Reinforcement Learning and Adaptive Optimisation of Complex Dynamic Systems and Industrial Applications
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Zhu Dachang, Du Baolin, Zhu Puchen, Wenqiang Wu, "Adaptive Backstepping Sliding Mode Control of Trajectory Tracking for Robotic Manipulators", Complexity, vol. 2020, Article ID 3156787, 11 pages, 2020. https://doi.org/10.1155/2020/3156787
Adaptive Backstepping Sliding Mode Control of Trajectory Tracking for Robotic Manipulators
Abstract
To achieve precise trajectory tracking of robotic manipulators in complex environment, the precise dynamic model, parameters identification, nonlinear characteristics, and disturbances are the factors that should be solved. Although parameters identification and adaptive estimate method were proposed for robotic control in many literature studies, the essential factors, such as coupling and friction, are rarely mentioned as it is difficult to build the precise dynamic model of the robotic manipulator. An adaptive backstepping sliding mode control is proposed to solve the precise trajectory tracking under external disturbances with complex environment, and the dynamic response characteristics of a twolink robotic manipulator are described in this paper. First, the Lagrange kinetic method is used to derive the precise dynamic model which includes the nonlinear factor with friction and coupling. Moreover, the dynamic model of twolink robotic manipulator is built. Second, the estimate function for the nonlinear part is selected, and backstepping algorithm is used for analyzing the stabilities of the sliding mode controller by using Lyapunov theory. Furthermore, the convergence of the proposed controller is verified subject to the external disturbance. At last, numerical simulation results are reported to demonstrate the effectiveness of the proposed method.
1. Introduction
Nowadays, with the development of modern industrial technology, robotic manipulators are widely used in automobile manufacturing, aerospace, electronic assembly, precision medical operation, and other fields. To obtain steady state accuracy and fast dynamical response, it is necessary for high precision of the trajectory tracking ability of robotic manipulator. Unfortunately, it is difficult to be satisfied on account of the nonlinear characteristics in complex environment like clearance of joints, friction, external disturbance, strong couple, and so on. The first stage of trajectory tracking is to establish the precise mathematical model of the robotic manipulator. However, the nonlinear part of the dynamic model of the robotic manipulator is ignored in many literatures [1–5] or parameter identification by many approaches [6–8]; even the torque in the joint space and the moment of inertia were ignored in [9]. By calculating kinetic energy, potential energy, and generalized force, the Lagrange equation was utilized to build the dynamic equation for robotic manipulator [10, 11]. As the recurrence relationship is not established between multiple links, it cannot be applied easily to the whole dynamic modeling for robotic manipulators. To overcome inaccuracy of the dynamical model of robotic manipulator, friction, clearance, and external disturbance were considered, and intelligent control strategies have been developed by many researchers for the uncertain manipulator [12–14], for example. In this paper, the dynamic equation with recurrence method by using Lagrange energy method is provided as an accurate mathematical model for precision trajectory tracking.
Furthermore, the design of intelligence controllers for nonlinear systems affected by disturbances is a topic that has been studied by several authors, and many different approaches have been proposed for this problem [15–17]. Backstepping is a systematic and recursive design method for nonlinear control applied to the feedback linearization system, which can guarantee global regulation and tracking performance [18, 19]. It is a regression design method which combines the selection of Lyapunov function with the design of controller, and the virtual control is designed with the requirements step by step, and then, control law of complex uncertain system is finally designed. The basic design idea is to decompose the complex nonlinear system into subsystems of no more than systematic order and then design the Lyapunov function of each subsystem separately. On the basis of ensuring that the subsystem has guaranteed convergence, the control law of the subsystem is obtained. In the design of the next subsystem, the control law of the preceding subsystem is taken as the tracking target of this subsystem, and finally the control law of the last subsystem is obtained. By analogy, the control law of the entire closedloop system can be obtained, and the convergence of the closedloop system is guaranteed by Lyapunov stability analysis method. Backstepping with intelligent algorithm can increase the quality of the transition process, reduce or even eliminate the uncertainty of the matching constraints, and provide a structured and systematic design method for the Lyapunov function design for complex nonlinear systems [20–23]. Sliding mode control (SMC) is another robust control method, which produces a switching control law (equivalent control law) to force the system to converge to the sliding surface within a boundary layer near the sliding surface under the convergence of the Lyapunov stability theory [24, 25]. Trajectory tracking control of a 6DOF pneumatically actuated GoughStewart parallel robot was investigated by Lafmejani [26], and position control of the pneumatic actuator was performed based on backstepping sliding mode controller according to the dynamic model of system. A methodology of dynamic analysis and control for a hybrid humanoid robot arm was presented in [27], and an adaptive backstepping sliding mode controller was developed for the parameters uncertainties and disturbances of the hybrid humanoid robot arm.
For the multiinputmultioutput (MIMO) nonlinear system with uncertainties and disturbances, sliding mode PI control with backstepping approach [28], presence of bounded uncertainties from unmodeled dynamic, parameters variations, disturbances, and visual fusion technology [29, 30], and asynchronous control with fuzzy approach [31] were proposed. To make the dynamic model of the robotic manipulator more accurate, an impedancecontrol strategy with dynamic compensation for interactive control of robot manipulators was presented in [32]. The kinematics and dynamics of multiple cooperative welding robot manipulators were studied on the basis of the Denavit–Hartenberg and Lagrange method, and adaptive neural control and dynamic movement primitives were considered in [33]. An adaptive backstepping sliding mode control of robotic manipulators is proposed in this paper to achieve the precision trajectory tracking with external disturbances. The main contributions of the current paper are summarized as follows:(1)By using Lagrange energy function, the precise dynamic model of the robotic manipulator is built, and the nonlinear characteristics and uncertainties are analyzed. Furthermore, dynamic model of a twolink robotic manipulator is derived.(2)According to the precision dynamic model of the twolink robotic manipulator, an estimate function of the nonlinear and coupling parts is proposed. Backstepping algorithm is used to construct the equivalent control law of sliding mode control through three steps, and the stability of the proposed controller is convergent by using Lyapunov theory.
This paper is organized in the following manner: Section 2 presents the precise dynamic modeling method and the precise dynamic model of the robotic manipulator. According to the previous method, the precise dynamic model of a twolink robotic manipulator is derived. Section 3 describes the sliding mode control with backstepping algorithm. Through the estimate function of nonlinear and coupling parts, the controller is proposed for the trajectory tracking under external disturbances, and its stability is discussed. Numerical simulation results are reported to demonstrate the effectiveness of the proposed method in Section 4. Finally, conclusions are provided in Section 5.
2. Description of Robotic Manipulator and Dynamic Modeling
To make the dynamic model recursive, the Newton–Euler method is used to establish all force balance between the links of the robotic manipulator, and the dynamic equation can be derived. Forward recursion is used for speed and acceleration transfer between all of the links and backward recursion is used for force transfer from the endeffector to each link of the robotic manipulator.
2.1. Description of Link Parameters of Robotic Manipulator
The dynamic parameters which describe the dynamic model are important for the control algorithms, effective simulation results, and accurate trajectory tracking algorithms. Dynamic equation of the robotic manipulator withDOF has been characterized in many literature studies [1–11] as follows:where are the link position, velocity, and acceleration vectors, respectively, is the symmetric positive definite inertia matrix, is the Coriolis or centrifugal forces, consolidates the gravitational force, incorporates the friction terms, and represents external disturbance.
However, quadratic velocity terms and dynamic coupling terms are not taken into account. So, the problem of model accuracy cannot be solved essentially only through parameter identification and compensation methods. The kinematic description of the link is shown in Figure 1.
An infinitesimal element is selected which lies on the link, and the position of the center of mass is represented by vector , the position vector of is presented by vector , and the position vector from the center of mass to the infinitesimal element is . When integrating in the whole area of the link, the infinitesimal element can represent the motion performance of the whole link.
The kinetic energy component of the link can be given bywhere is the linear velocity vector of the infinitesimal element, is material density, and is the volume of the infinitesimal element.
The position vector relationship between and is satisfied withwhere is calculated by centroid theorem aswhere is the mass of the link.
Differentiating equation (3) with respect to time, one can getwhere is the linear velocity vector of the center of mass, is angular velocity of the infinitesimal element around the center of mass, and is the antisymmetric matrix of threedimensional vector and defined as follows:
Substituting equation (5) into equation (2), one can obtain
Equation (7) shows that the kinetic energy of each link consists of three parts, described as follows:(1)Translational kinetic energy is expressed as(2)Implicated motion kinetic energy is expressed as(3)Rotation kinetic energy, combining with equation (6), is expressed as where ; is defined as(1)where is symmetric matrix and denotes inertia tensor related to the center of mass of the link in base coordinate system.
Combining equation (8) with equation (10), the kinetic energy of the link can be given bywhere is joint variable vector.
The kinetic energy component of the motor of joint can be calculated in a similar way. Under the assumption of rigid transmission, there iswhere is the transmission ratio of gear speed reducer and is the rotor angular position.
With the law of angular velocity synthesis, the total angular velocity is derived as follows:where is the angular velocity of the link and is the unit vector of rotor axis.
The linear and angular velocities of the rotor center of mass can be expressed aswhere , , and the elements of matrix are given by
If the rotor turns around its center, then, .
So, the kinetic energy component of the rotor can be given bywhere is the mass of the rotor and is the inertia matrix of rotor.
Summing different components of a single link in (12) and a single motor in (17), the total kinetic energy of the robotic manipulator can be calculated and yields where is the inertia matrix, satisfied with
The potential energy of the link can be calculated as follows:where is acceleration vector of gravity in the base coordinate system.
So, the total potential energy of the robotic manipulator can be expressed aswhere is the position vector of rotor center of mass.
According to (18) and (21), the Lagrange function is constructed as follows:
The dynamic equation is derived by using Lagrange function, yieldingwhere
The dynamic equation of robotic manipulator with DOF can be derived aswhere .
Comparing (25) with (1), the elements of should be satisfied with
Considering the viscous friction and Coulomb friction, equation (25) can also be rewritten as follows:where is contact force of endeffector, is velocity Jacobi matrix, is the torque of viscous friction, and is the torque of Coulomb friction.
2.2. Parameters and Dynamic Model of TwoLink Robotic Manipulator
The dynamic mathematical model for a rigid planar robotic manipulator having two links and a contact surface with the external force acting on the surface is shown in Figure 2. According to the coordinate system , it consists of two links having link lengths and with their centers of mass and lying at the middle of links, respectively. The length of the center of mass is and , respectively.
The Lagrange method is used to build the precise dynamic model of a twolink robotic manipulator with their nominal values as listed in Table 1.

The total kinetic energy of the twolink robotic manipulator iswhere and are joint variables of the twolink robotic manipulator.
The total potential energy of the twolink robotic manipulator is
By using (23), the dynamic equation of twolink robotic manipulator can be derived aswhere , , and
Meanwhile, the torque of joint 2 can be also derived aswhere
Combining (30) with (32), the robotic plant can be rewritten with following mathematical model:
Equation (14) completely represents the relationship between actuated torque and displacement, velocity, and acceleration in the joint space. The issues with and represent the moment of inertia caused by the acceleration of joint 1 and joint 2, respectively. The issues with and represent the moment of inertia of the acceleration coupling between two joints. The issues with and represent the coupling moment term of the centripetal force caused by the velocity between two joints. The issues with and represent the coupling moment term of the Coriolis force between two joints. and represent the gravity moment term. Considering the effect of centroid inertia, (35) can be modified as
Furthermore, to consider the effect of the contact force acting on the endeffector, the right side of (34) can be replaced bywhere is the force and moment vector applied by the endeffector in the working environment and is the velocity Jacobian matrix, yielding
3. Adaptive Backstepping Sliding Mode Control Algorithm
In this section, an adaptive backstepping sliding mode controller is presented which achieves precision trajectory tracking property by guaranteeing the robustness and stability of the closedloop system of robotic manipulator. The uncertainties included in the system are required for compensating the external disturbances and nonlinear dynamics in terms described as (27). The adaptive backstepping sliding mode control is constructed at the final step.
The inverse dynamic equation can be expressed as
Property 1. is the symmetric inertial matrix and bounded:where and are positive constants.
Property 2. is skew symmetric matrix and satisfies
Property 3. , , and are bounded as follows:where , , and are positive constants.
Property 4. is unknown disturbance and bounded asDefining vectors , , , the desired track is defined as , so the state equation of the robotic manipulator can be given bywhere is defined as the disturbance nonlinear function.
Assuming that is the desired trajectory of the robotic manipulator in joint space, select as the trajectory tracking error; it is defined asDifferentiating (44) with respect to time, one can getBy using backstepping algorithm, let be a virtual input; then, the feedback control law can be expressed aswhere is a positive definite diagonal coefficient matrix according to the DOF of robotic manipulator.
Theorem 1. Consider a robotic manipulator with nDOF with the dynamic in (28), by designing the following sliding mode controller aswhere is a positive definite diagonal coefficient matrix.
Select the Lyapunov function of the first step as
Differentiating (48) with respect to time, one can get
If , then . So, the sliding surface function is defined as , and the Lyapunov function of the second step is selected aswhere is a positive definite diagonal coefficient matrix according to the DOF of robotic manipulator.
Differentiating (50) with respect to time, one can get
An adaptive algorithm can be assigned for nonlinear function, yieldingwhere is the estimation error and is the estimated value of the nonlinear function .
The Lyapunov function of the third step is selected as
Differentiating (53) with respect to time, one can get
So, the adaptive control law is derived as .
Considering the boundary of and Property 3, (54) can be rewritten as follows:where
By giving the proper values of , , and , the matrix can be positive, and , and the stability of the robotic manipulator is guaranteed.
4. Simulation and Discussion
In this section, a twolink robotic manipulator is utilized to verify the effectiveness of the proposed control strategy. The structural parameters are described in Table 1, and the dynamic equation is given by (35). The desired trajectory tracking of two joints is .
The parameter values used in the adaptive backstepping sliding mode control system are , , , and the external disturbance is added by a pulse signal in 6 s, which is used to verify the robustness and stability of the twolink robotic manipulator system. The schematic for backstepping algorithm by using Matlab^{@} software is shown in Figure 3, and the schematic for the proposed control strategy is shown in Figure 4.
Figure 5 shows the trajectory tracking performance of the proposed control with the actuated torque in joint space and , shown in Figure 6, respectively.
(a)
(b)
(a)
(b)
The trajectory tracking errors of the two joints are shown in Figure 7, respectively. The maximum tracking error of the joint is equal to 0.0296 mm and in 1.6 s is 5.92%. The maximum tracking error of the joint is equal to 0.0896 mm and in 1.2 s is 17.92% and in 2.2 s tends to convergence. However, the convergence time of joint is in 2.8 s, which is affected by the coupling of joint.
The trajectory tracking performance in endeffector space (operating space) is shown in Figure 8. Furthermore, the mean square error of the estimation function is derived, shown in Figure 9. Figure 9 shows that the maximum estimation error is , and the minimum error is , meaning that the proposed controller has a good effect in nonlinear estimation and tracking abilities.
To verify the robustness of the proposed control system, an external disturbance is added to the system in 6 s. The trajectory tracking of the twolink robotic manipulator under external disturbances is shown in Figure 10. Furthermore, the trajectory tracking errors in joint space are shown in Figure 11. It shows that the maximum tracking error of the joint is equal to 0.0316 mm, and the maximum tracking error of the joint is equal to 0.076 mm. According to the given external disturbance, the percentages of the tracking errors are 6.32% and 15.2%, respectively.
(a)
(b)
5. Conclusions
In this article, an adaptive backstepping sliding mode control subject to external disturbance is proposed. The dynamic model of the robotic manipulator is built by considering the coupling and nonlinear characteristics, and the estimate function of these nonlinear factors is proposed and used for the equivalent control law of sliding mode control. The control system is designed by the backstepping algorithm, and the stability and robustness of the twolink robotic manipulator are analyzed. Simulation results show that the proposed control system has good tracking performance and strong robustness for the external disturbance.
Data Availability
The data used to support the findings of this study are available from the corresponding author upon request.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
Acknowledgments
The authors are grateful for the financial support from the National Natural Science Foundation of China (Grant no. 51165009) and Innovation School Project of Education Department of Guangdong Province, China (Grant nos. 2017KZDXM060 and 2018KCXTD023).
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Copyright © 2020 Zhu Dachang et al. 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.