Intelligent Systems Engineering

The Intelligent Systems Engineering Research Team is an academic research team within the School of Physics, Engineering, and Computer Science at the University of Hertfordshire (UH). Led by Dr Pouria Sarhadi, the ISE team is dedicated to advancing the frontiers of intelligent systems through cutting-edge research and innovation.

Research Themes and Areas

Autonomous Systems
Autonomous Vehicles

Autonomous Vehicles (AVs) are systems that operate without (or with minimal) human intervention, making independent decisions to carry out predetermined missions. The ISE team specialises in various aspects of AVs and has achieved success in the following areas:

  • System design of autonomous marine, aerial, and ground vehicles;
  • Motion planning and decision-making algorithms;
  • Collision avoidance;
  • Navigation and perception;
  • Control algorithms;
  • Machine learning in decision-making;
  • Non-conventional AVs;
  • Teleoperation and shared autonomy with humans;
  • Intelligent transportation systems.
Intelligent Control Systems
Intelligent Control Systems

The ISE team holds experience in developing theoretical and applied intelligent control solutions in the following areas:

  • Adaptive control systems;
  • Machine learning in control;
  • System identification and state estimation;
  • Modelling and simulation;
  • Energy optimisation;
  • Hardware/software/Processor -in-the-loop (HIL/SIL/PIL) testing platforms'
  • Sensors and actuators.
Systems Engineering
Systems Engineering

Systems engineering is a holistic approach that ensures the successful delivery of a system through requirements analysis, system development, integration, verification, and validation, while also managing functions and resources throughout the entire system lifecycle. The ISE group offers research interests in the following areas:

  • System requirements analysis;
  • Verification and Validation (V&V);
  • Test design;
  • System effectiveness and value models;
  • System maturity level analysis (TRL, SRL).

Our Team

Team Lead

Dr Pouria Sarhadi

Dr Pouria Sarhadi

Pouria Sarhadi (BEng, MSc and PhD) is a Senior Lecturer (A. Professor) in the School of Physics, Engineering & Computer Science at the University of Hertfordshire. He has worked as a research fellow at Queen's University Belfast (2021-2022), University of Surrey (2019-2021) and as an adjunct professor at the Babol Noshirvani University of Technology BNUT (2016-2018). He has held key roles to play in the UK Research and Innovation (UKRI) and European Horizon projects and provided consultancy to different industries. He is a Chartered Engineer (CEng), Member of IET (MIET), and also recognised as a UK 'Global Talent' by the UKRI. In the past 15 years, he has conducted research on a wide range of ground, marine, and aerial autonomous vehicles which has resulted in more than 50 peer-reviewed journal publications. Pouria is a member of the International Federation of Automatic Control (IFAC) and the Editorial Board of Applied Ocean Research (Elsevier). Furthermore, he is passionate about transforming academic theories into industrial products. Pouria's research studies include: control theory and applications, adaptive control and machine learning, autonomous vehicles as well as systems engineering.

Current Researchers:

Reza Jafari

Reza Jafari

Reza Jafari holds a BSc and a MSc in Electrical Engineering from K. N. Toosi University of Technology. He is currently pursuing a PhD at the University of Hertfordshire, where his research focuses on improving electric vehicle (EV) dynamics and energy management through advanced reinforcement learning (RL) methods. He has hands-on experience with a range of industry-standard tools, including ANSYS Maxwell and COMSOL Multiphysics for electromagnetic analysis, as well as MATLAB/Simulink and Python for system modelling, control design, and machine learning algorithm development. His research interests encompass sustainable mobility, intelligent energy management, and the integration of renewable energy within transportation systems.

Klinsmann Agyei

Klinsmann Agyei

Klinsmann Agyei holds a BSc in Mechanical Engineering from the University of Energy and Natural Resources and an MSc in Robotics and Artificial Intelligence from ITMO University. He is currently a PhD researcher at the University of Hertfordshire, focusing on advancing adaptive control systems through the integration of machine learning techniques. Klinsmann is proficient in Python, MATLAB, and C++, as well as expertise in CAD tools such as SolidWorks. He is experienced in communication frameworks like ROS 2, enabling seamless interaction with robotic systems. His research interests span adaptive control, data-driven decision-making, and the application of advanced machine learning algorithms to enhance system performance and robustness.

Pegah Janbakhsh

Pegah Janbakhsh

Pegah holds a BEng in Aerospace Engineering and is currently a part-time PhD student at the University of Hertfordshire. Her research focuses on developing amphibious fixed wing Unmanned Aerial Vehicles (UAS) with multi-purpose applications. She investigates of the systems and algorithms for autonomously take off and landing in land and water environments. She is a UAS Pilot and serves as the Principal Technical Officer and Lab Manager for Aerospace, and is also the Safety Lead for the School of Physics, Engineering, and Computer Science (SPECS) at the University of Hertfordshire. Pegah is a Fellow of the Higher Education Academy (FHEA). She is registered as an Incorporated Engineer (IEng) with the Engineering Council and is an Associate Member of the Royal Aeronautical Society (AMRAeS).

Former Researchers:

Sajid Fadlelseed

Dr Sajid Fadlelseed

Sajid Fadlelseed (researcher) has received an MSc and a PhD in computer science from the University of Hertfordshire. His studies were supported by the National University, Sudan (NUSU). His current research interests include mixed-criticality scheduling, real-time embedded systems, fault tolerance, NoC architectures, and optimization techniques. Sajid has lectured modules in embedded systems and fault tolerance algorithms. He has conducted research on DRT services and Software Integrity Levels (SIL).

Hossein Rouzegar

Dr Hossein Rouzegar

Hossein Rouzegar is a researcher at the Space Communication Research Group at the i2CAT Foundation in Barcelona, Spain where he leads the Software-defined Satellite research line. He earned his BSc in Electrical Engineering in 2011, MSc in Electrical Engineering and Control in 2014, in Iran. In 2020, he completed his PhD in Electrical Engineering at Babol Noshirvani University of Technology, Babol with a focus on spacecraft formation flying control. In 2018, he served as a visiting researcher at the Universitat Politècnica de Catalunya (UPC), collaborating with the Institute of Space Studies of Catalonia (IEEC) in Barcelona. From 2022 to 2023, he was an assistant professor at Semnan University, Iran. His research interests include spacecraft formation flying control, optimal control, satellite networks, SDN/NFV in satellite constellations, and satellite flexible payload technologies.

Javad Enayati

Dr Javad Enayati

Javad Enayati is currently R&D manager of Sander Elektronik AG in Switzerland specialising in intelligent emergency systems. He received a BSc degree in power electrical engineering from the Nooshirvani University Of technology, in 2009 an MSc and a PhD degrees in power electrical engineering from University of Semnan, in 2012 and 2017, respectively. His research interests include embedded system development, state estimation, system identification, wireless mesh systems, and automotive systems reliability.

Narjes Ahmadian

Dr Narjes Ahmadian

Narjes Ahmadian is currently an Assistant Professor at the Department of Electrical Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran. She received a BSc degree in Electrical Engineering from Guilan University, Rasht, Iran, in 2012, and the MSc and PhD degrees in Control Engineering from Babol Noshirvani University of Technology, Babol, Iran, in 2015 and 2020, respectively. Her PhD research was focused on multilayer integrated adaptive controllers for vehicle lateral dynamics and steering system. She was an exemplary student in all her academic levels. Dr. Ahmadian has authored several peer-reviewed journal and conference papers. She has established two research laboratories and participated in the navigation research team's setup projects. Her research interests include Adaptive control, vehicle dynamics, navigation, and aerospace.

Behnaz Hadi

Dr Behnaz Hadi

Behnaz Hadi (BEng, MSc, PhD) is a lecturer at the University of Guilan, Rasht, Iran. She received her PhD degree in control engineering from the Babol Noshirvani University of Technology, Babol, Iran, in 2022. She has conducted research in the areas of motion planning, formation control and obstacle avoidance in marine systems, as well as deep reinforcement learning with control applications. Her research interests include control theory and applications, machine learning in control applications, autonomous vehicles, and nonlinear control.

Hock C Gan

Dr Hock C Gan

Hock C Gan (researcher) holds a PhD in Computer Science from the University of Hertfordshire. He is a researcher in various projects and a Visiting Lecturer in the University of Hertfordshire for the School of Engineering and the School of Computer Science. His interests include autonomous systems, machine learning, computer vision, blockchains, IoT security, voice discrimination, brain computer interfacing, software engineering and telecommunications.

Research Projects

Active Project

Risk-Aware Motion Planning and Control of Maritime Autonomous Surface Ships (MASS)

Collision avoidance is a critical component of mission planning in all autonomous vehicles. The decision-making algorithms in MASS must adhere to the COLREGs (Convention on the International Regulations for Preventing Collisions at Sea) for obstacle avoidance and manoeuvres.

However, implementing COLREGs within MASS presents significant challenges. The textual and somewhat ambiguous nature of COLREGs, which were primarily designed for human operators, has long been a barrier to effective algorithmic implementation. Recent advancements in explainable Artificial Intelligence (AI) and machine learning show promise in enabling human-like decision-making.

This research investigates AI-based, risk-aware motion planning and collision avoidance algorithms for MASS.

The developed algorithms integrate state-of-the-art AI solutions with classical and resilient local planning and control mechanisms, incorporating Line of Sight (LOS) and Cross Track Error (CTE) for optimal waypoint tracking and disturbance rejection. For the first time, the application of Large Language Models (LLMs) is proposed to ensure adherence to COLREGs. The proposed decision-making system introduces an innovative online risk index, considering Distance and Time to Closest Point of Approach (DCPA and TCPA), alongside other relevant indices, to maintain safe operational behaviour. With further development, text-to-rule-based decision-making algorithms hold promise in enhancing the safety of MASS. This research is in its early stages but has the potential to revolutionise safety in MASS and related applications. Plans are underway for the formal verification of the proposed algorithms.

MASS Project
Completed Project

HertsLynx CAM (Connected & Automated Mobility) on Demand

HertsLynx CAM on Demand was a funded project by Innovate UK, the Centre for Connected and Autonomous Vehicles (CCAV), and delivered in collaboration with Sustainicity, Siemens Mobility (PADAM), Hertfordshire County Council, and Fusion Processing.

The project analysed the potential for autonomous bus mass transit services in the Maylands Business Park region of Hertfordshire. The study focused on identifying viable routes capable of accommodating current or near-term AV technologies without requiring significant infrastructure changes.

The objective was to unlock the potential for CAM applications in similar environments. Our role involved enabling the application of System of Systems (SoS) analysis to related areas. An innovative System Readiness Level (SRL) analysis approach was applied to intelligent transportation systems, encompassing AVs, connectivity systems, fleet and traffic management, as well as user interface and booking systems.

HertsLynx Project
Completed Project

Remotely Driven Car System Design and Algorithm Development

Remotely driven vehicles, leveraging network-based teleoperation and advances in new-generation networks (4G+), enable car teleoperation over long distances. This technology has two primary applications: serving as a fallback for autonomous vehicles and supporting car-sharing services. As part of an industrial collaboration (consultancy), this project focused on developing and implementing novel delay-aware controllers for remotely driven vehicles

The project contributed to system design through simulation, algorithm development, and testing. Key achievements included specialised driver modelling, teleoperation simulations under varying delays, Driver-in-the-Loop simulator design, customised Adaptive Cruise Control (ACC) systems, safety analysis, and delay-aware control algorithms. These algorithms were implemented and successfully tested on various categories of electric vehicles.

RDS Project
Completed Project

AI-based Adaptive Formation Motion Planning and Control of Autonomous Underwater Vehicles (AUVs)

Collaborative operation of AUVs can enhance performance, reduce costs, and shorten mission time across various applications, including search and rescue, oceanography, disaster recovery, and pipeline or cable monitoring. As a result, collaborative formation planning and control systems have always been key objectives in autonomous vehicle development.

This project, a pioneer in the field, introduced novel approaches to leverage AI for reward-based, model-free autonomous planning and control algorithms. Various Deep Reinforcement Learning (DRL) solutions were developed to tackle complex (collaborative and formation) tasks, demonstrating robust performance in the presence of external disturbances such as ocean currents, localisation errors, communication delays, and dynamic behaviour variations.

AI_AUV Project
Active Project

AI-based Energy Optimisation and Lateral Stability of All-Wheel Drive Electric Vehicles

Recent advancements in Electric Vehicles (EVs) present new opportunities through in-wheel drive motors, enabling all-wheel drive more efficiently. This advancement allows for the application of novel algorithms to enhance energy efficiency and vehicle stability using advanced control approaches.

As part of an ongoing project, this study explores innovative DRL-based (Deep Reinforcement Learning) control and online energy optimisation algorithms for all-wheel drive EVs, aiming to improve performance and reduce energy consumption.

EV_AI Project
Completed Project

Automated Parking of Articulated Vehicles

Motion planning and control of truck-trailers is a challenging task due to operational challenges such as unstable, non-minimum phase behaviour during reverse manoeuvres and the risk of jackknifing.

As part of an industrial collaboration, this project focused on the development and testing of automated parking algorithms for truck-trailers. For this purpose, trajectory planning and control algorithms were designed and hierarchically tested using simulation software (IPG CarMaker), Hardware-in-the-Loop (HiL), and a real 13-tonne vehicle.

Auto Parking Project
Completed Project

Model Reference Adaptive Control with Dynamical Anti-Windup Compensators for AUVs

Actuator saturation and parametric uncertainties have always been significant challenges in the control of autonomous vehicles. Model reference adaptive control (MRAC) has traditionally been a solution for addressing these uncertainties. However, failing to properly consider input limitations has led to several catastrophic failures in the history of control engineering.

This research had two main objectives: to propose the first MRAC with dynamical (non-static) anti-windup compensators, along with corresponding stability analysis, and to explore the practical considerations and implementation of the algorithms on AUVs. Two main configurations, enhanced PID adaptive control and LQR-based adaptive controllers, were developed to address the aforementioned issues. The proposed solutions demonstrated suitable performance and the ability to handle both uncertainty and saturation in dynamic and challenging environments, attracting considerable attention within the research community.

MRAC Project

Recent Publications

2025:

  • P. Sarhadi, "On the Standard Performance Criteria for Applied Control Design: PID, MPC or Machine Learning Controller?", arXiv preprint arXiv:2503.14379, 2025.
  • K. Agyei, P. Sarhadi, W. Naeem, "Large Language Model-based Decision-making for COLREGs and the Control of Autonomous Surface Vehicles", Accepted in European Control Conference (ECC), 2025.
  • K. Agyei, P. Sarhadi, W. Naeem, "CORALL: A COLREGs-Guided Risk-Aware LLM for Decision-Making in Autonomous Surface Vehicles", Submitted to IEEE Transaction on 2025.
  • S. Fadleseed, P. Sarhadi, et. al., "Recent Advances in Demand Responsive Transport: Opportunities with Autonomous Bus Service - A System-of-Systems Overview", Submitted to IEEE Transaction on X, Sep 2025.

2024:

  • B. Hadi, A. Khosravi, P. Sarhadi, "Cooperative motion planning and control of a group of autonomous underwater vehicles using twin-delayed deep deterministic policy gradient", Applied Ocean Research, June 2024, Vol: 147, pp: 103977.
  • B. Hadi, A. Khosravi, P. Sarhadi, "Adaptive formation motion planning and control of autonomous underwater vehicles based on deep reinforcement learning", IEEE Journal of Oceanic Engineering, Jan 2024, Vol: 49(1), pp: 311-328.
  • B. Hadi, A. Khosravi, P. Sarhadi, "Hybrid motion planning and formation control of multi-AUV systems based on DRL", American Control Conference ACC 2024, Toronto, Canada, pp: 2368-2373.
  • P. Thomas, P. Sarhadi, "Geofencing motion planning for unmanned aerial vehicles using an anticipatory range control algorithm", Machines, Jan. 2024, Vol: 12(1), pp: 36.
  • B. Clement, T. Chaffre, P. Sarhadi, M. Dubromel, "COLSim, a simulator for Hybrid Navigation Acceptability and Safety", 15th IFAC Conference on Control Applications in Marine Systems, 2024, Virginia, USA, 58(20), 147-152.
  • B. Hadi, A. Khosravi, P. Sarhadi, B. Clement, A. Memarzadeh, "Learning-based integrated cooperative motion planning and control of multi-AUVs", 15th IFAC Conference on Control Applications in Marine Systems, 2024, Virginia, USA, 58(20), 287-292.
  • F. Tavakkoli, P. Sarhadi, W. Naeem and B. Clement, "Model Free Deep Deterministic Policy Gradient Controller for Setpoint Tracking of Non-minimum Phase Systems", UKACC 14th International Conference on Control (CONTROL), 2024, Winchester, UK, pp: 1-6.
  • H. Raeesi, A. Khosravi, P. Sarhadi, "Collision avoidance for autonomous vehicles using reachability-based trajectory planning in highway driving", Proceedings of the IMechE, Part D: Journal of Automobile Engineering, Feb. 2024, Article in Press.
  • R. Jafari, P. Sarhadi, A. Paykani, S. S. Refaat, P. Asef, "Optimal Torque Allocation for All-Wheel-Drive Electric Vehicles Using a Reinforcement Learning Algorithm", 13th Mediterranean Conference on Embedded Computing (MECO), Budva, Montenegro, 2024, pp. 1-5.

Pre-2024:

  • B. S.M. Daniali, A. Khosravi, P. Sarhadi, F. Tavakkoli, "An Automatic Parking Algorithm Design Using Multi-Objective Particle Swarm Optimization", IEEE Access, 2023, Vol: 11, pp. 49611-4962.
  • R. Jafari, P. Asef, P. Sarhadi, X. Pei, "Optimal gear ratio selection of linear primary permanent magnet vernier machines for wave energy applications", IET Renewable Power Generation, Nov. 2023, Vol: 17(16), pp: 3856-3871.
  • B. Hadi, A. Khosravi, P. Sarhadi, "Deep reinforcement learning for adaptive path planning and control of an autonomous underwater vehicle", Applied Ocean Research, December 2022, Vol: 129, pp: 103326.
  • P. Sarhadi, W. Naeem and N. Athanasopoulos, "A survey of recent machine learning solutions for ship collision avoidance and mission planning", 14th IFAC Conference on Control Applications in Marine Systems, Robotics and Vehicles, Kongens Lyngby, Denmark, Sept. 2022, Elsevier, 2022, Vol: 55(31), pp: 257-268.
  • P. Sarhadi, W. Naeem, K. Fraser, and D. Wilson, "On the application of Agile project management techniques, v-model and recent software tools in postgraduate theses supervision", IFAC Symposium on Advances in Control Education 2022, Hamburg Bergedorf, Germany, July 2022, Elsevier, 2022, Vol: 55(17), pp: 109-114.
  • B. Hadi, A. Khosravi, P. Sarhadi, B. Clement, A. Memarzadeh, "Learning-based integrated cooperative motion planning and control of multi-AUVs", 15th IFAC Conference on Control Applications in Marine Systems, 2024, Virginia, USA, 58(20), 287-292.
  • F. Tavakkoli, P. Sarhadi, W. Naeem and B. Clement, "Model Free Deep Deterministic Policy Gradient Controller for Setpoint Tracking of Non-minimum Phase Systems", UKACC 14th International Conference on Control (CONTROL), 2024, Winchester, UK, pp: 1-6.
  • H. Raeesi, A. Khosravi, P. Sarhadi, "Collision avoidance for autonomous vehicles using reachability-based trajectory planning in highway driving", Proceedings of the IMechE, Part D: Journal of Automobile Engineering, Feb. 2024, Article in Press.
  • P. Sarhadi, W. Naeem and N. Athanasopoulos, "An integrated risk assessment and collision avoidance methodology for an autonomous catamaran with fuzzy weighting functions", UKACC 13th International Conference on Control (CONTROL), Plymouth, UK, April 2022, IEEE, pp: 228-234.
  • B. Hadi, A. Khosravi, P. Sarhadi, "A review of the path planning and formation control for multiple autonomous underwater vehicles", Journal of Intelligent Robotic Systems, April 2021, Vol: 101, No: 4, pp: 1-26.
  • N. Ahmadian, A. Khosravi, P. Sarhadi, "Driver Assistant Yaw Stability Control via Integration of AFS and DYC", Vehicle System Dynamics, 2022, Vol: 60(5), pp: 1742-1762.
  • H. Rouzegar, A. Khosravi, P. Sarhadi, "Spacecraft formation flying control around L2 sun-earth libration point using on–off SDRE approach", Advances in Space Research, April 2021, Vol: 67, No: 7, pp: 2172-2184.
  • S. Moghimi Rad, A. Khosravi, P. Sarhadi, "Pitch autopilot design for an autonomous aerial vehicle in the presence of amplitude and rate saturation", Aerospace Science and Technology, January 2021, Vol: 108, pp: 106371.
  • N. Ahmadian, A. Khosravi, P. Sarhadi, "Managing driving disturbances in lateral vehicle dynamics via adaptive integrated chassis control", Proceedings of the IMechE, Part K: Journal of Multi-body Dynamics, March 2021, Vol: 235, No: 1, pp: 122-133.
  • N. Ahmadian, A. Khosravi, P. Sarhadi, "Adaptive yaw stability control by coordination of active steering and braking with an optimized low-level controller", International Journal of Adaptive Control and Signal Processing, September 2020, Vol: 34, No: 9, pp: 1242-1258.
  • N. Ahmadian, A. Khosravi, P. Sarhadi, "Integrated model reference adaptive control to coordinate active front steering and direct yaw moment control", ISA Transactions, November 2020, Vol: 106, pp: 85-96.
  • H. Rouzegar, A. Khosravi, P. Sarhadi, "Vibration suppression and attitude control for the formation flight of flexible satellites by optimally tuned on-off SDRE approach", Transactions of the Institute of Measurement and Control, November 2020, Vol: 42, No: 15, pp: 2984-3001.
  • A. Yousefimanesh, A. Khosravi, P. Sarhadi, "Composite adaptive posicast controller for the wing rock phenomenon in a delta-wing aircraft", Proceedings of the IMechE, Part G: Journal of Aerospace Engineering, December 2019, Vol: 233, No: 15, pp: 5579-5591.
  • H. Rouzegar, A. Khosravi, P. Sarhadi, "Spacecraft formation flying control under orbital perturbations by state dependent Riccati equation method in the presence of on-off actuators", Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, June 2019, Vol: 233, No: 8, pp: 2853-2867.
  • P. Sarhadi, A. Ranjbar Noei, A. Khosravi, "Model reference adaptive autopilot with anti-windup compensator for an autonomous underwater vehicle: Design and hardware in the loop implementation results", Applied Ocean Research, Januray 2017, Vol: 62, pp: 27-36.
  • N. Ahmadian, A. Khosravi, P. Sarhadi, "Adaptive control of a jet turboshaft engine driving a variable pitch propeller using multiple models", Mechanical Systems and Signal Processing, August 2017, Vol: 79, pp: 1-12.
  • P. Sarhadi, A. Ranjbar Noei, A. Khosravi, "Adaptive mu-modification control for a nonlinear autonomous underwater vehicle in the presence of actuator saturation", International Journal of Control and Dynamics, September 2017, Vol: 5, No:3, pp: 596-603.
  • P. Sarhadi, A. Ranjbar Noei, A. Khosravi, "Adaptive integral feedback controller for pitch and yaw channels of an AUV with actuator saturations", ISA Transactions, November 2016, Vol: 65, pp: 284–295.
  • P. Sarhadi, A. Ranjbar Noei, A. Khosravi, "Model reference adaptive PID control with anti-windup compensator for an autonomous underwater vehicle", Robotics and Autonomous Systems, September 2016, Vol: 83, pp: 87-93.
  • J. Enayati, P. Sarhadi, M. Poyan Rad, M. Zarrini, "Monte Carlo Simulation Method for Behavior Analysis of an Autonomous Underwater Vehicle", Proceedings of the IMechE, Part M: Journal of Engineering for the Maritime Environment, August 2016, Vol: 230, No:3, pp: 481-490.
  • P. Sarhadi, A. Khosravi, "Tuning of Pulse-Width Pulse-Frequency Modulator Using PSO: An Engineering Approach for Spacecraft Attitude Controller Design", Automatika, June 2016, Vol: 57, No:1, pp: 212-220.
  • P. Sarhadi, B. Rezaei, Z. Rahmani, "Adaptive Predictive Control Based on Adaptive Neuro-Fuzzy Inference System for A Class of Nonlinear Industrial Processes", Journal of the Taiwan Institute of Chemical Engineers, April 2016, Vol: 61, pp: 132-137.
  • P. Sarhadi, R. Nad Alinia Chari, M. Pouyan Rad, J. Enayati "Hardware in the Loop Simulation for Real-Time Software Verification of an Autonomous Underwater Robot", International Journal of Intelligent Unmanned Systems, March 2016, Vol: 4, No: 3, pp: 163-181.
  • P. Sarhadi, S. Yousefpour, "State of the Art: Hardware in the Loop Modeling and Simulation with its Applications in Design, Development and Implementation of System and Control Software", International Journal of Control and Dynamics, December 2015, Vol: 3, No:4, pp: 470-479.
  • N. Ahmadian, A. Khosravi, P. Sarhadi, "A New Approach to Adaptive Control of Multi-Input Multi-Output Systems using Multiple Models", ASME Journal of Dynamic Systems, Meas. and Control, September 2015, Vol: 137, No:9, pp: 1-10.
  • K. Salahshoor, A. Khaki-Sedigh, P. Sarhadi, "An Indirect Adaptive Predictive Control for the Pitch Channel Autopilot of a Flight System", Aerospace Science and Technology, September 2015, Vol: 45, pp: 78-87.
  • P. Sarhadi, A. Khosravi, V. Bijani, "Identification of nonlinear actuators with time delay and rate saturation using meta-heuristic optimization algorithms", Proceedings of the IMechE, Part I: Journal of Systems and Control Engineering, October 2015, Vol: 229, No:9, pp: 808-817.
  • A. Khosravi, Z. Lachini, P. Sarhadi, "Predictor-Based Model Reference Adaptive Control for a Vehicle Lateral Dynamics Considering Uncertainties", Proceedings of the IMechE, Part I: Journal of Systems and Control Engineering, October 2015, Vol: 229, No:9, pp: 797-807.
  • N. Ahmadian, A. Khosravi, P. Sarhadi, "Multiple Model Adaptive Control Using Second Level Adaptation for a Delta-wing Aircraft ", International Journal of Intelligent Unmanned Systems, March 2015, Vol: 3, No: 1, pp: 2-17.
  • P. Sarhadi, K. Salahshoor and A. Khaki-Sedigh, "Application of Augmented UD Identification with Selective Forgetting", International Journal of Modelling Identification and Control (IJMIC), February 2014, Vol:21, No:1, pp: 54-64.
  • P. Sarhadi, A. Ranjbar Noei, A. Khosravi, "L1 Adaptive Pitch Channel Control of an Autonomous Underwater Vehicle Considering Practical Uncertainties", International Journal of Intelligent Unmanned Systems, June 2014, Vol: 2, No: 2, pp: 107-120.
  • M. T. Sabet, P. Sarhadi and M. Zarini, "Extended and Unscented Kalman Filters for Parameter Estimation of an Autonomous Underwater Vehicle", Ocean Engineering, November 2014, Vol: 91, pp: 329-339.
  • P. Sarhadi, K. Salahshoor and A. Khaki-Sedigh, "Robustness Analysis and Tuning of Generalized Predictive Control Using Frequency Domain Approaches", Applied Mathematical Modelling, November 2012, Vol 36, pp:6167-6185.

Contact Us