Control of 3D Printed Ambidextrous Robot Hand Actuated by Pneumatic Artificial Muscles

Mashood MukhtarDepartment of Electronic and Computer Engineering

Brunel University, Kingston Lane, UxbridgeLondon, UK

Mashood.Mukhtar@brunel.ac.uk

Emre AkyürekDepartment of Electronic and Computer Engineering

Brunel University, Kingston Lane, UxbridgeLondon, UK

Tatiana KalganovaDepartment of Electronic and Computer Engineering

Brunel University, Kingston Lane, UxbridgeLondon, UK

Nicolas LesneDepartment of System Engineering

ESIEE ParisNoisy-le-Grand Cedex, France

Abstract—Production of robotic hands have significantly increased in recent years due to their high demand in industry and wide scope in number of applications such as tele-operation, mobile robotics, industrial robots, biomedical robotics etc. Following this trend, there have been many researches done on the control of such robotic hands. Since human like clever manipulating, grasping, lifting and sense of different objects are desirable for researchers and crucial in determining the overall performance of any robotic hand, researchers have proposed different methods of controlling such devices. In this paper, we discussed the control methods applied on systems actuated by pneumatic muscles. We tested three different controllers and verified the results on our uniquely designed ambidextrous robotic hand structure. Performances of all three control methods namely proportional–integral-derivative control (PID), Bang-bang control and Backstepping control has been compared and best controller is proposed. For the very first time, we have validated the possibility of controlling multifinger ambidextrous robot hand by using Backstepping Control. The five finger ambidextrous robot hand offers total of 13 degrees of freedom (DOFs) and it can bend its fingers in both ways left side and right side offering full ambidextrous functionality by using only 18 pneumatic artificial muscles (PAMs). Pneumatic systems are being widely used in many domestic, industrial and robotic applications due to its advantages such as structural flexibility, simplicity, reliability, safety and elasticity.

Keywords—Robot Hand; Ambidextrous Design; Pneumatic Muscles; Grasping Algorithms; Backstepping control; PID Control; Bang-bang Control; Control Methods; Pneumatic Systems

I. INTRODUCTION

The world is overwhelmed with incredible advances in engineering. This advancement has resulted in production of many robotics that are currently being used across various industries [1] [2]. The use of robotics not only allows more productivity and safety at workplace but also save time and money. The history of robot development goes back to the

ancient world [3]. It all started from a basic idea and reached today at a point where most complicated and risky tasks are being handled by robots [4]. This rapid growth resulted is production of complicated robots [5] and intelligent control algorithms [6]. In the past considerable research has been done and as a result many control algorithms were proposed [7], [8],[9], [10], [11], [12] and [13]. The first theoretical basis of the pneumatic system control was made by Prof. J. L. Shearer in 1956 [14]. The pneumatic systems are widely used in robotics [15] [16], food packaging [17], construction [18], biomechanics industry [19] and manufacturing applications [20]. In this paper we will first present the detail classification of control systems and then review the related work that has been done in the past. In particular, our focus would be on control algorithms implemented on pneumatic systems using PID controller, Bang-bang controller and Backstepping controller. We will further test these controllers on an ambidextrous robot hand and compare the results to find the best option available to drive such devices.

Although number of control schemes are found in the literature [21], [22] such as adaptive control [23], [24], direct continuous-time adaptive control [25], neurofuzzy PID control [26] hierarchical control [27], [28], slave-side control [29], intelligent controls [30], neural networks [31] [32], just-in-time control [33] ,Bayesian probability [34], equilibrium-point control [35], fuzzy logic control [36], [37], [38], [39], [40], machine learning [41], [42], evolutionary computation [43] [44] and genetic algorithms [45] [46], nonlinear optimal predictive control [47],optimal control [48], [49] [50], stochastic control [51], variable structure control [52], chattering-free robust variable structure control [53] and energy shaping control [54], gain scheduling model-based controller [55], sliding mode control [56], proxy sliding mode control [57], neuro-fuzzy/genetic control [39] [26] but there are five types of control systems (Fig.1) mainly used on

pneumatic muscles. These are feedback control system, feed-forward control System, non-Linear control system, artificial Intelligence based control system and hybrid Systems.

Fig.

1. Classification of control algorithm implemented on pneumatic systems.

In this research, we investigated the feedback control system and non-linear control systems. Concept of feedback system was first introduced by Greeks around 2000 years ago[59]. Feedback is a control system in which an output is used as a feedback to adjust the performance of a system to meet expected output. Control systems with at least one non-linearity present in the system are called nonlinear control systems. The purpose of designing such system is to stabilise the system at certain target. In order to reach a desired output value, output of a processing unit (system to be controlled) is compared with the desired target and then feedback is provided to the processing unit to make necessary changes to reach closer to desire output.

Grasping abilities of the ambidextrous hand has been investigated using tactile sensors. Uses of tactical sensors are quiet common and it has been implemented on robotic hands driven by motors several time as it can be seen in [16], [60],[61], [62] and [63] or robot hands driven by PAMs [64], [65] and [66]. The automation of the ambidextrous robot hand and its interaction with objects are also augmented by implementing vision sensors on each side of the palm. Similar systems have already been developed in the past. For instance, the two-fingered robot hand discussed in [67] receives vision feedback from an omnidirectional camera that provides a visual hull of the object and allows the fingers to automatically adapt to its shape. The three-fingered robot hand developed by Ishikawa Watanabe Laboratory [61] is connected to a visual feedback at a rate of 1 KHz. Combined with its high-speed motorized system that allows a joint to rotate by 180 degrees in 0.1 seconds, it allows the hand to interact dynamically with its environment, such as catching falling objects. A laser

displacement sensor is also used for the two-fingered robot hand introduced in [62]. It measures the vertical slip displacement of the grasped object and allows the hand to adjust its grasp. In our research, the aim of the vision sensor is to detect objects close to the palms and to automatically trigger grasping algorithms. Once objects are detected by one of the vision sensors, grasping features of the ambidextrous robot hand are investigated using three different algorithms, which are proportional-integrative-derivative (PID), bang-bang and Backstepping controllers. Despite the nonlinear behavior of PAMs actuators [68], [69] previous researches indicates that these three algorithms are suitable to control pneumatic systems.

This research aims to validate the possibility of controlling a uniquely designed ambidextrous robot hand using PID controller, Bang-bang controller and Backstepping controller. The ambidextrous robot hand is a robotic device for which the specificity is to imitate either the movements of a right hand or a left hand.

(a) (b)

Fig.2. Ambidextrous Robot Hand, where (a) is the ambidextrous mode and (b) is the left mode.

As it can be seen in Fig.2 (a), its fingers can bend in one way or another to include the mechanical behavior of two opposite hands in a single device. The Ambidextrous Robot Hand has a total of 13 degrees of freedom (DOFs) and is actuated by 18 pneumatic artificial muscles (PAMs) [58].

II. RELATED WORK

A. PID Controller PID stands for Proportional, integral and derivative. It is by far the most commonly used controller in industry due to its simplicity and robust performance under various operating conditions. PID controllers are indeed widely used in the robotics area. They can drive either motorized systems, such as the ACT hand [70] and the Shadow hand [16]. In [71]] and [72], PID controllers were used to regulate both the position of the system and the pressure of its muscles. Similar concept of loops was applied to robot hands in [73], [74]. In [75] a control strategy using modified PID controller and pusher mechanism is proposed to achieve position and time accuracy required for the task. PID is also sometime combined with artificial intelligent controller such as neural networks to get the best results as can be seen in [76] or [77] or fuzzy logic in [78]. An adaptive fuzzy PD controller and adaptability of such controller with pneumatic servo system was presented in [79]. In [80], a non-linear PID controller and neural network is used to improve the control of PAM suitable for plants with nonlinearity uncertainties and disturbances. Particle swarm optimization (PSO) algorithm is used in [81] to self-tune PID controller of a joint introduced. In [82], [83], [84] and [85] PID controls implemented on a bipedal walking robot called “Lucy. Another approach is performed in [86], where the positioning of PAMs is driven by PID loops combined to nonlinear coefficients. In [87] authors investigated the possibility of applying a hybrid feed-forward inverse nonlinear autoregressive with exogenous input (NARX) fuzzy model-PID controller to a nonlinear pneumatic artificial muscle (PAM) robot arm to improve its joint angle position output performance.

B. Bang-bang Controller Bang-bang controllers are a nonlinear style of feedback controller also known as on-off controller or hysteresis controller. It is used to switch between two states abruptly. Bang-bang controllers are widely used in systems that accept binary inputs. System makes decision to turn controller on or off based on threshold and target values [88]. Application to regular and Bang-bang control is discussed in great detail in [89]. Bang-bang controller is not popular in robotics because its shooting functions are not smooth [90] and need regularisation [91]. Fuzzy logic [92], [93] and PID are usually combined with bang-bang controller to add flexibility and determine switching time of Bang-bang inputs [58]. Bram Vanderborght et al. in [82], [94], [95], [84], [96] and [83] controlled a bipedal robot actuated by pleated pneumatic artificial muscles using bang-bang pressure controller. Stephen M Cain et al. in [97], studied locomotor adaptation to powered ankle-foot orthoses using two different orthosis control methods. The pressure in the pneumatic muscles was controlled by footswitch control and proportional myo-electric control. In footswitch control, bang-bang controller is used to regulate air pressure in pneumatic muscle. Dongjun Shin et al. in [98], proposed the concept of hybrid actuation for human friendly robot systems by using distributed macro-Mini

control approach [99]. The hybrid actuation controller employ modified bang-bang controller to adjust the flow direction of pressure regulator. Zhang, Jia-Fan et al. in [100], presented a novel pneumatic muscle based actuator for wearable elbow exoskeleton. Hybrid fuzzy controller composed of bang-bang controller and fuzzy controller is used for torque control.

H. X. Zhang et al. in [101], presented the improved full pneumatic climbing robot. Effectiveness of two approaches was tested to check the system stiffness and control quality. Bang-bang controller is used to control the cylinder movement and eliminate oscillation. Juan Gerardo Castillo Alva et al. in [102], discussed the development of fatigue testing machine using a pneumatic artificial muscle. Using learning control techniques, a special control system is developed for the machine. The proposed methodology consists on implementing a bang-bang controller to control the solenoid valves.

C. Backstepping Controller Backstepping is a control technique originally intruded by Peter V. Kokotovic in 1990 to offer stabilise control with a recursive structure based on derivative control.and it was limited to nonlinear dynamical systems. Mohamed et al. presented a synthesis of a nonlinear controller to an electro pneumatic system in [103]. Nonlinear Backstepping control and nonlinear sliding mode control laws were applied to the system under consideration. First, the nonlinear model of the electro pneumatic system was presented. It was transformed to be a nonlinear affine model and a coordinate transformation was then made possible by the implementation of the nonlinear controller. Two kinds of nonlinear control laws were developed to track the desired position and desired pressure. Experimental results were also presented and discussed. P. Carbonell et al. compared two techniques namely sliding mode control (SMC) and Backstepping control (BSC) in [104] and found out BSC is somewhere better than SMC in controlling a device. He further applied BSC coupled with fuzzy logic in [105]. In [106], a paralleled robot project is discussed which is controlled by BSC.

Since literature review revealed no multifinger robot hand (actuated by PAMs) is ever driven using BSC, research presented in this paper validates the possibility of driving multifinger robot hand using BSC. We used an ambidextrous robot hand to test the Backstepping controller that goes even a step further to prove the originality of research.

III. IMPLEMENTATION OF A PID CONTROLLER As the name suggests, PID controller is a combination of three different controller’s (Fig. 3) proportional, integral, derivative. Proportional controller serves as a heart of a control system; it provides corrective force proportional to error present. Although proportional control is useful for improving the response of stable systems and considered a

building block of many applications but it comes with a steady-state error problem which is eliminated by adding integral control. Integral control restores the force that is proportional to the sum of all past errors, multiplied by time. On one hand integral controller solves a steady-state error problem created by proportional controller but on the other hand integral feedback makes system overshoot.

Equation of proportional control:

Pout=K p×e<t >¿ ¿Pout = System output K p = Proportional gain constante<t>¿ ¿ = Error E at specific time E=SP( ideal−output )−PV (measurable−output ) Equation of integral control:

I outI=K i×∫

0

t

e<t>¿dt

I out = System outputK I = Integral gain constant = Sum of the instantaneous error over time

To overcome overshooting problem, derivative controller is used. It slows the controller variable just before it reaches its destination. Derivatives controller is always used in combination with other controller and has no influence on the accuracy of a system. All these three control have their strengths and weakness and when combine together it offers the maximum strength whilst minimizing weakness.

Equation of derivative control:

Dout=Kd׿¿

Dout = System output Kd = Derivative gain constant = Rate of change (error)

When combine all three controller, the equation becomes:

Output PID=KP×e<t >+ K i×∫0

t

e<t>¿dt+K d׿¿

Output PID = output from PID controllerPout = Proportional control gainI out = Integral control gainDout = Derivative control gain

e<t>¿ ¿ = Error E at specific time E=SP( ideal−output )−PV (measurable−output ) = Sum of the instantaneous error over time

= Rate of change (error)

Fig.3. Block diagram of PID Controller

PID algorithms can be divided into three main categories: Serial, ideal and parallel.

Ideal PID Algorithm:

Parallel PID Algorithm:

Serial PID Algorithm:

PID Control loops were implemented on an ambidextrous robot hand using the parallel form of a PID controller, for which the equation is as follows:

u ( t )=K p e ( t )+K i∫0

t

e (τ ) dτ+K dddt

e (t) (2)

Snapshots of experiments can be seen in Fig.4. The force targets are fixed at 13N in Fig.4 (a) and at 15N in Fig.4 (b). It is observed that medial and distal phalanges flex when force applied remain below 13N and extends above 15N. Therefore, the system reaches a stable state when the force feedback stands between these two limit values and provide enough force to hold light objects. In the snapshots Fig. 4 (c) and Fig.4 (d), a single force target is fixed at 0.05 N and the finger’s prototype goes backward when it touches a piece of paper.

∫0

t

e<t >¿dt

¿¿

∫0

t

e<t >¿dt

¿¿

Output=Kc[ e< t >+ 1I ∫e<t >d<t >+ D de<t > ¿

dt] ¿

Output=Kp[ e<t >]+ 1I ∫e<t>d< t >+D de<t> ¿

dt¿

Output=Kc[ e< t >+ 1I ∫e<t >d<t>][1+D d

dt]

(a) (b)

(c)

(d)

Fig.4. Interactions of light weight objects and an early design of an ambidextrous finger is shown in the figure. According to the force put as target, the prototype provides enough force to maintain pieces of metal in (a) and (b) whereas the medial and distal phalanges go backward when they touch a piece of paper in (c) and (d).

Output u (t) should be calculated exactly in the same way as described in equation (1) but this is not the case due to asymmetrical tendon routing of ambidextrous robot hand[107]. Mechanical specifications are taken into consideration before calculating the u (t). It is divided into three different outputs for the three PAMs driving each finger. These three outputs areupl ( t ), umr (t ) anduml ( t ), respectively attributed to the proximal left, medial right and medial left PAMs. The same notations are used for the gain constants. The adapted PID equation is defined as:

[upl ( t )umr (t )uml ( t )]=[ K p pl

K i plKd

K pmrK imr

Kd

K pmlK iml

Kd] [

e ( t )

∫0

t

e (τ ) dτ

ddt

e( t) ] (2)

To imitate the behaviour of human finger correctly, some of the pneumatic artificial muscles must contract slower than others. This prevents having medial and distal phalanges totally close when the proximal phalange is bending. The proportional constant gains are consequently defined as:

[ K pml

K pmr]=[K ppl

/3K i pl

/3 ]=[−K pml

−K irl] (2)

When object interact on the left side of the hand, equation can be written as follows:

[ K pmr

K imr]=[ 0.4 X K pml

0.4 X−K iml]=[−K pmr

−K imr] (2)

Same ratios are applied to the integrative gain constants when object interact on right side of the hand.

Using equation (2), PID control loops with identical gain constants are sent to the four fingers with a target of 1 N and an error margin of 0.05 N, whereas the thumb is assigned to a target of 12 N with an error margin of 0.5 N. A can of soft drink is brought close to the hand and data is collected every 0.05 sec. The experiment result is illustrated in Fig.5 and data collected from the left hand mode during experiment is shown in Fig.6. If we notice Fig.6, we will realise that data is only collected from four of the five figured ambidextrous robot hand and graph started plotting from 0.1 sec. This is due to the fact that the thumb stabilises at 12.23N far higher than other fingers and no interaction was reported before 0.1 sec.

Fig. 5. The Ambidextrous Robot Hand holding a can (both right and left) with a grasping movement implemented with PID controllers.

0.1 0.15 0.2 0.25 0.30.75

0.80.85

0.90.95

11.05

1.1

ForefingerMiddle fingerRing fingerLittle finger

Time (sec)

Forc

e (N

)

Fig. 6. Graph shows force against time of the four fingers while grasping a drink can with PID controllers.

From Fig.6, it can be seen that grasping of a can of soft drink began at 0.15 approximately and it became more stable after 0.2 sec. An overshoot has occurred on all fingers but stayed in limit of 0.05N where system has automatically adjusted at the next collection. These small overshoots occurred because different parts of the fingers get into contact with the object before the object actually gets into contact with the force sensor. This results bending of fingers slower when phalanges touches the object. Since the can of soft drink does not deform itself, it can be deduced that the grasping control is both fast and accurate when the Ambidextrous Hand is driven by PID loops.

IV. IMPLEMENTATION OF A BANG-BANG CONTROLLER

The bang-bang controller allows all fingers to grasp the object without taking any temporal parameters into account. Execution of algorithm automatically stops when the target set against force is achieved. The controller also looks after any overshooting issue if it may arise. To compensate the absence of backward control, a further condition is implemented in addition to initial requirements. Since the force applied by the four fingers is controlled with less accuracy than with PID loops, the thumb must offset the possible excess of force to balance the grasping of the object. Therefore, a balancing equation is defined as:

F tmin=W o+∑f =1

4

F f (2)

Where F tminis the minimum force applied by the thumb, F f is the force applied by each of the four other fingers. W o is an approximate weight of the object to grabbed. In the case of light weight objects, since weight is not very much of importance but should be defined anything more than 25N. Equation (5) is only suitable for light weight objects. In order

to be more accurate, a mathematical model ∑f=1

4

W f that counts

the weight of all four fingers is preferable. Moreover, as the summation of W f is close to 7 N, it does not interfere either with heavier objects, which is whyW f is not included in (2).

Fig. 7. Bang-bang loops driven by proportional controllers.

Fig. 8. The Ambidextrous Robot Hand holding a can with a grasping movement implemented with bang-bang controllers. It is seen the can has deformed itself.

As discussed in section III, the phalanges must close with appropriate speed’s ratios to tighten around objects when interacting with object. By repeating the experiments realized in section III, the pictures obtained with the bang-bang controller are provided in Fig.8. This time, it is noticeable from (a) that the can of soft drink deforms itself when it is grasped on the left hand side. The graph obtained from the data collection of the left mode are provided in Fig. 9.

0.1 0.15 0.2 0.25 0.30.9

1

1.1

1.2

1.3

1.4

1.5

Forefinger

Middle finger

Ring finger

Little finger

Time (sec)

Forc

e (N

)Fig. 9. Graph shows force against time of the four fingers while grasping a drink can with bang-bang controllers.

It is noted during experiment that speed of fingers does not vary when it touches the objects. That explains why we got higher curves in Fig.9 than the previously obtained in Fig.6. This makes bang-bang controller faster than PID loops. The bang-bang controllers also stop when the value of 1 N is overreached but, without predicting the approach to the setpoint, the process variables have huge overshoots. The overshoot is mainly visible for the middle finger, which overreaches the setpoint by more than 50%. Even though backward control is not implemented in the bang-bang controller, it is seen the force applied by some fingers decreases after 0.2 sec. This is due to the deformation of the can, which reduces the force applied on the fingers. It is also noticed that the force applied by some fingers increase after 0.25 sec, whereas the force was decreasing between 0.2 and 0.25 sec. This is due to thumb’s adduction that varies from 7.45 to 15.30 N from 0.1 to 0.25 sec. Even though the fingers do not tighten anymore around the object at this point, the adduction of the thumb applies an opposite force that increases the forces collected by the sensors. The increase is mainly visible for the forefinger and the middle finger, which are the closest ones from the thumb.

Contrary to PID loops, it is seen in Fig.9 that the force applied by some fingers may not change between 0.15 and 0.2 sec, which indicates the grasping stability is reached faster with bang-bang controllers. The bang-bang controllers can consequently be applied for heavy objects, changing the setpoint of F f defined in (2).

V. IMPLEMENTATION OF A BACKSTEPPING CONTROLLER

BSC consists in comparing the system’s evolution to stabilizing functions. Derivative control is recursively applied until the fingers reach the conditions implemented in the control loops. First, the tracking error e1 ( t )of the BS approach is defined as:

[e1( t)e1 ( t ) ]=[F t−F f (t )

−F f (t) ] (2)

where F tis the force put as target and F f (t ) is the force received for each finger. The stability of this close loop system is then evaluated using a first Lyapunov function defined as:

V 1 (e1)=12

e12( t)<Fgmin (2)

V 1 (e1)=e1 (t )∗e1 (t )=−F f (t )∗e1 ( t ) (2)

The force provided by the hand is assumed not being strong enough as long as e1

2/2 exceeds a minimum grasping force defined asFgmin. In (2), it is noted thate1 (t ) ≠0 as long asF f ( t )keeps varying. Therefore, V 1 (e1) cannot be stabilized until the system stops moving. Thus, a stabilizing function is introduced. This stabilizing function is noted as a second errore2 ( t ):

e2 ( t )=k∗e1 ( t ) (2)

Fig. 10. Basic Backstepping controller is shown in above figure..

with k a constant > 1. e2 ( t ) indirectly depends on the speed, as the system cannot stabilized itself as long as the speed is varying. Consequently, both the speed of the system ande2 (t )are equal to zero when one finger reaches a stable position, even ifF f (t )≠ Ft . kaims at increasing e1 ( t ) to anticipate the kinematic moment when e1 ( t ) becomes too low. In that case, the BSC must stop running asF f ( t )is close toF t. Both of the errors are considered in a second Lyapunov function:

V 2 (e1, e2 )=12(e1

2 (t )+e22 (t ))<F s (2)

V 2 (e1 , e2 )=e1 (t )∗e1 ( t )+e2 ( t )∗e2 ( t ) (2)

where F srefers to a stable force applied on the object. This second step allows stabilizing the system using derivative control. Using (2), (2) can be simplified as:

V 2 (e1, e2 )=e1 (t )∗(e1 ( t )+k e1 (t )) (2)

Fig. 11. Ambidextrous hand grasping a can of soft drink using backstepping controllers (Right hand mode and left hand mode).

The block diagram of backstepping process is illustrated in Fig.10. According to the force feedbackF f (t ), the fingers’ positions adapt themselves until the conditions of the Lyapunov functions (V ¿¿1 , V 1)¿ and (V ¿¿2 , V 2)¿ are reached.

0.1 0.15 0.2 0.25 0.3 0.35 0.40.6

0.7

0.8

0.9

1

1.1

Forefinger

Mid-dlefinger

Ring finger

Little finger

Time (s)

Forc

e (N

)

Fig. 12. Graph shows force against time of the four fingers while grasping a drink can with Backstepping controllers.

The grasping features obtained with the BSC are illustrated in Fig.11. The force against time graph is obtained from the data collection is shown in Fig.12.

During the experiment, it was noticed that speed of finger tightening using backstepping control was much slower compared to PID controller and bang-bang controller. Since the backstepping controller’s target is based on force feedback and speed stability, it offers greater flexibility than PID and bang-bang but takes longer to stablise. Finger provided enough force to grab the can of soft drink at 0.30 sec, but it is seen the system carries on moving until 0.40 sec. Therefore it was dedcused that BSC takes longer to stablise. The force collected for the thumb at the end of the experiment is 13.10 N, which is a value close to the one obtained with the PID control. It can also be noted that the fingers’ speed is slower using BSC, as none of the sensors collect more than 0.80 N after 0.15 sec. The higher speeds of the PID and bang-bang controllers are respectively explained because of the integrative term and the lack of derivative control.

Fig. 13. Ambidextrous Robot hand holding water bottle and ball.

VI. EXPERIMENTAL ANALYSIS A table has been constructed outlining the key difference

found from the results of all three controllers.It can be seen from Table 1 that the best performances are reached with PID and BS controls, as both of them are accurate and permit the fingers to adapt to the shape of objects with backward movements. However, PID loops proved faster than any other controller, which could be one of the main reason of finding high number of resources in literature review.

Despite its accurate implementation, the BSC did not reveal as robust results. The fingers stabilize themselves after 0.35 sec with BSC, against 0.20 sec for PID and bang-bang controls. Indeed, as for sliding mode control (SMC), the main advantage of BSC is its ability to regulate nonlinear actuators. This is the reason why these two algorithms receive feedbacks from pressure or position sensors in [108] and [109]. Nevertheless, in the case considered in this paper, the feedback is received from force sensors directly implemented on the mechanical structure instead of the actuators themselves.

Speed Accuracy Adaptability Implementation

PID Fast High High Fast

Bang-bang

Fast Low Low Fast

BSC Average High High Long

IdealOption

Fast High High Fast

Table 1 : Comparison between PID, bang-bang and backstepping controls and an ideal condition.

Even though bang-bang control is the fastest of the three compared algorithms, it is not smooth enough to adapt itself to the shape of the objects and can crush them. The shooting function of the bang-bang controller is too sudden without additional controllers, which is why it is cascaded in [110] and [111]. However, bang-bang control can be used to grab heavy object. The higher is the PAMs’ pressure, the slower the PAMs contract, which is why their elasticity automatically opposes itself to the shooting function effect in that case.

Fig. 14. Ambidextrous Robot hand grabbing a can in ambidextrous fashion.

By implementing force sensors both on right and left sides of the fingers, the Ambidextrous Hand can also grab objects in atypical positions, as shown in Fig 14.

CONCLUSION Three different controllers namely PID, Bang-bang and BSC are tested and their performance in controlling an ambidextrous robotic hand is analyzed in great detail. PID controller was found the best when applied as compare to Bang-bang and Backstepping control. Backstepping control technique was validated for the first time on an ambidextrous robot hand. By combining PID controllers and force sensors, this research proposes one of the cheapest solutions possible. In future, PID controller could be used with artificial intelligent controllers to further improve the controls.

ACKNOWLEDGMENT

The authors would like to cordially thank Anthony Huynh, Luke Steele, Michal Simko, Luke Kavanagh and Alisdair Nimmo for their contributions in design and development of the mechanical structure of a hand, and

without whom the research introduced in this paper would not have been possible.

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