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  See discussions, stats, and author profiles for this publication at: Implementation of PID, Bang–Bang and Backstepping Controllers on 3D PrintedAmbidextrous Robot Hand Chapter  · July 2016 DOI: 10.1007/978-3-319-33386-1_9 CITATIONS 3 READS 427 4 authors , including: Some of the authors of this publication are also working on these related projects: Cryptography and Steganography   View projectThe Ambidextrous Robot Hand   View projectMashood MukhtarBrunel University London 10   PUBLICATIONS   36   CITATIONS   SEE PROFILE Emre AkyürekBrunel University London 7   PUBLICATIONS   17   CITATIONS   SEE PROFILE Tatiana G KalganovaBrunel University London 83   PUBLICATIONS   783   CITATIONS   SEE PROFILE All content following this page was uploaded by Mashood Mukhtar on 21 December 2017. The user has requested enhancement of the downloaded file.  Implementation of PID, Bang -  bang and Backstep- ping controllers on 3D Printed Ambidextrous Robot Hand Mashood Mukhtar, Emre Akyürek, Tatiana Kalganova, Nicolas Lesne Abstract Robot hands have attracted increasing research interest in recent years due to their high demand in industry and wide scope in number of applications. Almost all researches done on the robot hands were aimed at improving mechanical design, clever grasping at different angles, lifting and sensing of different objects. In this chapter, we presented the detail classification of control systems and reviewed the related work that has been done in the past. In  particular, our focus was on control algorithms implemented on pneumatic systems using PID controller, Bang-bang controller and Backstepping controller. These controllers were tested on our uniquely designed ambidextrous robotic hand structure and results were compared to find the best controller to drive such devices. The five finger ambidextrous robot hand offers total of 13 degrees of freedom (DOFs) and it can bend its fingers in both ways left and right offering full ambidextrous functionality by using only 18 pneumatic artificial muscles (PAMs).  __________________________ Mashood Mukhtar, Emre Akyürek, Tatiana Kalganova Department of Electronic and Computer Engineering, Brunel University, Kingston Lane, Uxbridge, London, UK, e-mail:  Nicolas Lesne Department of System Engineering, ESIEE Paris, Noisy-le-Grand Cedex, France  1 Introduction Robots play a key role in our world from providing daily services to industrial use. Their role is in high demand due to the fact they offer more productivity, safety at workplace and time reduction in task completion. Number of control schemes are found in the literature [1] to control such robot such as adaptive con-trol [2], direct continuous-time adaptive control [3], neurofuzzy PID control [4] hierarchical control [5], slave-side control [6], intelligent controls [7], neural networks [8], just-in-time control [9] ,Bayesian probability [10], equilibrium- point control [11], fuzzy logic control [12], machine learning [13], evolutionary computation [14] and genetic algorithms [15], nonlinear optimal predictive con-trol [16],optimal control [17], stochastic control [18], variable structure control [19], chattering-free robust variable structure control [20] and energy shaping control [21], gain scheduling model-based controller [22], sliding mode control [23], proxy sliding mode control [24], neuro-fuzzy/genetic control [25] but there are five types of control systems (Fig.1) mainly used on pneumatic muscles. Fig .1 . Classification of control algorithm implemented on pneumatic systems. Focus of our research was on the feedback control system and non-linear con-trol systems. Feedback is a control system in which an output is used as a feed- back to adjust the performance of a system to meet expected output. Control sys-tems with at least one non-linearity present in the system are called nonlinear  control systems. 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 feed- back is provided to the processing unit to make necessary changes to reach closer to desire output. The purpose of designing such system is to stabilise the system at certain target. Tactical sensors are employed to investigate the grasping abilities of the ambi-dextrous hand. These sensors have been used several times in the past on the ro- bot hands driven by motors [26], [27] and robot hands driven by pneumatic artifi-cial muscles [28] and [29]. 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 [30] receives vision feed- back 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 [31] 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 inter-act dynamically with its environment, such as catching falling objects. A laser displacement sensor is also used for the two-fingered robot hand introduced in [32]. 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 de-tect 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 con-trollers. Despite the nonlinear behavior of PAMs actuators [33], previous re-searches indicates that these three algorithms are suitable to control pneumatic systems. The work presented in this chapter aim 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. As it can be seen in Fig.2, its fingers can bend in one way or an-other 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]. 2 Implementation of Controllers 2.1 PID Controller PID controller is a combination of proportional, integral and derivative controller [34].Proportional controller provides corrective force proportional to error pre-sent. Although it is useful for improving the response of stable systems but it
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