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About

Daniel Pereira is a control systems researcher whose work spans advanced robotics control, intelligent systems, and fault-tolerant control for autonomous platforms. His research focuses on developing robust and adaptive control strategies for complex robotic systems, including robot manipulators, parallel robots, and multi-linked mobile robots. Among his most recognized contributions is his 2020 work on super-twisting sliding mode control (STSMC) for robot manipulators, which demonstrated rigorous stability guarantees via Lyapunov theory and garnered 66 citations — establishing him as a notable voice in robust tracking control. The following year, his exploration of Interval Type-2 Fuzzy Logic Controllers optimized via social spider algorithms for parallel robots drew 63 citations, reflecting strong community interest in bio-inspired tuning methods for intelligent controllers. Pereira has also made meaningful strides in fault-tolerant control, addressing actuator faults and adaptive recovery strategies for multi-linked two-wheel-drive mobile robots — a practically significant area for autonomous systems reliability. His 2018 foundational work in this domain laid the groundwork for more comprehensive fault estimation frameworks developed in 2023. Collectively, his publications demonstrate a coherent research vision: making robotic systems smarter, more resilient, and more dependable across increasingly complex configurations.