Hybrid Controller

Original Text: Hybrid Controller


Abstract— In this paper, the application of Particle Swarm Optimization (PSO) on the optimization of the membership functions and the sliding surface positives constants λh, λv of the decentralized fuzzy sliding mode controller (FSMC) is presented. This technique is used in order to find an optimal decentralized intelligent controller for a nonlinear aerodynamic a Twin Rotor Multi-input- multi-output System (TRMS). Changes have been made on the original PSO algorithm to make it adaptable to this problem. Simulation results show the effectiveness of this optimization method, the controller designed demonstrate a very good accuracy, a very good response time compared to a PID controller designed with the same method.

I. INTRODUCTION
Fuzzy logic controller (FLC) is a practical alternative for a variety of challenging control applications, it is a popular technique that has seen increasing interest in the past decades, since it has a linguistic based structure and its performance are quite robust for non-linear systems. However, stability analysis for general FLC systems is relatively difficult. To overcome with this problem, a combination of FLC and the well-known Sliding Mode Control (SMC) has been proposed in recent years, called FSMC 1,2.The stability of FSMC can be proved using Lyapunov theory 3.
This technique has been extensively used in many control applications 4,5,6. The other advantage of the FSMC is that it has fewer rules than FLC. Furthermore, by use the SMC technique, the system possesses becomes more robustness against parameter variations and external disturbances.
Among the complexities existing in the developing of the FLC, the determination of the best parameters of the membership functions. A solution for this problem is the application of bio-inspired algorithms. Optimization algorithms can be a useful tool due to their capabilities of solving nonlinear problems, well-constrained or even NP-hard problems. Among the most used optimization methods we can find: genetic algorithms (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization, etc.
In this paper, PSO algorithm was used to tune the Mamdani type-fuzzy controller’s membership functions parameters, the sliding surface positives constants λh, λv are also optimized. This paper is organized as follows: Description of the TRMS is illustrated in Section 2. A FSMC is presented in Section 3. The PSO-tuning method for the FSMC is described in Section 4. The simulations results and conclusion are given in Sections 5 and 6, respectively.

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Revised Text:

Abstract

In this paper, the application of Particle Swarm Optimization (PSO) on the optimization of the membership functions, and the sliding surface positives constants λh, λv of the decentralized fuzzy sliding mode controller (FSMC) is presented. This technique is used in order to find an optimal, decentralized, intelligent controller for a nonlinear aerodynamic, Twin Rotor Multi-input, multi-output System (TRMS). Changes have been made in the original PSO algorithm to make it adaptable to this problem. Simulation results show the effectiveness of this optimization method. The controller design demonstrates a very good accuracy and a very good response time compared to a PID controller designed using the same method.

I. INTRODUCTION

The Fuzzy Logic Controller (FLC) is a practical alternative for a variety of challenging control applications, it is a popular technique that has seen increasing interest in the past decades, since it has a linguistic based structure and its performance is quite robust for non-linear systems. However, stability analysis for general FLC systems is relatively difficult. To overcome this problem, a combination of FLC and the well-known Sliding Mode Control (SMC) has been proposed in recent years, called the FSMC 1,2. The stability of the FSMC can be proven using Lyapunov theory 3.

This technique has been extensively used in many control applications 4,5,6. The other advantage of the FSMC is that it has fewer rules than the FLC. Furthermore, by using the SMC technique, the system possesses more robustness against parameter variations and external disturbances.

Among the complexities existing in the developing of the FLC, is the determination of the best parameters for the membership functions. A solution for this problem is the application of bio-inspired algorithms. Optimization algorithms can be useful tools due to their capabilities of solving nonlinear problems, well-constrained, or even NP-hard problems.

Among the most used optimization methods we have found are: genetic algorithms (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization, etc.

In this paper, a PSO algorithm was used to tune the Mamdani type fuzzy controller’s membership function parameters. The sliding surface positives constants λh, λv were also optimized.

This paper is organized as follows: Description of the TRMS is illustrated in Section 2. A FSMC is presented in Section 3. The PSO-tuning method for the FSMC is described in Section 4. The simulations results and conclusion are given in Sections 5 and 6, respectively.


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Aug 16, 2011
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predictive control
by: Anonymous

This paper proposes a new reformulation of the problem of robust quadratic programm (for)of a nonlinear predictive control (RQPNPC) via Linear matrix inequalities (LMIs), using of first principales-based nonlinear models the application to nonlinear system.

in this work, a new approach is introduced to solve some problems in quadratic programming algorithm based on LMIs, this approach allows to introduce stability conditions on the loss function optimized. Since the method of nonlinear Sector is used to find an accurate approximation of the nonlinear system,

this approximation and incorporation of the description of plant uncertainty in the problem formulation are used to calculate the objective function.

On the other hand, the stability objective is guaranteed by the use of terminal equality constraints, in particular the Constrained Receding Horizon Predictive Control (CRHPC) algorithm. To show the effectiveness of this approach by way of strength, a series of simulation results of the algorithm RNCPC-LMI (Robust nonlinear constrained predictive control based Linear matrix inequalities design) are applied on a two constrained nonlinear systems.

Several alternatives have been developed to overcome this problem including the active set [6,7], interior point methods [8-12], although this method is becoming increasingly popular, against part of the computational load per iteration is higher, which makes it unsuitable for small problem. Other aspects, such as the difficulty of constructing an initial feasible solution, can play an important role. Without going into details of each method used to solve the quadratic programming problem linear and nonlinear and it may inconvenience the reader see reference [11].

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