Papers
11
Total Citations
245
H-Index
7
About
Ian Howard is a leading researcher in robotics and control systems, with a primary focus on neural adaptive control, human-robot interaction, and bio-inspired robotics. His major contributions include developing neural network-based adaptive tracking controllers for robot manipulators operating under time-varying joint constraints and velocity limitations—work that has garnered over 65 and 32 citations, respectively. Howard has also advanced assistive robotics through neural impedance adaptation for human-robot interaction (45 citations) and assist-as-needed control strategies for rehabilitation robots, which aim to provide minimal robotic assistance to maximize patient effort during stroke therapy. In the realm of bio-inspired design, he led the creation of a bionic piezoelectric robotic jellyfish using a large deformation flexure hinge (43 citations), addressing the challenge of small output displacement in piezoelectric actuators. Additionally, his work on piezoelectric inertial robots for small pipeline inspection, based on stick-slip mechanisms, has been cited 28 times. Howard’s research extends to optimal robot-environment interaction via the inverse differential Riccati equation and path planning for polyhedrons with rolling contact. With a career spanning from early work on beam-type structure vibration modeling (1988) to cutting-edge chaotic behavior analysis in micro pipeline robots (2024), Howard’s diverse and impactful contributions continue to shape the fields of adaptive control, rehabilitation robotics, and bio-inspired engineering.
Research Focus
Key Achievements
Top Papers
- 1
- 2Neural impedance adaption for assistive human–robot interaction45 citations · 2018
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- 8Neural adaptive assist-as-needed control for rehabilitation robots7 citations · 2016
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- 10A Literature Review on Path Planning of Polyhedrons with Rolling Contact3 citations · 2019