Discrete Dynamics in Nature and Society
Volume 2016 (2016), Article ID 2952738, 10 pages
http://dx.doi.org/10.1155/2016/2952738

Research Article

Control Law Design for Twin Rotor MIMO System with Nonlinear Control Strategy

M. Ilyas,¹ N. Abbas,² M. UbaidUllah,² Waqas A. Imtiaz,¹ M. A. Q. Shah,² and K. Mahmood¹

¹Department of Electrical Engineering, Iqra National University, Peshawar 25000, Pakistan
²Department of Electrical Engineering, CIIT, Islamabad 45550, Pakistan

Received 13 May 2016; Revised 10 June 2016; Accepted 15 June 2016

Academic Editor: Jean J. Loiseau

Copyright © 2016 M. Ilyas 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.

Abstract.

Modeling of complex air vehicles is a challenging task due to high nonlinear behavior and significant coupling effect between rotors. Twin rotor multi-input multioutput system (TRMS) is a laboratory setup designed for control experiments, which resembles a helicopter with unstable, nonlinear, and coupled dynamics. This paper focuses on the design and analysis of sliding mode control (SMC) and backstepping controller for pitch and yaw angle control of main and tail rotor of the TRMS under parametric uncertainty. The proposed control strategy with SMC and backstepping achieves all mentioned limitations of TRMS. Result analysis of SMC and backstepping control schemes elucidates that backstepping provides efficient behavior with the parametric uncertainty for twin rotor system. Chattering and oscillating behaviors of SMC are removed with the backstepping control scheme considering the pitch and yaw angle for TRMS.

1. Introduction

Recent times have witnessed the evolution of various approaches for proper flight of air vehicles such as helicopter. Modeling of air vehicles dynamics is difficult owing to the significant coupling effect among rotors and the unavailability of some system states. The laboratory setup, twin rotor MIMO system (TRMS), is readily utilized, which resembles the flight of a helicopter [1]. It has gained much importance among the control community by serving as a tool for different experiments and providing real time environment of an air vehicle.

It is difficult to design a controller for TRMS due to its nonlinear behavior between two axes [2, 3]. TRMS consists of a beam with two rotors connected at its ends which are driven by separate DC motors and the beam is counterbalanced by an arm having weight at its end [4]. It has two degrees of freedom, which facilitate movements in both horizontal and vertical direction. TRMS is basically a prototype model of a helicopter; however, there is significant difference in control of helicopter and its prototype. In order to control TRMS in a desired way, the speed of rotors is altered, while in helicopter it is done by changing the angles of rotors. There is no cylindrical control in TRMS while in helicopter it is used in directional control [5].

The control problem of TRMS has gained much attention, owing to the high coupling effect between two propellers, unstable and nonlinear dynamics. Several techniques like observer based and hybrid adaptive fuzzy output feedback control approaches are developed to solve the nonlinear MIMO system with unknown control direction and dead zones [6, 7]. Genetic algorithms to control the unstable and nonlinear dynamics in TRMS are designed using PID control [8]. Adaptive fuzzy sliding mode control is developed for a class of MIMO nonlinear system which estimates the states from a semi high gain observer to construct the output feedback fuzzy controller by incorporating the dynamic sliding mode [9]. References [10, 11] developed the observer based adaptive fuzzy backstepping dynamic surface control (DSC) for nonlinear MIMO with immeasurable states. [12] performs a comparative analysis between intelligent control and classical control for TRMS. Adaptive fuzzy, neural network, and feedback linearization based controllers are also designed for the tracking of yaw and pitch angles in TRMS [4, 13–22]. However, the proposed state variables are assumed measurable, which is practically not feasible to control the pitch and yaw angle of TRMS.

This paper proposes the first-order sliding mode control and backstepping control scheme for the nonlinear TRMS. In our proposed methodology, the mathematical model of TRMS is linearized and the cross coupling effect between the main rotors is considered as disturbance. The main advantage of SMC is that it mitigates the parametric uncertainty present in TRMS while backstepping control algorithm performs better in case of external disturbance, which in this case is the coupling effect of the rotors, because of its recursive structure. The proposed approaches are investigated for TRMS keeping in view the need for cancelling the strong coupling between rotors and finally providing the desired tracking response of both the controllers. Simulation results show the effectiveness of control algorithms but comparatively the backstepping scheme gives the best performance in terms of stability and reference tracking.

The remainder of the paper is arranged as follows. In Section 2, the mathematical model of TRMS system is introduced and the parameters of the system are specified. The proposed SMC and backstepping controller along with their simulation results are given in Sections 3 and 4, respectively. Comparison of proposed controllers is introduced in Section 5 followed by the concluding remarks.

2. Mathematical Modeling

Figure 1 shows the TRMS laboratory setup, which is used to develop the mathematical model to compare the operation of SMC and backstepping controllers.

Figure 1: TRMS laboratory setup.

TRMS system is designed with two rotors (main rotor and tail rotor) as shown in Figure 1 encompassing the effect of forces like gravitational, propulsion, centrifugal, frictional, and disturbance torque on movement of the propellers. To overcome the effects of these forces we provide control input through motors. In the given case, only the pitch and azimuth angles are the measureable outputs and its stability is the main objective of designing the controller. As far as the mechanical unit is concerned the following nonlinear momentum equations can be derived for the pitch movement of TRMS [1]. Consider

where

where and are constants. Equation (5) is derived based on law of conservation of angular momentum of main rotor:

The momentum equations in the vertical plane of motion are written as

Similar momentum equation can be used for the horizontal plane motion as well.

Equation (9) is derived based on law of angular conservation of momentum of main rotor. The state space equations are as follows:

(i)For main motor,

(ii)For tail motor,

where and are the motor gain and , , , and are the motor parameter. and are momentum of main motor and tail motor. The linearization of the nonlinear model of TRMS is given in the following section.

(A) Linearization of TRMS. The state space equation of nonlinear system along with the parameters is given by the following equations:

The state and output vectors are given by

where variables are as follows (Table 1):

 : pitch (elevation) angle. : yaw (azimuth) angle. : momentum of main rotor. : momentum of tail rotor.

Table 1: Values of tuning parameters for SMC.

Pitch angle (rad)
Yaw angle (rad)

Case 1
= 2.102
= 3.5
= 1.5
= 6.305
= 0.0875
= 4.9

Case 2
= 1.1
= 3.5
= 1.95
= 1.3
= 0.088
= 2.897

Case 3
= 2.5
= 3.5
= 1.915
= 3.3
= 0.088
= 2.89

Here all the variables of system are expressed in term of “.” So

Now the state space of the system in term of variable “” will become

To linearize the system, let the system be represented as

where as states, as the control input, and as the measured output.

Consider

After taking Jacobean and putting point , then resulting system matrices are given below. Consider

(B) State Space Equations of Linearized Model. Values of constants are given in the Abbreviation section [1]. By putting values of all these constants, the state space equations can be given as

3. Proposed SMC Controller

(A) Choosing Sliding Surface. SMC is a nonlinear control technique, which deals with the capability of controlling the uncertainties of nonlinear systems [23, 24]. The primary advantage of the SMC technique is the low sensitivity to system disturbances. Moreover, it accredits the decoupling of the lower dimensions, and consequently, it scales down the complication of feedback design [25]. SMC generally consist of two phases: reaching phase and the sliding phase. The reaching phase converges the system states to desired surface and sliding phase handles the oscillations. Sliding surface can be designed as

Now the control input consist of two parts:

(i)Equivalent controller, .(ii)Discontinuous controller, .

Consequently, the required controller can be determines as

(B) SMC Design for Linearized Model. This section outlines the SMC design for linearized model. Sliding surface of the system is designed at first to facilitate the process. TRMS is a MIMO system so we will design two sliding surfaces.

Sliding surface for the vertical plane is as follows:

Lyapunov condition is satisfied as

Sliding surface for horizontal plane is as follows:

where we have the following.

Lyapunov condition is satisfied as

3.1. Simulation Results of SMC

Figures 2 and 3 show the pitch and yaw position of TRMS obtained after implementation of SMC in Simulink MATLAB on linearized model. It is clear that the desired objective of regulating the system for two degrees of freedom has been achieved under the robust control action of SMC. It is shown that settling time for pitch and yaw angles is under 3 and 5 seconds, respectively. Moreover, it is observed that steady state error is approximately zero. Therefore, the proposed TRMS attains the equilibrium position with respect to pitch and yaw movement under applied control action.

Figure 2: Pitch angle for linearized system using SMC.

Figure 3: Yaw angle for linearized system using SMC.

The control inputs and for pitch and yaw movements of TRMS, respectively, are in volts and provided by two independent DC motors connected to corresponding rotors. The control inputs contain two types of control action, that is, the equivalent and discontinuous control. It is evident from Figures 2 and 3 that the corresponding equivalent control efforts successfully drive the system dynamics to corresponding sliding surfaces in a short period of time.

Moreover, the discontinuous control parts efficiently maintain the system states on sliding manifolds for all subsequent times and are responsible for system robustness against uncertainties. However, chattering in control inputs “” and “” can be clearly seen from Figures 4 and 5, which arises due to fast switching of discontinuous control action around the sliding manifolds. Since the amplitude of chattering is small, both yaw and pitch movement of TRMS are not affected by this undesired phenomenon.

Figure 4: Control input for pitch angle.

Figure 5: Control input for yaw angle.

The sliding manifolds and have been designed by linearly combining system states for regulation purpose of TRMS under system uncertainties and significant coupling but in the absence of external perturbation. The tuning parameters have been suitably adjusted for sliding surface. Figures 6 and 7 show chattering phenomenon of sliding surface for pitch and yaw angle of TRMS. It is observed that the chattering phenomenon in the sliding surface is miniscule. Moreover, the settling time of sliding surface for both vertical and horizontal planes is under 1 second, which is desirable for the system under consideration. Now another discussion is provided about the implementation of SMC with tracking.

Figure 6: Chattering in sliding surface for pitch angle.

Figure 7: Chattering in sliding surface for yaw angle.

3.2. Simulation Results of TRMS with Tracking

The pitch and yaw angles are obtained after implementation of SMC on a TRMS in Simulink MATLAB. Figure 8 shows the response of pitch angle at different values of tuning parameters and Figure 9 shows the response of yaw angle at different values of tuning parameters. Figure 8 shows that the tuning parameter for case 1 has approximately 20% overshoot from the desired position. Case  3 is showing undershoot from the desired position due the difference tuning parameters. It is observed that case 2 is the most suitable set of tuning parameters for achieving the desired results without showing over- and undershoot. Thus, the desired objective of regulating the system for two degrees of freedom has been achieved under the robust control action of SMC. Same phenomenon is obtained for yaw angle in Figure 9, where case 2 is most preferable with desired tuning parameters.

Figure 8: Response of pitch angle for TRMS.

Figure 9: Response of yaw angle for TRMS.

Different values of tuning parameters show how we can obtain different responses of pitch and yaw angles according to requirements. Overshoot problem is faced in case of sharp response, and if we need a slower response then settling time is increased.

4. Backstepping Controller

In control system theory, backstepping controller scheme is introduced by Krstic in 1995 and his companions for designing stability control system for a special class of linear and nonlinear dynamical system [26]. Backstepping is a systematic, Lyapunov-based method for nonlinear control which refers to the recursive nature of the design procedure which starts at the scalar equation separated by the largest number of integrations from the control input and steps back towards the control input [27, 28].

In the theory of Ordinary Differential Equations (ODEs), Lyapunov functions are scalar functions that may be used to prove the stability of equilibrium of an ODE. The basic idea behind the Lyapunov function method consists of (I) choosing a radially unbounded positive definite Lyapunov function candidate and (II) evaluating its derivative along system dynamics and checking its negativeness for stability analysis [25, 26].

The recursitivity terminates when the final control phase is reached. The process which receives its stability through recursitivity is called backstepping [27, 28]. Backstepping can be used for tracking and regulation problem. With the aid of Lyapunov stability, this control approach for asymptotic tracking can be achieved.

4.1. Design Steps

The controller is designed using backstepping control technique on the proposed control problem. The standard backstepping control is based on step-by-step construction of Lyapunov function. Here we design controller, based on Lyapunov function.

(A) For Vertical Plane. First of all in 1st step we introduce a new state

where “” is the new state and “” is state variable.

Lyapunov candidate function (LCF) for new state is

where , , and .

By taking time derivative of Lyapunov function we get

where “” is virtual control input to control the system and is the new state

Now another new state is introduced that is given by “”:

where “” is the second state variable of the system. By putting values in (28) we get

Now again we repeat the previous step to calculate the virtual control input.

So,

We know

New Lyapunov candidate function (LCF) is as follows:

Taking derivative of (35),

By putting the value of and we get

where

Now again we introduce new state

By taking derivative we get

Now LCF will be as

Taking derivative of above equation, we get

By putting the values in above equation we obtain another virtual control input for the second state of the system as given below:

After differentiation,

Now the control input is given by

We get the control input for vertical plane of TRMS. Now putting the value of control input we get

Hence condition is satisfied and system is asymptotically stable.

(B) For horizontal Plane. First of all in 1st step we introduce a new state

where is the new state and is the state variable.

LCF for new state is

By taking derivative and putting the value of variables we get

Now, the abovementioned steps are repeated to calculate the other virtual control input.

Consequently,

Here is arbitrary control input to converge the state towards stability.

Introduce another new state to calculate another arbitrary control input.

So,

where is new state for state variable . By taking time derivative,

Also we take time derivative of previous arbitrary control input as

Now LCF will be as

By taking derivative and putting the values of and we get

After some algebraic calculations we get

where is the new state:

Now LCF will be as

After taking derivative,

where

By taking derivative of (60),

By putting the values of , , and we get

where is negative definite and system will be asymptotically stable. Now we get control input for the horizontal plane. Finally we get the control law, which will regulate all the states of the system to the origin. The system is asymptotically stable by using Backstepping design method:

After mathematical calculations of backstepping controller, we use user defined block from MATLAB Simulink library. Required equations are used in this function. On the basis of simulation results we will elaborate the performance of controller which is given below

4.2. Simulation Results of Backstepping Controller

On the basis of backstepping control design, simulation results in Figures 10 and 11 show the stability response of pitch angle and the control input for pitch angle, respectively. Similarly Figures 12 and 13 show the stability of yaw angle and control input for the pitch angle, respectively. It is observed that settling time for pitch and yaw angle in case of backstepping technique is less as compared to sliding mode control. The controller shows very promising results and it is found that backstepping controller is capable of tracking and little variation in control inputs of both pitch and yaw angle.

Figure 10: Pitch angle (rad) for backstepping.

Figure 11: Control input for pitch angle.

Figure 12: Yaw angle (rad) for backstepping.

Figure 13: Control input for yaw angle.

The performance of SMC is limited due to chattering in control inputs but this issue is resolved through backstepping, which is shown in Figures 11 and 13. An extensive overview is given below for comparison of backstepping and SMC on the basis of simulation results.

5. Comparison of Backstepping and SMC Controller

Figures 14 and 15 show the comparison between SMC and backstepping controller for pitch and yaw angle of TRMS. It is observed that backstepping controller shows good results compared to SMC in terms of handling oscillation and chattering. By using backstepping controller, the settling time for the pitch and yaw angle is less as compared to SMC as shown in Figures 14 and 15.

Figure 14: Pitch angle (rad) for SMC and backstepping.

Figure 15: Yaw angle (rad) for SMC and backstepping.

6. Conclusion

This work outlines the design analysis of robust controller techniques by implementing them in TRMS, which is a nonlinear system. SMC and backstepping are implemented and analyzed for handling the oscillation and chattering in pitch and yaw angles of TRMS. It is observed that backstepping shows better performance in terms of less settling time and handling perturbation as compared to SMC, owing to the recursive structure for controller design. This philosophy is the core idea that has been followed for developing robust controller. The controller was implemented in the Simulink environment where the state space model of the controller was engaged with system to achieve the desired result. Implementing the sliding mode control via backstepping control can be considered as a recommended future work, since parametric uncertainty and external disturbance can be mitigated within a single model, which can stabilize the TRMS in a more robust way.

System Parameters

: | Moment of inertia of vertical rotor ( kgm²)
: | Moment of inertia of horizontal rotor ( kgm²)
: | Friction momentum function parameter ( Nms²/rad)
: | Gyroscopic momentum parameter (0.05 s/rad)
: | Motor denominator parameter
: | Cross reaction momentum gain
: | Friction momentum function parameter ( Nms²/rad)
: | Friction momentum function parameter (Nm·s/rad)
: | Static characteristic parameter (0.0135)
: | Static characteristic parameter (0.0924)
: | Static characteristic parameter (0.02)
: | Motor 1 denominator parameter
: | Gravity momentum (0.32 Nm)
: | Motor 1 gain (1.1)
: | Motor 2 gain (0.8)
: | Motor 1 denominator parameter (1.1).

Competing Interests

The authors declare that they have no competing interests.

References

M. Twin Rotor, System Manual, Feedback Instruments, Crowborough, UK, 2002.

M. Chen, S. S. Ge, and B. V. E. How, “Robust adaptive neural network control for a class of uncertain MIMO nonlinear systems with input nonlinearities,” IEEE Transactions on Neural Networks, vol. 21, no. 5, pp. 796–812, 2010.

A. Boulkroune, M. M'Saad, and H. Chekireb, “Design of a fuzzy adaptive controller for MIMO nonlinear time-delay systems with unknown actuator nonlinearities and unknown control direction,” Information Sciences, vol. 180, no. 24, pp. 5041–5059, 2010.

A. Rahideh, A. H. Bajodah, and M. H. Shaheed, “Real time adaptive nonlinear model inversion control of a twin rotor MIMO system using neural networks,” Engineering Applications of Artificial Intelligence, vol. 25, no. 6, pp. 1289–1297, 2012.

A. Rahideh, M. H. Saheed, A. Safavi, and J. C. Huijberts, “Model predictive control of a twin rotor MIMO system,” in Proceedings of the 21st International Conference on Methods and Models in Automation and Robotics, p. 2831, Międzyzdroje, Poland, 2006.

Y. Li, S. Tong, and T. Li, “Observer-based adaptive fuzzy tracking control of MIMO stochastic nonlinear systems with unknown control directions and unknown dead zones,” IEEE Transactions on Fuzzy Systems, vol. 23, no. 4, pp. 1228–1241, 2015.

Y. M. Li, S. C. Tong, and T. S. Li, “Hybrid fuzzy adaptive output feedback control design for uncertain MIMO nonlinear systems with time-varying delays and input saturation,” IEEE Transactions on Fuzzy Systems, 2015.

J.-G. Juang, M.-T. Huang, and W.-K. Liu, “PID control using presearched genetic algorithms for a MIMO system,” IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, vol. 38, no. 5, pp. 716–727, 2008.

S. Tong and H.-X. Li, “Fuzzy adaptive sliding-mode control for MIMO nonlinear systems,” IEEE Transactions on Fuzzy Systems, vol. 11, no. 3, pp. 354–360, 2003.

S. C. Tong, Y. M. Li, and P. Shi, “Observer-based adaptive fuzzy backstepping output feedback control of uncertain MIMO pure-feedback nonlinear systems,” IEEE Transactions on Fuzzy Systems, vol. 20, no. 4, pp. 771–785, 2012.

S.-C. Tong, Y.-M. Li, G. Feng, and T.-S. Li, “Observer-based adaptive fuzzy backstepping dynamic surface control for a class of MIMO nonlinear systems,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 41, no. 4, pp. 1124–1135, 2011.

J.-G. Juang, R.-W. Lin, and W.-K. Liu, “Comparison of classical control and intelligent control for a MIMO system,” Applied Mathematics and Computation, vol. 205, no. 2, pp. 778–791, 2008.

J.-G. Juang, W.-K. Liu, and R.-W. Lin, “A hybrid intelligent controller for a twin rotor MIMO system and its hardware implementation,” ISA Transactions, vol. 50, no. 4, pp. 609–619, 2011.

C.-W. Tao, J.-S. Taur, Y.-H. Chang, and C.-W. Chang, “A novel fuzzy-sliding and fuzzy-integral-sliding controller for the twin-rotor multi-input–multi-output system,” IEEE Transactions on Fuzzy Systems, vol. 18, no. 5, pp. 893–905, 2010.

C. W. Tao, J. S. Taur, and Y. C. Chen, “Design of a parallel distributed fuzzy LQR controller for the twin rotor multi-input multi-output system,” Fuzzy Sets and Systems, vol. 161, no. 15, pp. 2081–2103, 2010.

M. Sacki, J. Imura, and Y. Wada, “Flight control design of twin rotorhelicopter model by 2 step exact linearization,” in Proceedings of the IEEE International Conference on Control Applications, August 1999.

G. Mustafa and N. Iqbal, “Controller design for a twin rotor helicopter model via exact state feedback linearization,” in Proceedings of the 8th International Multitopic Conference (INMIC '04), pp. 706–711, Lahore, Pakistan, December 2004.

C. Yang, S. S. Ge, C. Xiang, T. Chai, and T. H. Lee, “Output feedback NN control for two classes of discrete-time systems with unknown control directions in a unified approach,” IEEE Transactions on Neural Networks, vol. 19, no. 11, pp. 1873–1886, 2008.

M. Chen, C.-S. Jiang, and Q.-X. Wu, “Robust adaptive control of time delay uncertain systems with FLS,” International Journal of Innovative Computing, Information and Control, vol. 4, no. 8, pp. 1995–2004, 2008.

M. Chen, S. S. Ge, and B. Ren, “Robust attitude control of helicopters with actuator dynamics using neural networks,” IET Control Theory & Applications, vol. 4, no. 12, pp. 2837–2854, 2010.

Y. Li, C. Yang, S. S. Ge, and T. H. Lee, “Adaptive output feedback NN control of a class of discrete-time MIMO nonlinear systems with unknown control directions,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 41, no. 2, pp. 507–517, 2011.

Z. J. Li and C. Yang, “Neural-adaptive output feedback control of a class of transportation vehicles based on wheeled inverted pendulum models,” IEEE Transactions on Control Systems Technology, vol. 20, no. 6, pp. 1583–1591, 2012.

H. A. Hashim and M. A. Abido, “Fuzzy controller design using evolutionary techniques for twin rotor MIMO system: a comparative study,” Computational Intelligence and Neuroscience, vol. 2015, Article ID 704301, 11 pages, 2015.

P. Wen and T.-W. Lu, “Decoupling control of a twin rotor MIMO system using robust deadbeat control technique,” IET Control Theory & Applications, vol. 2, no. 11, pp. 999–1007, 2008.

S. Mondal and C. Mahanta, “Second order sliding mode controller for twin rotor MIMO system,” in Proceedings of the Annual IEEE India Conference: Engineering Sustainable Solutions (INDICON '11), pp. 1–5, December 2011.

M. Krstic, I. Kanellakopoulos, and P. V. Kokotovic, Nonlinear and Adaptive Control Design, John Wiley & Sons, New York, NY, USA, 1995.

A. A. Ezzabi, K. C. Cheok, and F. A. Alazabi, “A nonlinear backstepping control design for ball and beam system,” in Proceedings of the IEEE 56th International Midwest Symposium on Circuits and Systems (MWSCAS '13), pp. 1318–1321, IEEE, Columbus, Ohio, USA, August 2013.

Y. Yang, G. Feng, and J. Ren, “A combined backstepping and small-gain approach to robust adaptive fuzzy control for strict-feedback nonlinear systems,” IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, vol. 34, no. 3, pp. 406–420, 2004.