Stimulating Higher Order Thinking in Mechatronics by Comparing PID and Fuzzy Control

Technical Summary: Stimulating Higher Order Thinking in Mechatronics by Comparing PID and Fuzzy Control

Problem Statement
Traditional engineering curricula often rely on homework and lab exercises with well-defined solutions, which fail to cultivate the higher-order thinking skills (analysis, synthesis, and evaluation) required for professional engineering practice. Real-world engineering problems are frequently ambiguous and lack explicit performance criteria or step-by-step instructions. Consequently, students often struggle to transfer classroom concepts to open-ended design scenarios where they must independently evaluate alternatives and make informed design decisions. The authors identify a gap in existing mechatronics courses: while many employ project-based learning, they often focus on system functionality rather than requiring students to rigorously analyze design alternatives, quantify performance using self-defined metrics, and defend design choices through critical evaluation.

Methodology
To address this gap, the authors implemented a semester-long mechatronics course at the United States Military Academy (USMA) featuring a culminating comparative study project. The course targets senior-level electrical and mechanical engineering students and utilizes a scaffolded approach:

1. Foundational Exercises: Students complete preliminary mini-projects using Arduino microcontrollers to integrate sensors (specifically the Pixy camera for computer vision) and actuators. Crucially, they must program Proportional-Integral-Derivative (PID) and Fuzzy logic controllers from scratch using C, without relying on pre-written libraries, to ensure a deep understanding of underlying principles.
2. The Comparative Study Project: The final project requires teams (typically interdisciplinary pairs of one electrical and one mechanical engineer) to determine the superior control methodology (PID vs. Fuzzy) for an autonomous "leader-follower" scenario. In this scenario, a follower robot must track a lead vehicle based on visual detection of a colored target.
3. Open-Ended Constraints: Unlike traditional labs, the project provides specifications but no explicit experimental procedures or performance criteria. Students must:
   • Design their own experimental setups (e.g., determining step response tests for speed and steering).
   • Define their own metrics for evaluating controller performance (e.g., steady-state error, resource usage, oscillation).
   • Conduct data logging and statistical analysis to support their conclusions.
4. Pedagogical Structure: The project is supported by weekly In-Progress Reviews (IPRs) where teams present findings to the class, fostering peer critique and idea exchange. Additionally, students are trained in scientific writing (using LaTeX) and must review existing literature on controller comparisons before beginning their experiments.

Key Contributions
The paper details a specific educational intervention designed to shift student learning from the lower levels of Bloom's Taxonomy (comprehension and application) to the higher levels (analysis, synthesis, and evaluation). The primary contributions include:

• Curriculum Design: A structured framework that transitions students from guided mini-projects to an open-ended comparative study where they must generate their own evaluation criteria.
• Implementation of Ambiguity: The deliberate removal of step-by-step instructions to force students to confront the complexity of real-world engineering decisions, including conflicting literature and the need for independent experimentation.
• Assessment of Higher-Order Thinking: The use of student-generated experimental designs and the subsequent analysis of their ability to synthesize concepts and evaluate alternatives as evidence of higher-order thinking.

Results
The approach was implemented and observed over three semesters. Key findings include:

• Student Creativity and Critical Thinking: Most teams developed creative and non-trivial performance criteria and experimental methods. For example, some teams analyzed the trade-offs between controller performance and microcontroller resource usage (memory and refresh rates), a factor not explicitly required by the instructors.
• Handling Complexity: Students discovered that creating a comparative study is complex due to the numerous factors influencing performance (e.g., starting distances, variable velocities, and the interaction between steering and throttle controls). This mimicked the practical challenges engineers face.
• Student Perception: Survey data from 24 students indicated that the majority agreed the open-ended nature of the project forced them to think more deeply than in other courses. While some students initially felt uncomfortable or frustrated by the lack of a defined procedure, they reported increased confidence in their ability to frame experiments and make evidence-based design decisions.
• Peer Learning: The IPRs were effective in stimulating critical evaluation of peer approaches, with many teams modifying their own experimental plans based on ideas presented by classmates.

Significance
The paper claims that this project-based approach successfully stimulates higher-order thinking skills essential for professional engineers. By requiring students to establish decision criteria, gather evidence, and compare alternatives without a "correct" answer provided by the instructor, the course fosters the ability to make evidence-based judgments. The authors posit that this method bridges the gap between academic exercises and the ambiguous, open-ended problems encountered in professional practice. The significance lies not in the specific control algorithms (PID vs. Fuzzy) themselves, but in the pedagogical process of forcing students to navigate the uncertainty of design decisions, thereby strengthening their analytical, synthetic, and evaluative capabilities.