1. Introduction
Enormous amount of real time robot arm research work is still being carried out in different aspects, especially on dynamics of robotic motion and their governing equations. Taha [5] discussed the dynamics of robot arm problems. Research in this field is still ongoing and its applications are massive. This is due to its nature of extending accuracy in order to determine approximate solutions and its flexibility. Many studies [48] have reported different aspects of linear and nonlinear systems. Robust control of a general class of uncertian nonlinear systems are investigated by zhihua [10].
Most of the initial value problems (lVPs) are solved using RungeKutta (RK) methods which in turn are employed in order to calculate numerical solutions for different problems, which are modelled in terms of differential equations, as in Alexander and Coyle [11], Evans [12], Shampine and Watts [14], Shampine and Gordan [18] codes for the RungeKutta fourth order method. RungeKutta formula of fifth order has been developed by Butcher [1517]. Numerical solution of robot arm control problem has been described in detail by Gopal et al.[19]. The applications of nonlinear differential–algebraic control systems to constrained robot systems have been discussed by Krishnan and Mcclamroch [22]. Asymptotic observer design for constrained robot systems have been analyzed by Huang and Tseng [21]. Using fourth order RungeKutta method based on Heronian mean (RKHeM) an attempt has been made to study the parameters concerning the control of a robot arm modelled along with the single term Walsh series (STWS) method [24]. Hung [23] discussed on the dissipitivity of RungeKutta methods for dynamical systems with delays. Ponalagusamy and Senthilkumar [25,26] discussed on the implementations and investigations of higher order techniques and algorithms for the robot arm problem. Evans and Sanugi [9] developed parallel integration techniques of RungeKutta form for the step by step solution of ordinary differential equations.
This paper is organized as follows. Section 2 describes the basics of robot arm model problem with variable structure control and controller design. A brief outline on parallel RungeKutta integration techniques is given in section 3. Finally, the results and conclusion on the overall notion of parallel 2stage 3order arithmetic mean RungeKutta algorithm and obtains almost accurate solution for a given robot arm problem are given in section 4.
2. Statement of the robot arm model problem and essential variable structure
2.1. Model of a robot arm
It is well known that both nonlinearity and coupled characteristics are involved in designing a robot control system and its dynamic behavior. A set of coupled nonlinear second order differential equations in the form of gravitational torques, coriolis and centrifugal represents dynamics of the robot. It is inevitable that the significance of the above three forces are dependent on the two physical parameters of the robot namely the load it carries and the speed at which the robot operates. The design of the control system becomes more complex when the end user needs more accuracy based on the variations of the parameters mentioned above. Keeping the objective of solving the robot dynamic equations in real time calculation in view, an efficient parallel numerical method is needed. Taha [5] discussed dynamics of robot arm problem represented by as
where
For a robot with two degrees of freedom, by considering lumped equivalent massless links, i.e. it means point load or in this case the mass is concentrated at the end of the links, the dynamics are represented by
where
The values of the robot parameters used are
where
Here, the robot is simply a double inverted pendulum and the Lagrangian approach is used to develop the equations.
In [5] it is found that by selecting suitable parameters, the nonlinear equation (3) of the twolink robotarm model may be reduced to the following system of linear equations:
where one can attain the system of second order linear equations:
with the parameters concerning joint1 are given by
A_{10} = 0.1730, A_{11} = 0.2140, B_{10} = 0.00265,
and the parameters of joint2 are given by
A_{20}= 0.0438, A_{21} = 0.3610, B_{20} = 0.0967
If we choose
considering
3. A brief sketch on parallel RungeKutta numerical integration techniques
The system of second order linear differential equations originates from mathematical formulation of problems in mechanics, electronic circuits, chemical process and electrical networks, etc. Hence, the concept of solving a second order equation is extended using parallel RungeKutta numerical integration algorithm to find the numerical solution of the system of second order equations as given below. It is important to mention that one has to determine the upper limit of the stepsize
with
3.1. Parallel RungeKutta 2stage 3order arithmetic mean algorithm
A parallel 2stage 3order arithmetic mean RungeKutta technique is one of the simplest technique to solve ordinary differential equations. It is an explicit formula which adapts the Taylor’s series expansion in order to calculate the approximation. A parallel RungeKutta 2stage 3order arithmetic mean formula is of the form,
Hence, the final integration is a weighted sum of three calculated derivatives per time step is given by,
Parallel 2stage 3order arithmetic mean RungeKutta algorithm to determine y_{j} and
The corresponding parallel 2stage 3order arithmetic mean RungeKutta algorithm array to represent equation (9) takes the form
0 




1 
1 

1  4  1 
Therefore, the final integration is a weighted sum of three calculated derivatives per time step given by,
3.2. Parallel RungeKutta 2stage 3order geometric mean algorithm of typeI
The parallel 2stage 3order geometric mean RungeKutta formula of type–I is of the form,
Hence, the final integration is a weighted sum of three calculated derivates per time step which is given by,
Parallel 2stage 3order geometric mean RungeKutta algorithm of type–I to determine y_{j} and
parallel RungeKutta 2stage 3order geometric mean of type–I array represent equation (13) takes the form
Hence, the final integration is a weighted sum of three calculated derivatives per time step and the parallel RungeKutta 2stage 3order geometric mean of type–I formula is given by,
0 




1 
1 



The parallel 2stage 3order geometric mean RungeKutta formula of type–II is of the form,
Hence, the final integration is a weighted sum of three calculated derivates per time step given by,
Parallel 2stage 3order geometric Mean RungeKutta algorithm of type–II to determine y_{j} and
The corresponding parallel RungeKutta 2stage 3order geometric mean algorithm of typeII array to represent Equation (17) takes the form:
0 






Therefore, the final integration is a weighted sum of three calculated derivatives and the parallel RungeKutta 2stage 3order geometric mean algorithm formula is given by
4. Results and conclusion
In this paper, the ultimate idea is focused on making use of parallel integration algorithms of RungeKutta form for the step by step solution of ordinary differential equations to solve system of second order robot arm problem. The discrete and exact solutions of the robot arm model problem have been computed for different time intervals using equation (5) and y_{n+1}. The values of
To obtain better accuracy for
Sol. No.  Time  Exact Solution 
Parallel RKAM Solution 
Parallel RKAM Error 
1  0.00  1.00000  1.00000  0.00000 
2  0.25  0.99365  0.99533  0.00167 
3  0.50  0.97424  0.97864  0.00440 
4  0.75  0.94124  0.94943  0.00819 
5  1.00  0.89429  0.90733  0.01303 
Sol.  Time  Exact Solution 
Parallel RKAM Solution 
Parallel RKAM Error 
1  0.00  0.00000  0.00000  0.00000 
2  0.25  0.05114  0.04598  0.00515 
3  0.50  0.10452  0.09412  0.01044 
4  0.75  0.15968  0.14389  0.01578 
5  1.00  0.21610  0.19499  0.02110 
Sol. No.  Time  Exact Solution 
Parallel RKAM Solution 
Parallel RKAM Error 
1  0.00  1.00000  1.00000  0.00000 
2  0.25  0.99965  0.99973  0.00008 
3  0.50  0.99862  0.99871  0.00009 
4  0.75  0.99693  0.99700  0.00007 
5  1.00  0.99460  0.99462  0.00001 
Sol. No.  Time  Exact Solution 
Parallel RKAM Solution 
Parallel RKAM Error 
1  0.00  0.00000  0.00000  0.00000 
2  0.25  0.00277  0.00285  0.00007 
3  0.50  0.00545  0.00560  0.00015 
4  0.75  0.00805  0.00879  0.00074 
5  1.00  0.01056  0.01084  0.00028 
Similarly, by repeating the same computation process for parallel RungeKutta 2 stage 3 order geometric mean algorithm of typeI and typeII respectively, yield the required results. It is pertinent to pinpoint out that the obtained discrete solutions for robot arm model problem using the 2parallel 2processor 2Stage 3order arithmetic mean RungeKutta algorithm gives better results as compared to 2parallel 2procesor 2stage 3order geometric mean RungeKutta algorithm of typeI and 2parallel 2procesor 2stage 3order geometric mean RungeKutta algorithm of typeII. The calculated numerical solutions using 2parallel 2procesor 2stage 3order arithmetic mean Runge Kutta algorithm is closer to the exact solutions of the robot arm model problem while 2parallel 2procesor 2stage 3order geometric mean RungeKutta algorithm of typeI and typeII gives rise to a considerable error. Hence, a parallel RungeKutta 2stage 3order arithmetic mean algorithm is suitable for studying the system of second order robot arm model problem in a real time environment. This algorithm can be implemented for any length of independent variable on a digital computer.
Acknowledgments
The first author would like to extend his sincere gratitude to Universiti Sains Malaysia for supporting this work under its postdoctoral fellowship scheme. Much of this work was carried out during his stay at Universiti Sains Malaysia in 2011. He wishes to acknowledge Universiti Sains Malaysia’s financial support.
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