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

@iamhamzazaki

As an experienced Mechanical Design Engineer with a keen interest in electrical engineering, I believe I have all the necessary skills to successfully execute your project. With over 7 years in product development, CAD modeling, and engineering analysis, I have honed abilities that align perfectly with your needs. My proficiency in Matlab and Mathematica lets me dive into intricate calculations efficiently to bring about desired outcomes, such as your tabular Q-learning controller. Throughout my professional journey, I have designed numerous electromechanical systems wherein precise voltage regulation played an integral role. Given this familiarity with similar projects, I will deliver an offline dataset of transitions and trained Q-table that guarantees highly accurate voltage regulation under varying irradiance, load, and battery SOC conditions. With a strong conviction in the value of iterative prototyping and engineering validation processes, I ensure my designs don't just work in theory but also lead to seamless implementation and measurable results.

@MuhammadUzair16

Hi I am an embedded systems engineer with over 16 years of experience. I have extensive experience with MATLAB/Simulink, power-converter modeling, closed-loop control, battery/PV systems, and practical reinforcement-learning controllers. I can develop the project in the requested two phases: first building and validating the PV–battery–buck model, generating transition data, training the tabular Q-learning controller, and integrating the greedy policy through a MATLAB Function block; then enhancing the state and reward design using the buck converter’s physical relationships. I will test voltage regulation across irradiance, load, and SOC variations and provide clear documentation covering encoding, reward design, training, and model usage. Do you already have converter, PV, battery, and target-voltage parameters, or should I select representative values? Please also confirm your MATLAB/Simulink version and available toolboxes. Please contact me to discuss details.

@Maryam015014

Hello, I can design and implement the offline tabular Q-learning controller for your hybrid PV–battery–buck system in MATLAB/Simulink. ⚙️ Approach 1. Build full Simulink model of PV + battery + buck converter with variable irradiance, load, and SOC 2. Generate offline transition dataset, train Q-table, and deploy via MATLAB Function block for greedy voltage regulation 3. Phase 2: Add physics-informed RL by embedding buck converter equations into reward function and state features 4. Test regulation under varying conditions and deliver results + brief documentation on encoding, reward design, and usage ? Deliverables Complete Simulink model, offline dataset, trained Q-table, MATLAB Function controller, physics-informed reward/state features, demo results, and documentation ? Skills MATLAB/Simulink, Power Electronics, RL/Q-learning, Control Systems, Data-driven modeling ⏱️ Timeline: 10-15 days with milestone updates ? Revisions: 2 rounds included I’m ready to start once we confirm reference voltage and system specs.

@hayat38402

Hello, I am Muhammad Javed, an Electrical Engineer with more than 10 years of experience in MATLAB/Simulink, power electronics, PV-battery systems, DC-DC converters, and control systems. I can develop your complete hybrid PV–battery–buck converter model and implement the offline tabular Q-learning controller in the two requested phases. I will build and validate the PV, battery, buck converter, load, sensing, and control sections in Simulink, then generate an offline transition dataset covering irradiance, load, SOC, and converter operating conditions. I will define suitable state/action encoding, train the Q-table offline, and implement the greedy Q-table controller using a MATLAB Function block for closed-loop output-voltage regulation. For Phase 2, I will incorporate buck-converter physics into the state features and reward function to create a physics-informed RL controller. The reward will promote accurate voltage tracking while discouraging excessive control actions and undesirable operating conditions. Deliverables will include the complete Simulink model, transition dataset, trained Q-table, MATLAB Function controller, physics-informed reward/state implementation, simulation results under varying conditions, and concise documentation covering encoding, reward design, and usage. I will ensure the model and controller are organized, reproducible, and easy to modify for further testing.

@shykhAbubakar

Hi,I am ready to design and implement an offline tabular Q-learning controller in MATLAB/Simulink for your hybrid PV–battery system feeding a buck converter, with closed-loop voltage regulation under varying irradiance, load, and battery SOC, following IEEE 2030.2 and buck converter physics-based standards for accurate, reliable, and reproducible voltage regulation. Project Deliverables:  Simulink model of PV–battery–buck system  Offline dataset and trained Q-table MATLAB Function block for Q-table controller Physics-informed reward/state features Voltage regulation demonstration Brief documentation of encoding, reward design, and usage I have a few questions: Q1: What switching frequency, state encoding, and output voltage reference should I use? Q2: Do you have any existing Simulink model or specific buck converter parameters? Over 7 years of experience in MATLAB/Simulink modeling, reinforcement learning, and power electronics control systems. Similar projects I have done: • Q-Learning Based DC-DC Buck Converter Voltage Regulation • Hybrid PV-Battery System Modeling with Reinforcement Learning Control Software I can use: MATLAB / Simulink My portfolio is attached below. I am ready to start immediately and look forward to receiving your converter parameters, state definitions, and target voltage specifications. Thank you! Warm regards, Abubakar

@mehwishb3

As a MATLAB expert I want to offer my services . Having prior experience of working in this domain I can design your required Simulink model of the hybrid PV–battery–buck system.

@miguelcen64

Hello, The key part of this project is **building a reliable offline Q-learning controller that keeps the buck converter output stable while irradiance, load, and battery SOC change**. I can help you handle this accurately and efficiently without overcomplicating the process. I have hands-on experience with **Electronics, Engineering, and Documentation**, including control-focused technical work where model clarity and implementation details matter. For your project, I would focus on **the complete Simulink plant model**, **offline transition generation and Q-table training**, and **the MATLAB Function block for greedy voltage regulation**, while making sure the final result is **clear, reproducible, and easy to validate**. I can start immediately and expect to complete this within 10 days. One detail I'd like to confirm before starting: **what switching frequency, state encoding, and output-voltage reference should I use for the Simulink model and reward design**? Best regards, Miguel

@alluankit01

Hi, I can design and implement the offline tabular Q-learning controller for the hybrid PV–battery buck converter system in MATLAB/Simulink. My approach will be to first build the PV, battery and buck converter model, then generate transition data under changing irradiance, load and SOC conditions. After that, I’ll train the Q-table offline and implement the greedy controller inside a MATLAB Function block for closed-loop voltage regulation. I can help with: * Hybrid PV–battery Simulink model * Buck converter modelling * Offline transition dataset generation * Tabular Q-learning training * State/action/reward encoding * MATLAB Function block controller * Voltage regulation testing * Physics-informed reward design * Buck equation-based features * Result plots and documentation Deliverables: * Complete Simulink model * Offline dataset * Trained Q-table * Greedy Q-table controller block * Physics-informed reward/state logic * Simulation results under varying conditions * Brief documentation and usage notes I’ll focus on a clear, reproducible academic implementation with understandable MATLAB code, Simulink structure and documented controller logic. Best regards Ankit

@bohdan38

As a seasoned Electrical Engineer, my experiences perfectly align with the project you've outlined. I have successfully designed and developed numerous firmware projects from inception to manufacturing, encompassing components such as STM32, ARM Cortex-M and FreeRTOS - all of which I developed in C and C++. This expertise is the foundation for my understanding of your need for Simulink modeling of the hybrid PV–battery system which can deliver accuracy in voltage regulation under varying irradiance, load, and battery SOC. Over the years, I've worked extensively on device communication (UART, SPI, I2C, CAN, USB, Ethernet, BLE) and implemented IoT-based projects which required keen attention to power consumption and management. This makes me perfectly positioned to tailor a reliable driver that will effectively regulate your buck converter's output voltage under any condition in question. Given my experience designing multi-layer PCBs using various software you mentioned (Altium Designer, KiCad, OrCAD etc.), I ensure that not only will the developed system be effective but also manufacturable. On top of providing all expected deliverables such as a - Complete Simulink model - Offline dataset of transitions and trained Q-table etc., I'll provide an extensive brief documentation covering enconding,reward design and usage making sure you're well-versed with every element of the project. It'd be an honor to discuss further how we can bring this project to fruition.

@Tahir789

I am a Licensed Professional Electrical Engineer with 8+ years of experience in power systems analysis, solar PV system design, battery energy storage integration, and MATLAB/Simulink modeling. I have delivered multiple hybrid PV-BESS system studies and control system designs, with strong expertise in Simulink-based circuit and power system simulation. I bring solid foundational knowledge in PV array modeling, battery state-of-charge estimation, converter control concepts, and closed-loop regulation strategies. My MATLAB background includes developing simulation models for power systems, control systems, and signal processing applications. For this project, I will develop a complete Simulink model of the hybrid PV–battery–buck converter system, generate transition datasets through simulation under varying irradiance, load, and battery SOC conditions, train the offline Q-table using the generated data, and implement a MATLAB Function block for greedy Q-table-based voltage regulation. Deliverables include the complete Simulink model, trained Q-table and dataset, working MATLAB Function block controller, physics-informed reward/state design, demonstration results. While my primary expertise is in power systems and PV design rather than reinforcement learning, I am genuinely interested in building RL capabilities and am committed to delivering a rigorous, well-documented solution. I'm available immediately. Best regards, Tahir Saleem

@ahmaddar2970

Accepted under manual override. I can deliver the full MATLAB/Simulink workflow for this hybrid PV-battery buck converter control project, including: - Complete Simulink model setup - Offline transition dataset generation - Tabular Q-learning training and greedy Q-table deployment - MATLAB Function block for closed-loop voltage regulation - Physics-informed reward and state design using buck converter equations - Voltage regulation demonstration under varying irradiance, load, and SOC - Brief documentation covering encoding, reward design, and usage I will structure the implementation for clear phase-wise execution and ensure the controller is directly usable in Simulink for closed-loop testing.

@Zack111Arya97

Hi, As a Master’s graduate in Automatics and Systems, I can build, train, and document your offline tabular Q-learning controller for the PV–battery–buck converter system across both phases. # Execution Plan: • Phase 1 (System & Q-Table): Build the complete Simulink model (PV, battery, buck converter), generate transition datasets across varying conditions, train the Q-table offline, and implement the greedy controller inside a MATLAB Function block. • Phase 2 (Physics-Informed RL): Enhance state features and reward design using buck converter governing equations to minimize voltage error, current stress, and chatter under varying irradiance, load, and SOC. # Deliverables: 1. Complete Simulink model (`.slx`) with integrated RL controller block. 2. Training scripts (`.m`) and trained Q-table matrix (`.mat`). 3. Performance plots verifying voltage regulation under transient steps. 4. Concise documentation covering state encoding, physics-informed reward design, and usage. Ready to begin as soon as system parameters, power rating, switching frequency) are confirmed! Best regards, Zakaria L.

@bhargav922002

Hi, How are you! I've carefully checked your requirements and really interested in this job. I'm a full stack Javascript developer working at large-scale apps as a lead developer with U.S. and European teams. I'm offering best quality and highest performance at lowest price. I can complete your project on time and your will experience great satisfaction with me. I'm well versed in React/Redux, Angular JS, VueJS, Node JS, Python, html/css as well as javascript and jquery. Simply, I have rich experienced in Engineering, Electronics, Matlab and Mathematica, Verilog / VHDL, Electrical Engineering as you enumrated. For more information about me, please refer to my portfolios. I'm ready to discuss your project and start immediately. Looking forward to hearing you back and discussing all details. Regards Sincerely, Bhargav

@JaniceR92

Hi, I’ve reviewed your project and understand that you need help with designing and implementing an offline tabular Q-learning controller in MATLAB/Simulink for a hybrid PV-battery system. I can assist you with developing a complete Simulink model of the system, generating an offline dataset of transitions, training the Q-table, and implementing the MATLAB Function block for closed-loop voltage regulation. My experience with control systems and reinforcement learning will allow me to handle the physics-informed enhancements efficiently while keeping communication clear throughout the project. I can also help with documenting the encoding, reward design, and usage, ensuring the final result meets your expectations. I'd love to chat about your project! The worst that can happen is you walk away with a free consultation. Regards, JaniceR92

@birukarg

Hello, I am an Electrical and Computer Engineer with strong experience in MATLAB, Simulink, control systems, and power electronics. I can develop the hybrid PV–battery–buck converter model and implement the offline tabular Q-learning controller for closed-loop voltage regulation. I can deliver the Simulink model, transition dataset, trained Q-table, MATLAB Function implementation, and physics-informed reward/state features based on the buck converter equations. I will also test the controller under varying irradiance, load, and battery SOC conditions and provide clear documentation of the state encoding and reward design. I can complete the project within 5 days and will provide organized, editable MATLAB/Simulink files. Best regards, Biruk

@alonsojosuep

Hi,I can design, train, and validate the offline tabular Q-learning controller for your hybrid PV–battery–buck system in MATLAB/Simulink by building a complete Simscape/Power Systems model, generating the transition dataset under varying irradiance, load, and battery SOC, and embedding a greedy Q-table controller directly into a MATLAB Function block for real-time closed-loop voltage regulation. To enhance performance, I will integrate physics-informed state features and a custom reward function derived from the buck converter's differential equations to minimize voltage ripple, deliver robust tracking under dynamic conditions, and supply the complete .slx file, offline training scripts , simulation plots, and concise documentation covering state encoding and reward design.

@sophies51

You may worry an offline tabular Q-learning setup won’t generalize across irradiance, load, and battery SOC. I’ll build a complete MATLAB/Simulink hybrid PV–battery–buck plant and generate transition data to train a stable Q-table for output-voltage regulation. Phase 1 includes modeling PV/BMS/SOC dynamics, buck converter and load, logging (state, action, next state, reward), training the table offline, and implementing a greedy Q-table policy inside a MATLAB Function block for closed-loop control. Phase 2 strengthens learning with physics-informed design: I’ll embed buck converter relationships into state features and the reward (e.g., voltage error terms shaped by expected inductor/capacitor behavior), improving robustness under parameter variation. You’ll receive the trained Q-table, the offline dataset, a Simulink model ready to run scenarios, a regulation demonstration under varying irradiance/load/SOC, and brief documentation explaining encoding, reward shaping, and how to execu

@riob39

I will start by building the full hybrid PV–battery–buck Simulink model: PV module + battery SOC dynamics + buck converter states and switches, then validate voltage/current waveforms against expected converter behavior. Next, I will generate an offline transition dataset by sweeping irradiance, load, and SOC, discretizing the state/action spaces, and logging (s,a,r,s’) for Q-learning. After training the tabular Q-table, I will implement a MATLAB Function block that uses the greedy policy to regulate converter output voltage in closed loop. In phase 2, I will make the RL “physics-informed” by embedding buck converter equations into both state features and the reward. Concretely, the reward will penalize voltage error and overshoot while rewarding consistency with inductor capacitor dynamics under candidate duty cycles.

@stephenw26

Voltage regulation in a hybrid PV–battery–buck converter breaks down fast when irradiance, load, and SOC swing. I’ll build your MATLAB/Simulink plant model and offline Q-learning workflow so the controller learns stable greedy actions from realistic transitions, then enforces output voltage in closed loop via a MATLAB Function block. You’ll get a complete hybrid PV–battery–buck Simulink system, logged transition dataset, trained Q-table, and a working greedy policy that tracks the reference across conditions. Phase 2 will make the learning physics-aware: I’ll incorporate buck converter equations into the state features and reward terms (e.g., penalizing voltage error while respecting converter dynamics), improving convergence and robustness versus purely data-driven reward. I’ll run repeatable training experiments, validate regulation under varying irradiance/load/SOC, and provide brief documentation on state encoding, reward design, and how to run the controller in your model.