Abstract— Force control is one of the most challenging controls in robot manipulators. In this control scheme, the system dynamic does not only depend on actuator dynamic but also environment. Non-adaptive controller is not sufficient to efficiently regulate the plant when the environment such as manipulated object, contact point, etc. is changed. Adaptive controller is able to deal with this problem; however, its response in the learning (adaptation) period is often unsatisfactory. In some cases, this undesired response may damage the environment and actuator. To overcome this problem, our proposed technique applies Particle Swarm Optimization (PSO) to achieve the desired response. Hybrid structure is adopted to reduce the problem of unlearned response. The controller structure is based on the concept of impedance control which the controller regulates the system to act as the pre-specified impedance dynamics. Simulation results show that our proposed technique is applicable and superior to the conventional learning system.

I. INTRODUCTION N many industrial applications, force control is widely used for regulating several manufacturing processes. However, controlling in this control scheme is not easy

because the dynamic of the controlled system depends on both actuator and environment which are normally fluctuated. By this reason, it is difficult to identify the plant before designing the controller. Thus, only non-adaptive controller is not enough to regulate the plant. At present, there are two important force control schemes, i.e., direct force and impedance controls. Direct force control directly regulates the force output according to the force command signal. In this control scheme, only force sensor is needed for control. Impedance control is a control scheme which regulates the actuator dynamics to act as the specified mechanical dynamics. Instead of direct controlling force, this control scheme controls the mechanical dynamics, i.e., mass, spring and damper coefficients of the system. Both position and force sensors are needed for this control scheme. In the previous research works, many researchers adopted the impedance controls to force control applications. Fateh and Alavi [1] applied the impedance control to control an active suspension system. The proposed impedance control can regulate the dynamics behavior of suspension. As shown in their results, the specified mechanical impedance can reduce the damage caused by the

Sarucha Yanyong is with the faculty of Engineering, King Mongkut’s

Institute of Technology Ladkrabang, Bangkok, Thailand 10520. (e-mail: sereneqd@gmail.com).

Somyot Kaitwanidvilai is with the faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand 10520. (e-mail: drsomyotk@gmail.com).

contact force between robot and environment. As their conclusion, damping parameters can provide a damped response to avoid the damage. When the environment is changed, the dynamic of force control system is also changed; this sometimes causes undesired response. To efficiently control the system in this situation, adaptive control can perform an adaptation system which the controller parameters are adapted for regulating the plant. Several researchers applied adaptive control techniques in many systems including force control system. Moreover, some researchers [2-4] adopted neuro-fuzzy and fuzzy control systems to regulate the plant. Fuzzy control is a controller which can incorporate human knowledge to the control system via linguistic structure. This control scheme is a class of nonlinear controller to deal with the nonlinear plant. Neural network control is a nonlinear control scheme which its learning algorithm is based on the concept of gradient method. By combining two above mentioned schemes, a new class of controller, Neuro-Fuzzy control, is proposed. This type of controller gains the advantages of nonlinear adaptation and human knowledge incorporation. Several researchers adopted the neuro-fuzzy systems to control the plant [2-4]. However, the problem of adaption process in both neural network and neuro-fuzzy control systems is local minima problem which is a normal problem when applying the gradient method. To overcome this problem, global search optimization algorithms such as genetic algorithms, particle swarm optimization, etc. can be applied. Particle Swarm Optimization [5] is a computational technique that optimizes a problem by trial and error method; this technique is based on the concept of swarm movement. Based on the searching algorithm in PSO, the local minima problem can be avoided. Recently, several researchers adopted the PSO to design the controllers. Piyapong [6] used the PSO for evaluating the parameters of fuzzy controller. Kao et al.[7] applied the PSO for finding the parameters of PID controller. Although the PSO is a good technique to deal with the local minima problem; however, this technique is rarely applied to the real system due to their undesired response in learning period. In addition, time interval needed for learning period is very long. To overcome this problem, Somyot et al.[8] proposed a hybrid adaptive neuro-fuzzy model reference to control the contact and internal forces in pneumatic actuating systems. Multimode controller was a main structure used in their paper to control the plant. Their proposed technique can avoid the problem of noncontact situation during learning period. A hybrid genetic based controller [9] was presented to control the servo system. In this paper, a hybrid controller

Hybrid Adaptive Impedance Force Controller using Bang-Bang and Particle Swarm Optimization Approaches

Sarucha YanYong and Somyot Kaitwanidvilai

I

978-1-4577-2138-0/11/$26.00 © 2011 IEEE 2694

Proceedings of the 2011 IEEEInternational Conference on Robotics and Biomimetics

December 7-11, 2011, Phuket, Thailand

was successfully applied to control the plant. Undesired response during the learning period is reduced. In this paper, a hybrid adaptive particle swarm optimization is proposed to tune the impedance parameters in pre-specified impedance controller. Bang-bang controller is included in the proposed hybrid structure and is activated when the serious unwanted response is occurred. Bang-bang is an on-off control scheme which can quickly drive the actuator response away from the undesired situation; however, the performance of this controller is not satisfactory. In the proposed force control system, Bang-bang can prevent the damage from contact force between manipulator and environment. Based on this multi-mode structure, the advantages of learning ability, acceptable response at learning period, etc. can be achieved. Thus, the proposed technique can be used in online learning and can be applied to the real system. As seen in the simulation results, our proposed technique is superior to the PSO based controller. The proposed controller can perform acceptable response even in the learning period which undesired response is normally occurred. Clearly, the proposed control scheme can reduce the main problem of applying online learning controller in real system.

II. Hybrid PSO based Impedance Control Fig. 1 shows the diagram of the proposed control structure. The followings describe the details of the proposed hybrid controller.

A. Multimode Controller Hybrid PSO is a multimode-switching controller which two controllers are connected and worked as hybrid fashion. The selection of the activating controller is based on the force error as following. IF ( |error| > Em) THEN Bang-bang controller ELSE PSO based impedance controller. where Em is a specified error threshold for switching mode.

B. Bang-bang controller Bang-bang controller (on-off controller) is a simple feedback controller which can switch between two states. The performance of this controller is poor but it can drive the response apart from the unwanted situation quickly. Bang-bang controller algorithm can be written as: if error > UU , u = V if error < UL , u = -V where u is control output; V is maximum value of control

output; UL and UU are lower and upper bounds, respectively.

C. Impedance Control using Particle Swarm Optimization (PSO) approach

1) Impedance Control In this paper, an impedance control is adopted to control the dynamic behavior of a force control system. The following equation shows the concept of impedance control.

F mx bx k= + + (1) where F is control force; m is the desired mass; b is the desired damper coefficient; k is the desired stiffness coefficient and x is the position or displacement. In this paper, the PSO is adopted to find the optimal impedance parameters of the impedance control for minimizing the force error. Block diagram of impedance control using Particle Swarm Optimization approach is shown in Fig. 2. 2) Fitness function

Fitness is a measure of goodness of each particle. In this paper, the fitness function is

21 20

1TFitness

w e dt w M=

+ (2)

where w1 and w2 are weight factors. e is the force error which is the difference between the measured force and command. M is the maximum overshoot. T is the specified period. In the proposed learning system, the objective function which attempted to be maximized is fitness function.

3) Particle Swarm Optimization Algorithm PSO is an optimization algorithm which particles fly

around the solution space for searching the optimal solution. Each particle acts as a candidate of optimal solution which the next particles will be generated by the concept of swarm movement and the fitness value. In this paper, the PSO technique is used for searching the optimal impedance parameters, i.e., m, b and k for maximizing the fitness value. Brief description of the PSO is shown in Fig. 3. More details about the PSO can be seen in [5-6].

Fig. 1. Proposed Hybrid PSO Control Scheme

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II. SIMULATION RESULTS In this paper, a Mass-Spring-Damper System is adopted as the plant of force control system. The dynamic model of this system can be written as:

KBsMsKsG f ++

= 21)( (3)

where M is mass; B is damper coefficient; K is spring constant; K1 is a constant gain. In this paper, the parameters of environment have been selected as the following equation.

)8(20)( 2 +

=s

sG f (4)

The desired response is selected as the reference model as shown in (5).

1( )(0.2 1)rG s

s=

+ (5)

Fig. 4 shows the results of learning by performing two types of controllers, i.e., PSO based controller and hybrid PSO based controller. The parameters of the PSO are selected as follows: population size = 24, velocity = 2, minimum and maximum inertia weights are 0.9 and 0.6, respectively; acceleration coefficient = 2.1, m, b, k ∈ [0.001, 5] and maximum iteration = 20. In the hybrid PSO based controller, UU and UL are selected as 0.4 N and -0.4 N, respectively. As seen in the results, at iteration#1 (learning period), the response from the proposed Hybrid PSO based controller is better than that of the response from the PSO based controller. Low oscillation and small maximum overshoot can be achieved by the proposed control scheme. When running the PSO for 11 iterations, the optimal parameters were found. Fig. 5 shows the learned responses of both control schemes. As seen in this figure, both the PSO and the hybrid PSO can perform satisfied responses.

Fig. 3. Particle Swarm Optimization Algorithm.

Fig. 2. Tuning impedance parameters by Particle Swarm Optimization.

0 10 20 30 40 50-10

-5

0

5

10

15Output Response and Desired Response

Time(s)

For

ce(N

)

PSO Output ResponseHybridPSO Output ResponseDesired Response

Switch to Bang-Bang

Fig. 4. Output responses of the controllers, the PSO and the Hybrid PSO controllers (during learning period at iteration#1).

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To investigate the adaptation performance of the proposed Hybrid PSO, a changing of environment was performed by adjusting the constant gain K1 from 20 to 50. Thus, the transfer function of the plant was changed to (6).

)8(50)( 2 +

=s

sG f (6)

When Environment was changed, both responses from the PSO and Hybrid PSO based controllers had overshoots. As shown in Fig. 6, the overshoot from the PSO based controller is higher than that obtained from the Hybrid PSO controller.

III. CONCLUSION In this paper, the PSO and the Hybrid PSO based controllers were applied to the impedance based force control. Comparative study is performed via the simulations. As seen in the results, at the learning period, the proposed

controller performs a better response compared to the PSO based impedance controller. This makes the online learning system can be implemented in practice. In addition, when the environment was changed, the proposed hybrid control technique can perform the better response in terms of lower overshoot/undershoot.

REFERENCES [1] Mohammad Mehdi Fateh and Seyed Sina Alavi, “Impedance control

of an active suspension system,” Mechatronics., Vol. 19, No. 1, pp. 134-140, 2009.

[2] Srinivasan Alavandar and M.J.Nigam, “New Hybrid adaptive neuro-fuzzy algorithms for manipulators control with uncertainties - Comparative study,” ISA Transactions., Vol. 48, No. 4, pp. 497-502, 2009.

[3] Hamit Erdem, “Application of Neuro-Fuzzy Controller for Sumo Robot Control,” Expert System-with Applications., Vol. 38, No. 8, pp. 9752-9760, 2011.

[4] J. Sargolzaei and A. Kianifar, “Neuro-fuzzy modeling tools for estimation of torque in Savonius rotor wind turbine,” Advances in Engineering Software., Vol. 41, No. 4, pp. 619-626, 2010.

[5] Kennedy J and Eberhart R, “Particle swarm optimization,” in IEEE Int. Conf. Neural Networks., Vol. 4, pp. 1942-1948, Nov. 27-Dec. 1, 1995.

[6] S. Kaitwanidvilai and P. Olranthichachat, “Robust loop shaping-fuzzy gain scheduling control of a servo-pneumatic system using particle swarm optimization approach,” Mechatronics., Vol. 21, No. 1, pp. 11-21, 2011.

[7] Chih-Cheng Kao , Chin-Wen Chuang and Rong-Fong Fung, “The self-tuning PID control in a slider-crack mechanism system by applying particle swarm optimization approach,” Mechatronics., Vol. 16, No. 8, pp. 513-522, 2006.

[8] Kaitwanidvilai S and Parnichkun M, “Force control in a pneumatic system using hybrid adaptive neuro-fuzzy model reference control,” Mechatronics., Vol. 15, No. 1, pp. 23-41, 2005.

[9] Kaitwanidvilai S, “Online Evolutionary Control using A Hybrid Genetic Based Controller,” in Proc. of the 2004 IEEE Conf. on Robotics, Automation and Mechatronics., Vol. 1, pp. 461-466, Dec. 1-3, 2004.

0 10 20 30 40 500

0.5

1

1.5

2

2.5

3Output Response and Desired Response

Time(s)

For

ce(N

)

PSO Output ResponseHybridPSO Output ResponseDesired Response

Fig. 5. Output Responses at learned periods of the PSO and the Hybrid PSOcontrollers.

0 10 20 30 40 500

0.5

1

1.5

2

2.5

3Output Response and Desired Response

Time(s)

For

ce(N

)

PSO Output ResponseHybridPSO Output ResponseDesired Response

HybridPSO Maximum error = 0.75 N

Spring constant is changing

PSO Maximum error = 1.1 N

Fig. 6 Output Response from the PSO and Hybrid PSO based controllers when the environment is changed.

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