## 1. Introduction

The fractional calculus is a branch of mathematics that attracted attention since G.W. Leibnitz proposed it in the seventeenth century. However, the researchers were not attracted to this area because of the lack of applications and analytical results of the fractional calculus.

On the contrary, the fractional calculus currently attracts the attention of a large number of scientists for their applications in different fields of science, engineering, chemistry, and so on.

This chapter presents the design of a fractional order nonlinear identifier modeled by a dynamic neural network of fractional order.

Although PID controllers are introduced long time ago, they are widely used in industry because of their advantages such as low price, design simplicity, and suitable performance. While three parameters of design including proportional (Kp), integral (Ki), and derivative (Kd) are available in PID controllers, two more parameters exist in FOPID controllers for adjustment. These parameters are integral fractional order and derivative fractional order. In comparison with PID controllers, FOPID controllers have more flexible design that results in more precise adjustment of closed‐loop system. FOPID controllers are defined by FO differential equations. It is possible to tune frequency response of the control system by expanding integral and derivative terms of the PID controller to fractional order case. This characteristic feature results in a more robust design of control system, but it is not easily possible. According to nonlinearity, uncertainty, and confusion behaviors of robot arms, they are highly recommended for experimenting designs of control systems. Despite nonlinear behavior of robot arm, it is demonstrable that a linear proportional derivative controller can stabilize the system using Lyapunov. But, classic PD controller itself cannot control robot to reach suitable condition. Several papers and wide researches in optimizing performance of the robot manipulator show the importance of this issue.

There are several ways of defining the derivative and fractional integral, for example, the derivative of Grunwald‐Letnikov given by Eq. (1)

where [.] is a flooring operator, while the RL definition is given by:

For

Similarly, the notation used in ordinary differential equations, we will use the following notation, Eq. (3), when we are referring to the fractional order differential equations where

which is:

The Caputo’s definition can be written as

For

Trajectory tracking, synchronization, and control of linear and nonlinear systems are a very important problem in science and control engineering. In this chapter, we will extend these concepts to force the nonlinear system (plant) to follow any linear and nonlinear reference signals generated by fractional order differential equations.

The proposed adaptive control scheme is composed of a recurrent neural identifier and a controller (**Figure 1**).

We use the above scheme to model the unknown nonlinear system by means of a dynamic recurrent neural network of adaptable weights; the above is modeled by differential equations of fractional order. Also, the scheme allows us to determine the control actions, the error of approach of trajectories, as well as the laws of adaptation of adaptive weights and the interconnection of such systems.

## 2. Modelling of the plant

The nonlinear system (Eq. (5)) is forced to follow a reference signal:

The differential equation will be modeled by:

The tracking error between these two systems:

We use the next hypotheses.

In this research, we will use

The nonlinear system is [1]:

Where the

## 3. Tracking error problem

In this part, we will analyze the trajectory tracking problem generated by

Are the state space vector, input vector, and

To achieve our goal of trajectory tracking, we propose

The time derivative of the error is:

The Eq. (11) can be rewritten as follows, adding and subtracting the next terms

(12) |

The unknown plant will follow the fractional order reference signal, if:

Now,

If

And by replacing Eq. (16) in Eq. (15), we have:

And:

So, the result for

and Eq. (17) is simplified:

Taking into account that

If

Now, the problem is to find the control law

## 4. Asymptotic stability of the approximation error

From Eq. (20), we consider the stability of the tracking error, for which we first observe that
_{,}

For such stability analysis of the trajectory tracking (Eq. (20)), we propose the following FOPID control law [2]:

Our objective is to find

A FOPID controller, also known as a

where

We will show that the feedback system is asymptotically stable. Replacing Eq. (21) in Eq. (20), we have

And if

We will show that the new state

Let

The fractional order time derivative of Eq. (25) along with the trajectories of Eq. (24) is

In this part, we select the next learning law from the neural network weights as in [7] and [8]:

Then, Eq. (27) is reduced to

Next, lets consider the following inequality proved in [9]

which holds for all matrices

we get

Here, we select

Of the previous inequality, Eq. (32), we need that the fractional order Lyapunov derivative,

To achieve this purpose, we select:

With the above Eq. (32), the control law that guarantees asymptotic stability of the tracking error is given by Eq. (33)

(33) |

**Theorem:** The control laws (Eq. (33)) and the adaptive weights (Eq. (28)) ensure that the trajectory tracking error between the fractional nonlinear system (Eq. (8)) and the fractional reference signal (Eq. (9)) satisfies

**Remark 2:** *From Eq. (32), we have*

## 5. Simulation

The manipulator used for simulation is a two revolute joined robot (planar elbow manipulator), as shown in **Figure 2**.

The dynamics of the robot is established by [10, 11],

And the torque vector

Thus, it is possible to write the equations of motion using the Lagrange equations for fractional manipulator system as [12]:

The terms containing

With the end of supporting the effectiveness of the proposed controller, we have used a Duffing equation.

The fractional order neural network is modelling by the differential equation:

We try to force this manipulator to track a reference signal [14] given by undamped Duffing equation:

To get the fractional order Duffing’s system, this equation can be rewritten as a system of the first‐order autonomous differential equations in the form [15]:

Here, the conventional derivatives are replaced by the fractional derivatives as follows:

where

Illustrated, the response in the time, angular position and torque applied to the fractional nonlinear system are shown in **Figures 3**–**7**. As can be observed, the trajectory tracking objective is obtained

Its phase space trajectory is given in **Figure 8**, and the time evolution for the position angles and applied torque are shown in **Figures 9**–**12**. As can be seen in **Figures 9** and **10**, the trajectory tracking is successfully obtained where plant and reference signals are the same.

Its phase space trajectory is given in **Figure 13**, and the time evolution for the position angles and applied torque are shown in **Figures 14**–**17**. As can be seen in **Figures 14** and **15**, the trajectory tracking is successfully obtained where plant and reference signals are the same.

As can be observed, in the graphs of the trajectory tracking, the experimental results obtained in this chapter show a good experimental performance. The laws of control are obtained online, as well as the laws of adaptive weights in the fractional order neural network.

The control laws obtained are robust to modeling errors and nonmodeled dynamics (unknown nonlinear systems).

## 6. Conclusions

We have discussed the application of the stability analysis by Lyapunov of fractional order to follow trajectories of nonlinear systems whose mathematical model is unknown. The convergence of the tracking error is established by means of a Lyapunov function, as well as a control law based on Lyapunov and laws of adaptive weights of fractional order dynamical neural networks.

The results show a satisfactory performance of the fractional order dynamical neural network with online learning.