Robust Tracking Control for Robotic Manipulator via Fuzzy Logic System and H ∞ Approaches

Based on fuzzy logic system (FLS) andH ∞ control methodologies, a robust tracking control scheme is proposed for robotic system with uncertainties and external disturbances. FLS is employed to implement the framework of computed torque control (CTC) method via its approximate capability which is used to attenuate the nonlinearity of roboticmanipulator.The robustH ∞ control can guarantee robustness to parametric and dynamics uncertainties and also attenuate the effect of immeasurable external disturbances entering the system. Moreover, a quadratic stability approach is used to reduce the conservatism of the conventional robust control approach. It can be guaranteed that all signals in the closed-loop are bounded by employing the proposed robust tracking control. The validity of the proposed control scheme is shown by simulation of a two-link robotic manipulator.

It is noteworthy that varieties of hybrid control systems have been designed for controlling the complex robotic systems using different combinations of the above-mentioned methods.For example, in [13,15], neural networks were utilized to approximate the equivalent control of variable structure control entirely, and then the  ∞ techniques were applied to achieve certain tracking performance.As a feedback linearization control technique, CTC was usually included in controller designing for complex robotic system due to its good performances [5,6,16,17].In order to eliminate the effect of the uncertainties, Song et al. [5] proposed an approach of CTC plus fuzzy compensator for robotic system; the nominal system was controlled by using CTC method and for uncertain system a fuzzy controller acts as compensator.In [16], CTC plus a neural network compensator was proposed and simulations were conducted on a two-link robotic manipulator; furthermore, an experimental example was tested on PUMA560.However, they assumed that system actual acceleration was measurable.Although the acceleration can be obtained through installing accelerometers on the robotic systems, the measurement noises and weight of these extra utilities would both sacrifice the tracking performance of robotic systems [17].Without robotic system acceleration information, Peng et al. [17] proposed robust hybrid tracking control for robotic system, which combines CTC with a neural network-based robust compensator.Kim et al. [19] presented a model-based motion control approach for industrial robots by considering a serial two-link robot arm model with joint nonlinearities.Chen et al. [20] proposed two types of adaptive control scheme combining conventional CTC and different fuzzy compensators for the robust tracking control of robotic manipulators with structured and unstructured uncertainties.Recently, Peng and Liu [6] proposed an adaptive robust quadratic stabilization tracking control scheme

Preliminaries
Standard notations are used in this paper.Let R be the real number set, let R  be the -dimensional vector space, and let R × be the  ×  real matrix space.The norm of vector  ∈ R  and that of matrix  ∈ R  are defined, respectively, as ‖‖ = √    and ‖‖ = tr(  ).If  is a scalar, then ‖‖ denotes the absolute value. min () and  max () are the minimum and the maximum eigenvalues of matrix , respectively.  ∈ R × is an  ×  identity matrix, tr(⋅) is the trace operator, and sgn(⋅) is the standard sign function.

Description of Fuzzy Logic System.
As shown in Figure 1, a typical fuzzy system comprises the fuzzy rule base, the fuzzifier, the fuzzy inference engine working on fuzzy rules, and the defuzzifier.The fuzzy inference engine performs a mapping from fuzzy sets in input space  ∈ R  to fuzzy sets in output space  ∈ R  based on fuzzy rules, where In general, consider a system with  inputs  = [ Once the inputs  = [ 1 ,  2 , . . .,   ]  are given, the output  of the fuzzy inference system with weighted-center defuzzifier, product inference, and singleton fuzzifier can be derived from the following form: where   is the point in   at which     (  ) achieves its maximum value.Without loss of generality, we assume that     (  ) = 1.
Assume that fuzzy basis functions are defined as follows: Then, the fuzzy system in (2) can be rewritten as where () = [ 1 (),  2 (), . . .,   ()]  ∈ R  is called the fuzzy basic function vector or the antecedent function vector and  = [ 1 ,  2 , . . .,   ]  ∈ R  is called the parameter vector.It has been proved that the adaptive fuzzy system can approximate any real continuous function over a compact set to arbitrary accuracy [10].

Robotic Manipulator Dynamic.
Consider a general link rigid robotic manipulator, which takes into account the external disturbances, with the equation of motion given by [5,17]  () q +  (, q ) q +  () +   = , where , q , and q ∈ R  are joint position, velocity, and acceleration vectors of the robot, respectively; () ∈ R × is the symmetric and positive definite inertia matrix; (, q ) ∈ R × is the effect of Coriolis and centrifugal forces; () ∈ R  is the gravity vector;   ∈ R  denotes unknown disturbances including unstructured dynamics and unknown payload dynamics. ∈ R  is the torque input vector.
For convenience, dynamical model ( 5) can be rewritten as the following compact form: where (, q ) = (, q ) q + ().The following properties are required for the subsequent development.
Property 1.The inertia matrix () is symmetric and positive definite, which is uniformly bounded and satisfies where   and   are some positive constants.
The parameters (), (, q ), and () in dynamical model (5) are functions of physical parameters of manipulators like links masses, links lengths, moments of inertia, and so on.The precise values of these parameters are difficult to acquire due to measuring errors, environment, and payloads variations.Therefore, here it is assumed that actual values (), (, q ), and () can be separated as nominal parts denoted by  0 (),  0 (, q ), and  0 () and uncertain parts denoted by Δ(), Δ(, q ), and Δ(), respectively.These variables satisfy the following relationships: Assumption 2. The bound of uncertainty parameters is known, which can be expressed as where   ,   , and   are positive constants.

Computed Torque Control Method for Robot.
As a nonlinear feedback linearization approach, CTC method is a common control scheme for nonlinear system; in particular, there are strongly coupled and highly nonlinear manipulator dynamics.According to the CTC method, the control law for robot can be chosen as where  are the tracking error defined by  =   −  and  and   are the joint and desired output trajectories for each joint.
The coefficients  V and   should be chosen such that all the roots of the polynomial ℎ() =  2 +  V  +   are in the open left-half plane.
Assumption 3. The desired trajectories   are continuous and bounded known functions of time with bounded known derivatives up to the second order.
Substituting ( 12) into (11) yields Remark 4. It should be noted that CTC approach is based on feedback linearization technique, which results in a linear time-invariant closed-loop system (13), implying acquirement of globally asymptotical stability.Furthermore, explicit conditions for performance indices may be obtained in terms of controller gain matrices.More specifically, globally asymptotical stability is guaranteed provided that   and  V in (13) are symmetric and positive definite constant matrices.
According to analysis above, CTC strategy relies on strong assumptions that exact knowledge of robotic dynamics is precisely known and unmodeled dynamics has to be ignored, which is impossible in practical engineering.Motivated by the idea of CTC introduced above, one can image that CTC is used to control nominal system and another robust controller attaching to CTC for uncertain system can be designed.In this way, applying (12) and ( 13) to practical robotic manipulator system (6) yields where (, q ) =  −1 0 ()[Δ() q + Δ(, q ) q + Δ() +   ] is a function of joint variables, physical parameters, parameters variations, unmodeled dynamics, and so on.
The controller can be defined as where  0 is CTC defined like (12) and  is a robust control law to be determined in next section.
Remark 5.All the parameters in the proposed scheme may be uncertain, which is true in practical situation.In some publications like [9], it is clearly unrealistic that only Coriolis terms, gravity terms, and friction terms may be uncertain, whereas inertia matrix was assumed to be known exactly.Literatures [5,6,17] assumed that the nominal system is completely known, which is controlled using computed torque method.Aiming at the uncertain system, a fuzzy or neural controller acts as compensator for computed torque method.However, in practical engineering, this assumption may be unreasonable.Therefore, an assumption is given as follows.
Then, we will utilize an FLS to approximate the nominal system in next section.
Assumption 6.It is assumed that only the nominal parameter of the inertia matrix  0 () is known; other nominal parameters  0 (, q ) and  0 () are both assumed to be unknown.
Remark 7. Assumption 6 is reasonable, since in reality the inertia matrix of a robotic manipulator is relatively easy to obtain an acceptable precision; furthermore, the parametric uncertainty of inertia matrix is considered.
Up to now, the control objective can be restated to seek a robust control law as a compensator for CTC, which is approximated by FLS due to the unknown nominal parameters  0 (, q ) and  0 (), such that joint motions of robotic systems (6) can follow the desired trajectories.

Robust Tracking Control Design Based on FLS
In this section, FLS is employed to implement the framework of CTC method (12).Firstly, it is assumed that the right-hand side in (12) can be represented by an ideal: where   (, q ) is fuzzy system of the form of (4). is the weight matrix and (, q ) denotes generalization result.We assume that there exists Ω  , and ideal parameter is in the compact set Ω  .The ideal parameter can be defined as Assumption 8.The norm of optimal weight matrix  * is bounded so that ‖ * ‖ ≤   .The reconstruction error  is bounded; that is, ‖‖ ≤   , where   is a positive constant.
According to the above analysis, the architecture of closed-loop system is shown in Figure 2.
Remark 12. Noting that the inputs of designed controller include velocity signals q , here it is assumed that the velocities are measurable.Compared with [5,16], the acceleration signals q are no longer needed to be measurable.Remark 13.Since the controller equation (30) contains the sign function, direct application of such control signals to the robotic system (6) may result in chattering caused by the signal discontinuity.To overcome this problem, the control law is smoothed out within a thin boundary layer Φ  [18] by replacing the sign function by a saturation function defined as Remark 14.The above stability result is achieved under the assumption that all the parameter vectors are within the constraint sets or on the boundaries of the constraint set but moving their interior (‖‖ ≤   ).To guarantee that the parameters are bounded, the adaptive laws (33) can be modified by using the projection algorithm [11] as follows: where the projection operator Pr(⋅) is defined as

Simulation Example
To verify the theoretical results, simulations are carried out in two degrees of freedom robot manipulator as shown in Figure 3 described by [6,17]  () where  1 and  2 are the mass of link 1 and link 2, respectively;  1 and  2 are the length of link 1 and link 2, respectively;   denotes sin   ;   denotes cos   ; and   denotes cos(  +   ) for  = 1, 2 and  = 1, 2.  is acceleration of gravity.
The robotic manipulator parameters used for the simulation are shown in Table 1, where the nominal values are used to calculate the controller functions to design the robust adaptive controller and the actual values are used to test the robustness of the controller.Furthermore, the external disturbances are assumed to be   = [−5 cos(5) −5 sin(5)] .The simulation sampling steps length is given by 0.01 s.
For comparison, the conventional CTC equation ( 12) under the same conditions is also demonstrated.The gains are chosen as   = diag[50, 50] and  V = diag [20,20].Figures 4  and 5 show the tracking results of  1 and  2 with CTC and the proposed FLS-based robust tracking control method, Figures 6 and 7 show the tracking errors  1 and  2 , and Figures 8 and 9 show the control inputs  1 and  2 , respectively.Figures 10 and 11 show the weights  1 and  2 of fuzzy logic system.It can be seen that the CTC controller cannot drive the joints to reach the desired positions and steadystate tracking error exists, while the tracking errors go to small values by the proposed method after some transients, which are caused by the initial choice of the consequent parameters.However, the tracking error decreases quickly since the online learning of fuzzy logic system and the effect of uncertainties are successfully compensated by the robust control term.The simulation results thus demonstrate that the proposed FLS-based robust tracking control method can Journal of Control Science and Engineering effectively control the rigid robotic manipulator with uncertainties.
To quantify the control performance, the root-mean square average of tracking error (based on the  2 norm of the tracking errors ) is given as follows [6,16]: where  represents the total simulation time.Table 2 shows the  2 error norms for CTC method, adaptive robust quadratic stabilization tracking control (ARQSTC) method [6],   neural network-based robust  ∞ control (NNRHC) method [16], and the proposed FLS-based robust tracking control method.Note that a smaller  2 norm represents a better performance.
From Table 2, the proposed control method shows the smaller tracking error norm in comparison to the CTC and   ARQSTC methods and the slight larger tracking error norm in comparison to the NNRHC method.It should be noted that the system nominal model was assumed to be known exactly and the acceleration signal q was assumed to be measurable, which were both used to implement the NNRHC methods, while the system nominal model and acceleration signal were not included in the proposed controller.In general, more system information used in controller design leads to a better tracking performance.

Conclusions
This paper presents a robust tracking control scheme for robotic manipulator with mathematical derivations of global  stability.The idea is to combine an FLS controller with a robust compensator.Here, we assumed that the nominal parameters of robotic manipulator are unknown except the nominal inertia matrix.The FLS is employed to implement the framework of CTC method which acts as the main controller, and the robust compensator is used to handle system uncertainties and external disturbances.In addition, an  ∞ controller is used to achieve a certain tracking performance.The intelligent hybrid control method demonstrated robust and effective control performance on robotic manipulator having uncertainties with good disturbance rejection.

Figure 1 :
Figure 1: The basic configuration of a fuzzy logic system.

Figure 2 :
Figure 2: The architecture of closed-loop system.

Figure 3 :
Figure 3: Diagram of a two-link robot manipulator.