I have been keen with distributed estimation, control and dynamic on large-scale systems, robust control, fault-tolerance control & fault-detection and optimization. I have been with laboratory of embedded and cyber-physical systems, Department of Engineering Physics, Institut Teknologi Sepuluh Nope
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Scopus Publications
Scopus Publications
Robust Stability Analysis of Positive Lur'e System with Neural Network Feedback Hamidreza Montazeri Hedesh, Moh Kamalul Wafi, Bahram Shafai, Milad Siami 2025 IEEE Conference on Control Technology and Applications Ccta 2025, 2025 This paper investigates the robustness of the Lur'e problem under positivity constraints, drawing on results from the positive Aizerman conjecture and robustness properties of Metzler matrices. Specifically, we consider a control system of Lur'e type in which not only the linear part includes parametric uncertainty, but also the nonlinear sector bound is unknown. We investigate tools from positive linear systems to effectively solve the problems in complicated and uncertain nonlinear systems. By leveraging the positivity characteristic, we derive an explicit formula for the stability radius of Lur'e systems. Furthermore, we extend our analysis to systems with neural network (NN) feedback loops. Moreover, we propose a method for obtaining tight sector bounds for NNs. This study introduces a scalable and efficient approach for robustness analysis of both Lur'e and NN-controlled systems. Finally, the proposed results are supported by illustrative examples.
Model reference adaptive control of networked systems with state and input delays Moh Kamalul Wafi, Katherin Indriawati, Bambang L. Widjiantoro International Journal of Electrical and Computer Engineering, 2024 Adaptive control strategies have been developed in response to more advanced complex systems and to deal with uncertain systems while maintaining the desired conditions. This paper addresses the networked unknown and unstable heterogeneous systems following a stable reference (leader), which is related to network synchronization. We deliver two different scenarios; each agent both fully communicates to the leader and shares communication among neighborhood agents and the leader. The communication among agents and the leader are weighted using Laplacian-like matrix and the model weight matrix in turn. Also, the state and input delays are induced to the systems to capture the real limited communication while the prediction of the reference signals and the augmented systems are proposed to deal with them. Moreover, the rigorous mathematical foundations of two adaptive laws, the stability analysis, the threshold of network, and the communication network are thoroughly presented. Also, the numerical illustrations of the two scenarios are given to show the effectiveness of the proposed method in the networked system. The results show that for both scenarios working on the required setting, the perfect tracking to the leader is guaranteed. Beyond that, the future research would implement the distributed adaptive control-oriented learning of networked system under some faults.
Distributed Adaptive Control of Disturbed Interconnected Systems with High-Order Tuners Moh. Kamalul Wafi, Milad Siami IEEE Control Systems Letters, 2024 This paper addresses the challenge of network synchronization under limited communication, involving heterogeneous agents with different dynamics and various network topologies, to achieve consensus. We investigate the distributed adaptive control for interconnected unknown linear subsystems with a leader and followers, in the presence of input-output disturbance. We enhance the communication within multi-agent systems to achieve consensus under the leadership's guidance. While the measured variable is similar among the followers, the incoming measurements are weighted and constructed based on their proximity to the leader. We also explore the convergence rates across various balanced topologies (Star-like, Cyclic-like, Path, Random), featuring different numbers of agents, using three distributed algorithms, ranging from first- to high-order tuners to effectively address time-varying regressors. The mathematical foundation is rigorously presented from the network designs of the unknown agents following a leader, to the distributed methods. Moreover, we conduct several numerical simulations across various networks, agents and tuners to evaluate the effects of sparsity in the interaction between subsystems using the $L_2-$norm and $L_\\infty-$norm. Some networks exhibit a trend where an increasing number of agents results in smaller errors, although this is not universally the case. Additionally, patterns observed at initial times may not reliably predict overall performance across different networks. Finally, we demonstrate that the proposed modified high-order tuner outperforms its counterparts, and we provide related insights along with our conclusions.
Investigating the Effectiveness of Reinforcement Learning in Closed-Loop Systems with Time Delays Moh Kamalul Wafi, Milad Siami, Mario Sznaier Proceedings of the American Control Conference, 2024 Data-driven controllers have gained prominence in diverse control applications, attributed to their inherent flexibility and adaptability to complex system dynamics. However, managing time delays in closed-loop systems remains a significant challenge in their deployment. These delays can arise from various sources, such as computational latency, actuator reaction time, and communication delays. Unaddressed, these time lags can induce system instability and degrade performance. This paper rigorously analyzes the impact of time delays on data-driven controllers and introduces methodologies to mitigate their adverse effects. Specifically, we explore the integration of the Smith predictor with Deep Reinforcement Learning (SP-DRL) to formulate a control law capable of effectively managing both time delays and system uncertainties, while ensuring robust performance. We demonstrate that this DRL-based framework, initially trained in stable environments, generalizes well to unstable systems. Our investigation delineates the scenarios conducive to the successful application of this approach and identifies factors influencing its effectiveness. To substantiate our findings, we present a case study involving a first-order delayed linear system with nonlinear actuation modules. Numerical simulations are employed to compare the robustness of SP-DRL scheme against the DRL standalone and the classical controls, such as PID and Linear Quadratic Regulator (LQR), in the presence of delays.
A Comparative Analysis of Reinforcement Learning and Adaptive Control Techniques for Linear Uncertain Systems 2023 SIAM Conference on Control and Its Applications CT 2023, 2023
Non-Linear Estimation using the Weighted Average Consensus-Based Unscented Filtering for Various Vehicles Dynamics towards Autonomous Sensorless Design B. L. Widjiantoro, M. Wafi, K. Indriawati Journal of Robotics and Control Jrc, 2023 The concerns to autonomous vehicles have been becoming more intriguing in coping with the more environmentally dynamics non-linear systems under some constraints and disturbances. These vehicles connect not only to the self-instruments yet to the neighborhoods components, making the diverse interconnected communications which should be handled locally to ease the computation and to fasten the decision. To deal with those interconnected networks, the distributed estimation to reach the untouched states, pursuing sensorless design, is approached, initiated by the construction of the modified pseudo measurement which, due to approximation, led to the weighted average consensus calculation within unscented filtering along with the bounded estimation errors. Moreover, the tested vehicles are also associated to certain robust control scenarios subject to noise and disturbance with some stability analysis to ensure the usage of the proposed estimation algorithm. The numerical instances are presented along with the performances of the control and estimation method. The results affirms the effectiveness of the method with limited error deviation compared to the other centralized and distributed filtering. Beyond these, the further research would be the directed sensorless design and fault-tolerant learning control subject to faults to negate the failures.
Advancing Fault-Tolerant Learning-Oriented Control for Unmanned Aerial Systems Moh Kamalul Wafi, Rozhin Hajian, Bahram Shafai, Milad Siami 9th 2023 International Conference on Control Decision and Information Technologies Codit 2023, 2023 The rapid advancement of automatic control technology has sparked significant interest among researchers in creating more reliable and simplified models of unmanned aerial vehicles (UAVs). This interest is motivated by the need to enhance the performance and resilience of these systems in challenging conditions, such as wind gusts and adverse weather. This paper presents novel strategies for enhancing the resilience of unmanned aerial systems (UAS) with fault-tolerant control (FTC) by learning-oriented control and a constructive fault estimation with Proportional-Integral (PI) observer. The learning-control is deep-deterministic policy gradient (DDPG) which is trained in only one state but used beyond its environment for other states to control. The faults are designed in three divergent conditions and the augmented PI observer is responsible in capturing them. The success of estimating the faults is used for this FTC to compensate the faulty system with learning-oriented control as the advancement of the FTC. The proposed approach has the potential to enhance the performance and resilience of UAVs, thus contributing to the development of more robust and reliable systems.
Filtering module on satellite tracking Moh Kamalul Wafi Aip Conference Proceedings, 2019 The scope of satellite has increasingly attained as one of the most challenging topics due to the attraction of elaborating the outer space. The satellite, as a means of collecting data and communicating, needs a proper calculation so as to maintain the movement and its appearance. The concept of the proposed research lies in the mathematical model along with certain noises. The mathematical model is started by initial two variable states, constituting a radius and an angle, with no process noise on it. These two states then are formulated with certain assumption of noises in terms of the range and the scaled angle deviations from them in turn. Keep in mind that those two noises are mutually-independent and their covariance are considered. the model is defined as Algebraic Riccati Equation (ARE) along with Kalman filter algorithm, from the estimation, the steady-state estimator, the computational of gain matrix to the stability of the predictor. The findings show that, as for the two pairs of states, the performance of the estimation can follow the state with just slight fluctuations in the first a fifth of a thousand iterations. With respect to the Mean Square Error (MSE), both noises are around 0.2 for the four states.The scope of satellite has increasingly attained as one of the most challenging topics due to the attraction of elaborating the outer space. The satellite, as a means of collecting data and communicating, needs a proper calculation so as to maintain the movement and its appearance. The concept of the proposed research lies in the mathematical model along with certain noises. The mathematical model is started by initial two variable states, constituting a radius and an angle, with no process noise on it. These two states then are formulated with certain assumption of noises in terms of the range and the scaled angle deviations from them in turn. Keep in mind that those two noises are mutually-independent and their covariance are considered. the model is defined as Algebraic Riccati Equation (ARE) along with Kalman filter algorithm, from the estimation, the steady-state estimator, the computational of gain matrix to the stability of the predictor. The findings show that, as for the two pairs of states, the ...
