R. J. Chang

About

R. J. Chang is a researcher whose work lies at the intersection of stochastic dynamics, nonlinear systems, and robotics. His primary contributions focus on developing analytical methods to predict the stationary response of complex mechanical systems subjected to random excitations. In his most cited work (1989, 13 citations), Chang derived a Lagrangian dynamic equation combined with statistical linearization to model robot manipulators under stochastic base and external excitations, accounting for geometric constraints. This approach was pioneering in its use of a truncated Gaussian density to handle the nonlinearities inherent in robotic motion. Chang further advanced the field by introducing a Fourier-series closure scheme (1991) to predict the stationary response of nonlinear oscillators under both parametric and external stochastic excitations, particularly those with nonpolynomial nonlinearities and state constraints. While his citation counts are modest, his work represents foundational contributions to the stochastic analysis of constrained nonlinear systems, offering tools that are valuable for engineers designing robots and structures that must operate reliably in unpredictable environments. His research remains relevant for those studying the intersection of control theory, nonlinear dynamics, and probabilistic methods.

Research Focus

Key Achievements

Top Papers

1. 1
   Prediction of Stationary Response of Robot Manipulators Under Stochastic Base and External Excitations—Statistical Linearization Approach

   13 citations · 1989
2. 2
   Stationary Response of States-Constrained Nonlinear Systems Under Stochastic Parametric and External Excitations

   2 citations · 1991
3. 3
   Prediction of Stationary Response of Robot Manipulators under Stochastic Base and External Excitations - Statistical Linearization Approach

   2 citations · 1988

Key Collaborators

Contact & Links