scholarly journals DISCRETE TIME MODEL PREDICTIVE CONTROL APPROACH FOR INVERTED PENDULUM SYSTEM WITH INPUT CONSTRAINTS

2015 ◽  
pp. 186-191
Author(s):  
HARSHITA JOSHI ◽  
NIMMY PAULOSE
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Discrete Time ◽  
Process Model ◽  
Inverted Pendulum ◽  
Time Model ◽  
Pendulum System ◽  
Control Approach ◽  
Inverted Pendulum System ◽  
Literature Selection

Model predictive control (MPC) includes a receding-horizon control techniques based on the process model for predictions of the plant output. Since late 1970’s several MPC approaches have been reported in the literature. Selection of the most appropriate MPC approach depend on the specific problem. In this paper, discrete time MPC is applied to a inverted pendulum system coupled to a cart. The objective of the MPC-controller is to drive the system towards pre-calculated trajectories that move the system from one reference point to another.Quadratic programming is used for optimization of objective function (with and without constraints).

Discrete-time backstepping control with nonlinear adaptive disturbance attenuation for the inverted-pendulum system

2019 ◽  
pp. 014233121986777
Author(s):  
Fatih Adıgüzel ◽  
Yaprak Yalçın
Keyword(s):  
Discrete Time ◽  
Feedback Linearization ◽  
Inverted Pendulum ◽  
Backstepping Control ◽  
Disturbance Attenuation ◽  
Pendulum System ◽  
Inverted Pendulum System ◽  
Partial Feedback Linearization ◽  
Partial Feedback ◽  
Backstepping Controller

A discrete-time backstepping controller with an active disturbance attenuation property for the Inverted-Pendulum system is constructed in this paper. The main purpose of this study is to show that Immersion and Invariance (I & I) approach can be used to design a nonlinear observer for disturbance estimation and demonstrate its effectiveness considering a nonlinear system with an unstable equilibrium point, namely Inverted-Pendulum system, by utilizing the estimated values in backstepping control design. All designs are directly performed in discrete-time domain to obtain directly implementable observer and controller in discrete processors with superior performance compared to emulators. The Inverted-Pendulum system is not in strict feedback form therefore backstepping procedure cannot be directly applied. In order to enable backstepping construction, firstly a partial feedback linearization is performed and afterwards a novel discrete-time coordinate transformation is proposed. Prior to the construction of partial feedback linearizing and backstepping controller, a nonlinear disturbance estimator design is proposed with Immersion and Invariance approach. The estimated disturbance values used in the partial feedback linearization and construction of the backstepping controller. The global asymptotic stability of the estimator and local asymptotic stability of overall closed loop system are proved in the sense of Lyapunov. Performance of proposed direct discrete-time backstepping control with discrete I & I observer is compared with a backstepping sliding mode controller with another nonlinear disturbance observer (NDO) by simulations.

scholarly journals T-S Fuzzy System Controller for Stabilizing the Double Inverted Pendulum

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Boutaina Elkinany ◽  
Mohammed Alfidi ◽  
Redouane Chaibi ◽  
Zakaria Chalh
Keyword(s):  
State Feedback ◽  
Inverted Pendulum ◽  
Fuzzy Controller ◽  
Pendulum System ◽  
Great Ability ◽  
Control Approach ◽  
Feedback Model ◽  
Inverted Pendulum System ◽  
System Controller ◽  
Fuzzy State

This article provides a representation of the double inverted pendulum system that is shaped and regulated in response to torque application at the top rather than the bottom of the pendulum, given that most researchers have controlled the double inverted pendulum based on the lower part or the base. To achieve this objective, we designed a dynamic Lagrangian conceptualization of the double inverted pendulum and a state feedback representation based on the simple convex polytypic transformation. Finally, we used the fuzzy state feedback approach to linearize the mathematical nonlinear model and to develop a fuzzy controller H ∞ , given its great ability to simplify nonlinear systems in order to reduce the error rate and to increase precision. In our virtual conceptualization of the inverted pendulum, we used MATLAB software to simulate the movement of the system before applying a command on the upper part of the system to check its stability. Concerning the nonlinearities of the system, we have found a state feedback fuzzy control approach. Overall, the simulation results have shown that the fuzzy state feedback model is very efficient and flexible as it can be modified in different positions.

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