ◽  
Mohammad Hossein Shafiei
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Discrete Time ◽  
Switched Systems ◽  
Control Method ◽  
Sufficient Conditions ◽  
Switched System ◽  
Linear Matrix ◽  
Robust Model Predictive Control ◽  
Robust Model

This paper studies the problem of robust stabilization for a class of nonlinear discrete-time switched systems with polytopic uncertainties and unknown state delay. Moreover, the control signal is assumed to be constrained. The objective of the proposed controller is to stabilize the switched system under arbitrary switching signals based on the switched Lyapunov function approach. Therefore, based on the constrained robust model predictive control method and an appropriate Lyapunov–Krasovskii functional, the sufficient conditions to guarantee the asymptotical stability of the switched system are developed as linear matrix inequalities. Through online solving an optimization problem, the predictive state-feedback controller is designed. Furthermore, in this delay-dependent approach, only the upper bound of time-delay should be known. Appropriate transient response, ability to handle constraints, and nonconstrained switching signal are the other advantages of the proposed method. Finally, the performance of the proposed approach is compared with a similar approach through a numerical example. As well as, to show the applicability of the proposed controller, it is applied to a drinking water supply network, as an application example.

Stochastic model predictive control for driver assistance control of intelligent vehicles considering uncertain driving environment

2022 ◽  
pp. 107754632110523
Author(s):  
Yimin Chen ◽  
Yunxuan Song ◽  
Liru Shi ◽  
Jian Gao
Keyword(s):  
Model Predictive Control ◽  
Stochastic Model ◽  
Predictive Control ◽  
Control Method ◽  
Controller Design ◽  
Intelligent Vehicles ◽  
Driving Safety ◽  
Driver Assistance ◽  
Control Approach ◽  
Stochastic Model Predictive Control

Advanced driver assistance control faces great challenges in cooperating with the nearby vehicles. The assistance controller of an intelligent vehicle has to provide control efforts properly to prevent possible collisions without interfering with the drivers. This paper proposes a novel driver assistance control method for intelligent ground vehicles to cooperate with the nearby vehicles, using the stochastic model predictive control algorithm. The assistance controller is designed to correct the drivers’ steering maneuvers when there is a risk of possible collisions, so that the drivers are not interfered. To enhance the cooperation between the vehicles, the nearby vehicle motion is predicted and included in the assistance controller design. The position uncertainties of the nearby vehicle are considered by the stochastic model predictive control approach via chance constraints. Simulation studies are conducted to validate the proposed control method. The results show that the assistance controller can help the drivers avoid possible collisions with the nearby vehicles and the driving safety can be guaranteed.

scholarly journals A Robust Fault-Tolerant Predictive Control for Discrete-Time Linear Systems Subject to Sensor and Actuator Faults

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2307
Author(s):  
Sofiane Bououden ◽  
Ilyes Boulkaibet ◽  
Mohammed Chadli ◽  
Abdelaziz Abboudi
Keyword(s):  
Model Predictive Control ◽  
Linear Systems ◽  
Predictive Control ◽  
Robust Stability ◽  
Discrete Time ◽  
Fault Tolerant ◽  
Linear Matrix ◽  
Input Constraints ◽  
Actuator Faults ◽  
Time Linear

In this paper, a robust fault-tolerant model predictive control (RFTPC) approach is proposed for discrete-time linear systems subject to sensor and actuator faults, disturbances, and input constraints. In this approach, a virtual observer is first considered to improve the observation accuracy as well as reduce fault effects on the system. Then, a real observer is established based on the proposed virtual observer, since the performance of virtual observers is limited due to the presence of unmeasurable information in the system. Based on the estimated information obtained by the observers, a robust fault-tolerant model predictive control is synthesized and used to control discrete-time systems subject to sensor and actuator faults, disturbances, and input constraints. Additionally, an optimized cost function is employed in the RFTPC design to guarantee robust stability as well as the rejection of bounded disturbances for the discrete-time system with sensor and actuator faults. Furthermore, a linear matrix inequality (LMI) approach is used to propose sufficient stability conditions that ensure and guarantee the robust stability of the whole closed-loop system composed of the states and the estimation error of the system dynamics. As a result, the entire control problem is formulated as an LMI problem, and the gains of both observer and robust fault-tolerant model predictive controller are obtained by solving the linear matrix inequalities (LMIs). Finally, the efficiency of the proposed RFTPC controller is tested by simulating a numerical example where the simulation results demonstrate the applicability of the proposed method in dealing with linear systems subject to faults in both actuators and sensors.

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