580 California St., Suite 400
San Francisco, CA, 94104
Key research themes
1. How can control structures be designed and analyzed for robust, adaptive, and optimal performance in nonlinear and uncertain dynamic systems?
This research area investigates the development, analysis, and implementation of control structures tailored for nonlinear and uncertain systems. The focus is on ensuring stability, robustness to disturbances, and optimal or near-optimal performance by employing advanced control theories such as flatness control, sliding mode control, and invariant geometric approaches. Compensating for model inaccuracies, environmental disturbances, and dynamic variations is critical for practical applications across engineering domains including robotics, aerospace, power systems, and biological systems.
Key finding: Demonstrated that the flatness control principle allows direct identification and decoupling of system inputs and outputs in a nonlinear DC motor model, resulting in high-quality speed control performance. When combined with... Read more
Key finding: Proposed a combined feedforward inverse model control with sliding mode corrective feedback controller for nonlinear oil tanker heading regulation. The structure effectively handles model uncertainties and wave disturbances,... Read more
Key finding: Identified internal feedback pathways (IFPs) within biological control systems as critical for achieving robust control despite severe hardware constraints such as sparse, delayed, noisy components (SLD+). Developed minimal... Read more
Key finding: Introduced novel graph-theoretic definitions of invariance notions for structured dynamical systems with sparse input connections, enabling characterization of feedback actions and structural disturbance decoupling. Provided... Read more
Key finding: Provided foundational principles for control and coordination in hierarchical distributed systems, emphasizing modularity and system-level integration. Highlighted the importance of layered architectures that enable complex... Read more
2. How can control structures be effectively designed and verified for practical engineering systems with complex real-world constraints?
This research theme focuses on methodologies for designing control structures and verifying their correctness and equivalence in practical engineering applications. It incorporates model-based control design, control structure discretization, and formal verification to ensure reliable and predictable system behavior despite complexities such as nonlinearities, constraints from discretization, and changes in control logic. Applications include industrial refrigeration, power quality compensation, manufacturing systems, and control architecture comparisons.
Key finding: Developed a systematic PI(D)-based control structure for supercritical CO2 refrigeration with heat recovery under varying operational conditions and constraints. Demonstrated that an advanced PI(D) control strategy can... Read more
Key finding: Proposed a value propagation-based equivalence checking method for finite state machines with datapath to verify correctness of code motion transformations applied during high-level synthesis scheduling. The method uniquely... Read more
Key finding: Introduced Control Network Programming (CNP) paradigm that naturally encodes problem-solving for graph-like, often nondeterministic problem domains. Demonstrated that CNP programs, structured as control networks of connected... Read more
Key finding: Presented foundational control engineering principles for single input, single output linear time-invariant systems. Emphasized the necessity of control structures in a broad range of technology applications, summarized... Read more
Key finding: Provided empirical comparison of hierarchical and distributed control architectures in a model factory for printed circuit board assembly. Showed that each architecture has benefits and drawbacks in terms of complexity,... Read more
3. What are the human and biological considerations influencing control structures, including perception of control and its role in motivation, and the modeling of control in biology and medicine?
Research in this area explores the psychological and physiological aspects of control, focusing on human perception of control (sense of agency) and how it motivates behavior, as well as the application of control theory to biological and medical systems. It highlights the interplay between subjective and objective control, discusses control system analogies in biology, and models control processes underlying neural, motor, and disease regulation, integrating engineering control principles with life sciences.
Key finding: Demonstrated through experimental behavioral economics that humans assign quantifiable intrinsic value to situational control, often preferring control even at financial cost. Found subjective perceived control can diverge... Read more
Key finding: Reviewed diverse applications of control theory to biological and medical systems highlighting parallels between engineered feedback control and physiological regulation (e.g., respiratory, cardiovascular, neuronal). Covered... Read more
Key finding: Showed that biological control systems use complex internal feedback pathways (IFPs), beyond classical plant-controller loops, driven by severe constraints on biological hardware. Proposed that these IFPs are essential for... Read more