Research Article | Open Access
Atefeh Marvi Moghadam, Saeed Balochian, "Design of Combined Sliding Mode Controller Back Stepping Using Genetic Algorithm", Journal of Engineering, vol. 2013, Article ID 614803, 6 pages, 2013. https://doi.org/10.1155/2013/614803
Design of Combined Sliding Mode Controller Back Stepping Using Genetic Algorithm
This research has tried to achieve a new robust controller with back stepping control and sliding mode control method. Also as we know, in all analytical controllers there are constant coefficients like the back stepping and sliding mode controllers, redesigning the Lyapunov and the feedback linearization, - and so forth. There are two major problems in their set: firstly, the adjustment is cumbersome and time-consuming. Secondly, assuming that these parameters can be adjusted to workability, a designer can never tell exactly what are the parameters chosen to be optimal. To resolve this problem, the numerical algorithm which is a genetic algorithm is used here and we have the optimal parameters of the proposed controller. That genetic algorithm (GA) has been used to solve difficult engineering problems that are complex and difficult to solve by conventional optimization methods, and at the end of this section, we apply a new robust controller on ball and beam system. Simulation results are expressed.
The ball and beam system is one of the most popular and important bench systems for studying control systems. Many classical and modern control methods have been used to stabilize the ball and beam system [1, 2]. However, some of the dynamic properties were neglected in most research work regarding the ball and beam mechanism in order to simplify the dynamic equation of the system [1–7]. Yu  and Oh et al.  modeled the ball and beam system and neglected the coupling effect of the dynamic equations for two DOFs. They controlled the system considering only one DOF to define the motion of the ball on the beam and suggested two separate control algorithms for motor and ball positions. The angular velocity of the beam during the slow motion of the ball has a small value. Therefore, this parameter was neglected in modeling the ball and beam system in [3, 4, 6]. Also, due to the nonlinearity and complexity of the governing dynamics, some researchers used non-model-based control strategies such as neural network , fuzzy logic , and PID  to control the ball position and beam angle. In  is designed a tracking control strategy for the ball and beam system using a pair of decoupled fuzzy sliding mode controllers (DFSMCs). Decoupling adaptive fuzzy sliding mode control with rule reduction for nonlinear system is presented in . Also, in , modeling and control of ball and beam system using model-based and non-model-based control approaches is presented. Pareto design of decoupled sliding mode controllers for nonlinear systems (ball and beam) based on a multiobjective genetic algorithm is designed in . Also, motivated by the work done in [11, 12], one of the characteristics of sliding mode control is the consistency of the uncertainty [13, 14].
We propose a dynamic sliding mode controllers for the ball on a beam system using the complete model of the system. In this study, combiningback stepping and sliding mode methods is tried; in both methods there are several fixed parameters which are determined by the designer. Setting the number of state variables increases, especially when they are too numerous, is difficult. It is also possible if the parameters are properly chosen, but it cannot be said that the optimal parameters are selected. To address these problems, we have used an optimization algorithm called genetic algorithm to optimize the parameters. This paper is organized as follows: in Section 2 control issues including back stepping and sliding mode are presented. In Section 3, a proposed controller approach presented. Finally, Section 4 is presents simulation result and performance of the proposed optimal combined sliding mode and back stepping control of the ball and beam.
2. Control Issues
There are many cases, such as the control of helicopters, aircraft performance, and advanced industrial robots, in control that need feedback control. Various issues such as sustainable building, detection, and removal/disturbance attenuation (several combinations of them) can produce different problems in control. In addition, in control issues, other targets for the design considerations are considered that, for example, we can meet the special demands in response to specific constraints on the input supply passing mention.
2.1. Back Stepping Method
Back stepping is a recursive procedure that selection of Lyapunov function and design of feedback control come together. So back stepping method to using the lower-order with higher flexibility, most of the problems of stabilization, tracking, and robust control with constraints are solved with lower limitations. In this method, the system equations can be divided into two or more categories, then those equations that are not included in the control input, the design begins, and the set of these equations become stable. Then, the following equations that include inputs are stable.
Consider the following single-input system: that is defined on the domain of including the origin of . Assuming that is zero, other functions to return all values are cleared. Also, you can assume that , and are obvious, and are also uncertainty sentences; and assumeing and to be zero at the origin, the uncertainties for all are stated in the following inequalities: Inequality (3) handles the uncertainty limits because the upper bound should only be dependent on . Now with the start of the system (1), we followed the stabilizer state feedback control law with the form, the condition , and Lyapunov function (smooth and positive definite); so we have for all for every positive constant Asymptotically stable means that the equilibrium is stable. The derivative is zero point; so the definite derivative is negative.
Inequality (5) shows that and the system has an asymptotically stable equilibrium point: Now suppose on the following inequality applies: We consider the Lyapunov candidate function as follows: In this case, the derivative along the trajectory of (1) and (2) is as follows: Select and using (5) is thus where “” is a nonnegative value. Select We have where is positive value; thus the origin is asymptotically stable. For more details, see .
2.2. Sliding Mode Control
In this controller, there is also a disturbance-rejection controller like back stepping and the noise. According to this method, first, a sliding surface is defined and the sliding surface goes through the origin . The position of a point in space will start to move towards the sliding surface (reaching phase). Then, these modes slide on the port side of the slide (sliding phase). If not, move toward the source that is a good point to move on the sliding surface. Consider the following system: where is disturbance; and as we can see it is a function of the states of the system: where is the maximum disturbance and is a positive number. Suppose that the switch is selected as follows: for the changes in the switching level reaching zero, , we must have Now for the controller to be robust against disturbances, a batch component is added to it and a new controller can be obtained as follows: To prove the stability of Lyapunov function, the following is considered: where is a positive number and is time. So the above conditions are a Lyapunov function. For the above system to be stable, related derivates Lyapunov function should be negative. So according to the properties of derivative, , and (16),
3. Suggested Controller
In this study, combiningback stepping and sliding mode methods is tried. As the reader has seen, in both methods there are several fixed parameters which are determined by the designer. Setting the number of state variables increases, especially when they are too numerous, is difficult. It is also possible if the parameters are properly chosen, but it cannot be said that the optimal parameters are selected. To address these problems, we have used an optimization algorithm called genetic algorithm to optimize the parameters. The cost function used in a genetic algorithm is as follows: The equations that include inputs are stable. The switching levels are defined as follows: that are defined on the territory of including the origin of . Assuming that is zero, other functions to return all values are cleared. Also, you can assume , that are obvious and and are also uncertainty sentences; and assuming and to be zero at the origin, statements of uncertainty for all values also apply to the following inequalities: Now with the start of the system (22), we followed the stabilizer state feedback control law with the form, the condition , and Lyapunov function , So we have for all for every positive constant Inequality (5) shows that and the system has an asymptotically stable equilibrium point: Now suppose on the relation (7). The switching levels are defined as follows: To prove the stability of Lyapunov function, the following is considered: In this case, the derivative along the trajectory of (22) and (23) is as follows ; select and using (25) is, thus, Select We have where is positive value. So the system is always stable. For proving that switching reaches to zero level, it is sufficient just to consider the Lyapunov function as follows: with the assumption, and following the previous process, we arrive at the conclusion that the derivative of this function will always be negative. This ensures that the switch goes to zero.
4. Simulation and Results
The ball and beam system is one of the most enduringly popular and important laboratory models for teaching control system engineering. The ball and beam system is widely used because it is very simple to be understood as a system, and yet the control techniques that can be studied cover many important classical and modern design methods. The ball and beam system is shown in Figure 2. The state equations are as follows : where is the angle of the shaft axis, angular velocity about the axis of the rod, position the ball toward the center, ball speed, and are as well.
4.1. Genetic Algorithm
Genetic algorithms are algorithms that have great power in finding the optimal answer. In this algorithm, first of all, we create some random populations. Every individual (gene) in GA is considered in the form of binary strings; then, fitness for every individual is chosen with regard to its fitness. For creating the next generation, three stages is the selection phase, which consists of different phases, including ranking, proportional and… The second phase is the combination phase. In this phase, the two parents are combined with pc possibility and the next generation comes into being. By considering that during the past phases of gene, noise may be, in fact, this phase is a random noise which causes a small pc possibility for every bit. Genetic algorithms can be obtained through a series of solutions to the problem. Better answers do imply the law of conservation of the best situations. Figure 1 shows flowchart of GA.
The cost function used in a genetic algorithm is as follows: Selected parameters for genetic algorithms, the coefficients in the switching, and some other factors are as follows.
Sliding surface for ball and beam system is like the below form: The coefficients of sliding surface and controller parameter in (29) are optimized using genetic algorithm. Genetic parameters to get the best selected are as follows: Simulation results of the control input and and state variables are shown in Figures 3, 4, and 5.
This research has tried to achieve a new robust controller with back stepping control and sliding mode control method. In this study, combiningback stepping and sliding mode methods is tried. As you have seen, in both methods there are several fixed parameters which are determined by the designer. Settting the number of state variables increases, especially when they are too numerous, is difficult. It is also possible if the parameters are properly chosen, but it cannot be said that the optimal parameters are selected. To address these problems, we have used an optimization algorithm called genetic algorithm to optimize the parameters.
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Copyright © 2013 Atefeh Marvi Moghadam and Saeed Balochian. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.