Offline Q-Learning Controller for Hybrid PV–Battery System in MATLAB/Simulink -- 2

@cesur135

As a highly experienced electrical and automation engineer, I possess the skills necessary to successfully complete the project of designing and implementing an offline tabular Q-learning controller for your hybrid PV–battery system in MATLAB/Simulink. My track record includes creating functional, efficient systems for wastewater treatment plants and water treatment plants using Siemens TIA Portal, Simatic Manager, and WinCC SCADA programs. During these projects, I also utilized SIEMENS PH & INFORMATION SERVER program for comprehensive reporting and conducted thorough point to point tests. My expertise extends to machine automation, where I have written software and controlled various machines using AB PLC -RS Logix 5000 and Factory talk. I also bring experience in energy automation to the table, having built and managed the generators PLC and SCADA systems in HES projects. These experiences uniquely position me to handle all aspects of your project, from developing a complete Simulink model to incorporating the buck converter equations into the reward function for physics-informed reinforcement learning.

@alluankit01

Hi, I can develop the complete offline tabular Q-learning controller in MATLAB/Simulink for your hybrid PV–battery system with buck-converter voltage regulation. My approach will be: • Build the PV–battery–buck Simulink plant model • Define state/action spaces for voltage error, irradiance, load, SOC, and duty-cycle control • Generate offline transition datasets from varied operating conditions • Train and validate the Q-table • Implement the greedy controller inside a MATLAB Function block • Regulate output voltage under changing irradiance, load, and battery SOC • Add physics-informed state features from buck-converter equations • Incorporate converter dynamics into the reward function • Compare baseline Q-learning vs physics-informed Q-learning • Provide voltage, duty-cycle, SOC, and tracking-error plots • Document state discretization, reward design, training, and usage Deliverables will include the full .slx model, MATLAB scripts, dataset, trained Q-table, controller block, result plots, and concise documentation. I can structure the work in two milestones matching your two phases and provide a first working baseline before adding the physics-informed improvements. Best regards Ankit

@Ahmadali9876

I have completed similar MATLAB/Simulink control, power-electronics and embedded control projects, and I can develop your offline tabular Q-learning controller for the hybrid PV–battery–buck converter system in two structured phases. I’ll build and validate the complete PV–battery–buck model, generate transition data across irradiance, load and SOC conditions, train the discretized Q-table, and implement the greedy controller through a MATLAB Function block. In Phase 2, I’ll incorporate the buck converter physics into the state representation and reward design to improve learning relevance and voltage regulation. Deliverables Complete PV–battery–buck Simulink model Offline transition dataset Trained Q-table MATLAB Function greedy controller State-space/discretization encoding Physics-informed reward and state features Buck-equation integration Variable irradiance/load/SOC simulations Voltage regulation results and plots Brief technical documentation Why Choose Me I have 5+ years of experience in engineering design, embedded systems, PCB/power electronics and simulation-oriented development. I understand the interaction between converter dynamics, control variables and operating conditions, allowing the RL controller to be developed around the actual system behaviour rather than as a standalone algorithm.

@qualityxenter

Affordable, Early Delivery. ★★★★★★★★★★★★★★I hold a Masters degree which gives me the requisite background to handle writing from various subjects. I am a highly committed person towards my work. You can rely on QualityXenter for quality and consistency in writing. We never violate copyright rules. I have vast amount of experience in this industry since I am working from 2015 as a professional writer. I provide many modifications till to get your satisfactions. I have access to enough journals to use in your research project. I always produce quality work at VERY LOW RATES so, don't worry if you have a low budget for your work, I will be very happy to make a new client like you. I am producing quality work for my clients including ARTICLE WRITING, REPORT WRITING, ESSAY WRITING, RESEARCH PAPERS, BUSINESS PLAN, TECHNICAL WRITING, MATLAB, THESIS, ACCOUNTING & FINANCE work ETC. Go through my profile link https://www.freelancer.com/u/qualityxenter

@sufyanasmat38

Hello, I have strong experience with MATLAB, Simulink, reinforcement learning, power electronics, and control systems. I can develop the complete hybrid PV battery buck converter model, generate offline transition data, train and implement the Q table through a MATLAB Function block, and validate closed loop voltage regulation under varying irradiance, load, and battery SOC. I can also add physics informed state features and reward design using the buck converter equations, then provide simulation results and clear documentation covering encoding, training, reward logic, and usage. I can start immediately and would be happy to discuss the required system parameters and voltage regulation targets.

@SidUSA

Hi, Quantum Code Solutions is available to transform your hybrid PV–battery system into an optimized, intelligent control environment in MATLAB/Simulink. The core of a high-end Q-learning controller lies in accurate state discretization, effective offline data collection, and seamless integration of a MATLAB Function block to regulate buck converter output voltage under shifting irradiance, load, and battery SOC. We plan to utilize physics-informed reinforcement learning, incorporating buck converter governing equations into the reward function and state features to ensure stable, robust closed-loop performance. Recently, our team delivered an advanced reinforcement learning control model in MATLAB/Simulink for a multi-source power system. We designed state-action transition spaces, structured custom reward functions, and implemented offline training routines that handled dynamic load variations efficiently. We resolved closed-loop convergence issues and built a modular framework that streamlined testing and validation across different operational scenarios. Our work ensured precise voltage regulation and provided clear, comprehensive system documentation. We are eager to discuss the project further. Reach out to initiate a conversation! Best regards, Quantum Code Solutions

@elijahhmonreal

Hi there, I'm excited to tackle your offline Q-learning controller for the hybrid PV-battery-buck converter system. As an AI/Controls Engineer with 12+ years of experience in reinforcement learning and power electronics modeling, I'll deliver a complete, physics-informed solution in MATLAB/Simulink. I'll build the full hybrid system model with accurate PV and battery dynamics, generate transition datasets through intelligent sampling, and train a robust Q-table controller. The greedy implementation in a MATLAB Function block will ensure reliable closed-loop voltage regulation across varying irradiance, load, and SOC conditions. For phase two, I'll enhance the reward function using actual buck converter equations, optimize state representations, and demonstrate stable performance under dynamic scenarios. Complete documentation will cover encoding schemes, reward design rationale, and practical usage guidelines. Your system will be production-ready with validated voltage control and clear implementation paths. Let me know if my profile looks interesting, and we can set up a time to talk. Best regards, Elijah M

@richard4835

Hi, I'm a licensed professional engineer with 20+ years of experience in electrical engineering, control systems, and MATLAB/Simulink modeling. I understand you need an offline Q-learning (Q-table) controller for a hybrid PV–battery system to regulate a buck converter’s output voltage across changing irradiance, loads, and battery SOC, with a physics-informed RL enhancement. - Build the full Simulink model for the PV–battery–buck converter system - Create and share the offline dataset of transitions for Q-table training - Train and implement the Q-table controller in a MATLAB Function block - Integrate buck converter equations into state/reward for physics-informed RL - Provide a short document explaining encoding, reward design, and usage I’ve worked on PV–battery and converter simulation projects, including RL-based controllers in MATLAB. Could you clarify if you already have any part of the Simulink system set up, or should I start from scratch? If you select me, I will satisfy you. Best Regards, Richard

@stevens297

Hi there, Can you confirm you want a single MATLAB/Simulink deliverable covering both phases end-to-end? I’ll handle this in straightforward steps: - Build the hybrid PV-battery-buck Simulink plant with irradiance, load, and SOC variation - Generate transition datasets from the plant under controlled exploration - Train an offline tabular Q-learning Q-table and save the trained policy - Implement a MATLAB Function block that applies the greedy Q-table for closed-loop voltage regulation - Upgrade the state/reward with physics-informed buck converter equations - Run demonstrations showing stable output voltage across scenarios and log results - Package brief documentation for encoding, reward design, and how to run/use the model Once these steps are completed, you’ll have a ready-to-run Simulink project with the controller and demo results. Please reply so we can discuss the exact model assumptions and get started. Thanks, Steven

@Aaleen110

The main technical risk here is ensuring the Q-table captures sufficient state space to accurately regulate the buck converter output voltage given the PV, battery, and load variations so it performs well across all conditions. I will build the Simulink model of the hybrid PV, battery, buck system, generating an offline dataset of system transitions by running simulations with different irradiance, load, and SOC profiles. I will then train a tabular Q-table using this dataset, directly implementing the Q-learning algorithm in MATLAB for efficiency. The trained Q-table will be integrated into a MATLAB Function block within Simulink to provide closed-loop voltage regulation. I would develop the reward function for the physics-informed RL by directly penalizing deviations from the target output voltage and also adding a term based on the buck converter's steady-state voltage equation, rather than just using state features. This grounds the learning in the system's physics from the start and should lead to faster convergence and a more accurate controller, directly addressing your goal of accurate voltage regulation. I'll use the greedy Q-table policy for control. I have 10 reviews on Freelancer at 5.0, everything delivered on time and on the agreed price so far. My question is about the state representation for the controller. Will the state include the current output voltage and the target output voltage, or will it be defined differently to capture PV and battery conditions? If you hand this over, I will have the core Simulink model built, the offline dataset generation framework set up, and the initial Q-learning training script ready to run by the end of the first day or two.

@vladislavl45

Building a robust and efficient Simulink model for your hybrid PV--battery system, then utilizing offline tabular Q-learning to train and apply the Q-tables - that's what I specialize in. With over eight years of developing precise embedded systems solutions for various applications, including IoT and automation, I'm more than capable of meeting and exceeding your expectations for this project. My core competency in microcontrollers such as STM32, ESP32/ESP8266 , nRF52, and experience with ultra-low power design is particularly relevant to this project. I also have a deep understanding of PCB design, embedded firmware development (URAT, SPI, I2C), wireless protocols (BLE, WiFi, LoRa), and motor control systems which will be key in developing an effective and efficient controller for your buck converter. Ensuring client satisfaction through clear communication, reliable delivery, and thorough post-project support is always my priority. Let's work together to create a complete Simulink model of your hybrid PV-battery system with all the necessary components requested and demonstrate its flawless performance under varying conditions. My skills combined with a production-ready design focus guarantee a high-quality end product that you can rely on.

@SoftwareLeader

HI,,,I have experinece in RL, Q-learning/Q-table. ALso I 'v used AI modelling in Matlab & Simulink. And the PV-battery system is interesting. Let 's collaborate. thanks

@claytond32

When this PV–battery controller is done right, your buck converter output voltage stays tightly regulated even as irradiance, load, and battery SOC swing—because the controller learned the system’s transition dynamics offline and then runs a fast greedy policy in Simulink. I’ll build a complete MATLAB/Simulink plant for PV–battery–buck, generate transition datasets, train a tabular Q-table, and implement the closed-loop greedy controller inside a MATLAB Function block for voltage regulation. In phase two, I’ll incorporate physics-informed reinforcement learning: I’ll embed buck converter equation–based features into the state representation and shape the reward using voltage/current tracking terms derived from the converter dynamics. You’ll get demonstrations across operating corners, plus brief documentation on encoding, reward design, dataset structure, and how to run/train/use the Q-table.

@davianp2

I’ll guarantee a reliable offline Q-learning voltage regulation solution in MATLAB/Simulink that stays stable across irradiance, load, and battery SOC by using a tabular Q-table and a greedy action policy. I’ll first build a complete Simulink PV–battery–buck system, then run the plant to generate a transition dataset (state, action, reward, next state), train the Q-table offline, and embed the controller in a MATLAB Function block for closed-loop output-voltage regulation. Next, I’ll enhance it with physics-informed RL: I’ll incorporate buck-converter equations into state features (e.g., relations tied to duty-cycle/inductor-current/output dynamics) and into the reward function so the agent is directly incentivized to minimize voltage error while respecting physically consistent behavior. You’ll get the Simulink model, the offline transitions plus trained Q-table, a working greedy controller block, and a voltage-regulation demo under varying conditions with brief documentation on enco

@jaysony5

Built a similar “offline RL power controller” for a hybrid energy prototype in MATLAB/Simulink: we trained a Q-table from an offline transition dataset, then embedded a greedy policy in a MATLAB Function block for closed-loop regulation. Lesson learned: discretization and reward shaping matter more than network depth—stable voltage control came only after aligning state/action bins with PV–battery dynamics and buck-converter constraints. I will deliver Phase 1 by creating a complete Simulink model (PV + battery + buck converter feeding the load), running scenario sweeps across irradiance, load, and SOC, and logging transitions to train a tabular Q-learning controller. I’ll implement the learned greedy policy in a MATLAB Function block to regulate output voltage reliably. For Phase 2, I’ll incorporate physics-informed features and reward: buck converter equations will guide voltage-error and control-smoothness terms, and additional state components will reflect converter dynamics.

@aydont

YOU ONLY NEED ONE DEVELOPER WHO GETS IT RIGHT THE FIRST TIME. HERE'S WHY I'M THAT DEVELOPER. I recently helped a client transform their concept into a robust MATLAB/Simulink solution that was efficient and easy to adapt. I'd love to do the same for you and deliver a system that's reliable from the start. We can create a complete Simulink model for your hybrid PV-battery system that accurately regulates voltage under varying conditions. I understand you need a clean and professional implementation of the Q-learning controller, along with physics-informed enhancements for optimal performance. We have 75+ 5-star reviews on similar projects and rank in the top 1 percent among 75 million users. I'd love to chat about your project. The worst that can happen is you walk away with a free consultation. Regards, Aydon.