All classes PID & FOPID Metaheuristics Syllabus · Episode 5 of 6

Class PID & FOPID Metaheuristic #5: Optimization of PID vs FOPID on DC Motor & AVR System

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Tutorial on PID and FOPID optimization using Whale Optimization and Harris Hawks based on ITAE for AVR and DC motor with Python language. Episode 5 of the PID and FOPID metaheuristics optimization series.

Software: Python (NumPy, SciPy, Matplotlib)
Level: Advanced
Duration: ± 7 min
Series total: 52 min · 6 videos
Published: October 25, 2026

Episode 5 of 6 · view syllabus

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Class PID & FOPID Metaheuristic #5: Optimization of PID vs FOPID on DC Motor & AVR System: Cover: PID & FOPID Metaheuristic Class #5: PID vs FOPID Optimization on DC Motor & AVR System

Class PID & FOPID Metaheuristic #5: Optimization of PID vs FOPID on DC Motor & AVR System: Before you start, steps list, and Step 1

Class PID & FOPID Metaheuristic #5: Optimization of PID vs FOPID on DC Motor & AVR System: Step 2: AVR & DC Motor Optimization

Class PID & FOPID Metaheuristic #5: Optimization of PID vs FOPID on DC Motor & AVR System: Step 3: Step Response after Optimization

Class PID & FOPID Metaheuristic #5: Optimization of PID vs FOPID on DC Motor & AVR System: Done: Final Result and Further Practice

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Cover: PID & FOPID Metaheuristic Class #5: PID vs FOPID Optimization on DC Motor & AVR System

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Transcript

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Introduction

Here is the final result, the step response of the AVR with PID and FOPID optimized using Whale Optimization and Harris Hawks. Compared to Ziegler-Nichols, overshoot drops by 53%, and FOPI-2OA cuts ITAI more than half compared to the best PID. Let's start. Hello, welcome to episode five of the PID and Fractional Order PID optimization series. All materials are ready, system model, PID, FOPID, objective function, and 2 metaheuristics. Today we will combine them to find the best parameters for 2 plans, AVR generator and DC motor, then fairly compare PID with FOPID.

Step 1: Plant, limits & optimization function

Create an optimization .py file. We prepare 2 plans along with the search parameter bounds. For AVR, KAP and KAI are between 0 and 2, KA da is between 0 and 1, the common bounds used in the literature to ensure the control signal is not excessive. DC motor needs much larger gain because its amplifier is only 0.1. FOPI is added with 2 parameters, lambda and MU between 0.5 and 1.5. The optimization function runs both algorithms for both controllers, with a population of 25 and 50 iterations. We run it for each plan, print ITAI, overshoot, settling time, and parameters, then save them to an optimization results .json file for use in the next episode.

Step 2: Optimization of AVR & DC motor

Run. For AVR, PIDWOA and PIDHHO both achieve ITAI of 0.0390, both finding the same optimum. FOPI2OA achieves 0.0189 with a settling time of 0.54 seconds, almost twice as fast as PID, and FOPIDHHO 0.0251. For the DC motor, the story is different, PIDWOA 0.0085, slightly better than FOPIDWOA 0.0088, with lambda returning to 1. Let's look at the graphs.

Step 3: Step response of optimized results

For AVR, PID Ziegler Nichols jumps up to 1.5, while all the optimization results are much smoother. The FOPI curve rises the fastest and settles the earliest, with a slightly higher overshoot as the price. For the DC motor, the PID and FOPIDWOA curves are almost overlapping. This is an important lesson for research, FOPI excels in high-order oscillating plans like AVR. However, in simple second-order plans, PID is already sufficient, and the additional parameters actually complicate the search, as seen from FOPIDHHO being left behind.

Done!

Done. We have optimized PID and FOPID with VOA and HHO on AVR and DC motor, and found that the superiority of FOPID depends on the plan. But a single trial is not enough for a random algorithm. In the final episode, we test robustness and perform statistical analysis. Currently working on a thesis, dissertation, or research in the field of control systems and optimization? Cerdas Inovasi Digital helps implement control systems, from modeling DC motors and AVR generators, tuning PI using Ziegler-Nichols, fractional order PID, up to meta-heuristic optimization such as whale optimization and Harris-Hawks, as well as offering Python programming courses for electrical engineering. Want to master the entire series, from alias functions to statistical analysis and robustness testing? All six episodes, complete with PDF step-by-step guides and source code, are available to premium tutorial subscribers. Subscribe starting at Rp69.000 per month, save 28% with the 12-month package, or pay once Rp999.000 for lifetime access. Want just one class? Buy a single class with lifetime access. Subscribe now at the premium page cerdasinovasidigital.com. Contact us via WhatsApp, or visit cerdasinovasidigital.com. Thank you for watching, see you in the next episode.

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Transcript

0:03Introduction

0:50Step 1: Plant, limits & optimization function

3:24Step 2: Optimization of AVR & DC motor

4:22Step 3: Step response of optimized results

5:52Done!

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Transcript

0:03Introduction

0:50Step 1: Plant, limits & optimization function

3:24Step 2: Optimization of AVR & DC motor

4:22Step 3: Step response of optimized results

5:52Done!

Ask the class material

My notes

Questions & answers

Your instructor

Introduction

Step 1: Plant, limits & optimization function

Step 2: Optimization of AVR & DC motor

Step 3: Step response of optimized results

Done!

Introduction

Step 1: Plant, limits & optimization function

Step 2: Optimization of AVR & DC motor

Step 3: Step response of optimized results

Done!