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008			260407t20252025my a|||fr|||| 000 0 eng d
020			_aTHE0010441 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aFTKEE .A94 2025 r Thesis
100	1		_aMohd Iskandar Putra Bin Azahar, _eauthor.
245	1	0	_aImproved prescribed performance control for dynamic motion of a pneumatic gripper robot / _cMohd Iskandar Putra Bin Azahar
264		1	_aKuantan, Pahang : _bUMPSA, _c© 2025
264		4	_c© 2025
300			_axx, 163 pages ; _billustrations ; _c30 cm. + _e1 CD-ROM.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Electrical and Electronics Engineering
502			_aThesis (Doctor of Philosophy) -- Universiti Malaysia Pahang - 2025
504			_aIncludes bibliographical references
520	3		_aPneumatic robots offer significant advantages in compliance, power-to-weight ratio, and speed, but their use is hindered by significant control challenges. The inherent nonlinearities from air compressibility, stiction, and friction, combined with unmodeled system uncertainties and sudden external disturbances, cause conventional model-free control methods (like standard PID) to struggle. This results in poor tracking performance, oscillations, and instability, particularly during dynamic tasks. This research addresses this gap by proposing and validating the progression of adaptive Prescribed Performance Control (PPC) methods. These methods are integrated with a PID inner-loop to guarantee high performance for a tri-pneumatic grasper (TPG) robot without requiring a complex system model. The methodology first addresses finger positioning by developing a Finite-Time PPC (FT-PPC), which solves the asymptotic convergence of conventional PPC, enabling user-defined settling times. For the more dynamic task of pressure regulation, a Variable Convergence Rate PPC (VCRPPC) is introduced to provide a more flexible and aggressive adaptive control envelope. Finally, to ensure safe physical interaction, a Dynamic Error PPC (DEPPC) is formulated to solve the critical problem of control singularity, incorporating a pressure-based force estimator to robustly manage external forces. Experimental results from the physical TPG testbed validate the efficacy of each proposed controller in its specific domain. The FT-PPC eliminated overshoot and achieved a 0.3-second settling time with a 49% improvement in rise time for positioning. The VCRPPC excelled in pressure regulation, demonstrating a 63% faster transient response and a 78% reduction in steady-state error. Most critically, the DEPPC proved robust for dynamic interaction, achieving an 84% faster rise time and a 78% reduction in tracking error under applied forces, all while successfully preventing the control singularity that caused other controllers to fail. In conclusion, this progressive development of adaptive PPC methods provides a significant contribution by demonstrating a clear pathway to achieving high-performance, guaranteed control for complex pneumatic systems. The proposed frameworks successfully enhance the TPG's static and dynamic control, improving response time, accuracy, and stability, proving their practical effectiveness for advanced pneumatic robotic challenges.
610	2	0	_aFaculty of Electrical and Electronics Engineering _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses _xDissertations
942			_2lcc _cTHESIS
999			_c104643 _d104649