Alireza Nezamzadeh

Hysteresis nonlinearities, parametric uncertainties, and external disturbances substantially impair the performance and energy efficiency of piezoelectric actuators (PEAs), which are extensively utilized in high-precision positioning and energy-sensitive actuation systems. Conventional model-based control strategies depend on accurate hysteresis modeling and parameter identification, which are often difficult to accomplish and may deteriorate under varying operating conditions. By contrast, data-driven methods usually focus on tracking performance without making sure that closed-loop stability or energy-aware operation is always present. This paper introduces a novel gray-box modeling and control framework designed to address the challenges associated with piezoelectric actuator systems. It integrates a brain emotional learning-based intelligent controller (BELBIC) with a fuzzy extended state observer (FESO). The BELBIC component learns about unknown nonlinear dynamics in real time, eliminating the need for explicit hysteresis modeling or precise parameter identification. Simultaneously, the FESO estimates unmeasured system states and aggregates disturbances related to hysteresis as they occur. This approach enables effective disturbance compensation without requiring hysteresis inversion. A comprehensive Lyapunov-based stability analysis is conducted for the entire BELBIC–FESO closed-loop system, considering the interactions between the learning mechanism, observer dynamics, and control law. Results from both simulations and tests on a real piezoelectric actuator show that it can track accurately, reduce hysteresis effectively, be more resistant to uncertainties, and use less energy. This is shown by lower control oscillations and optimized actuation effort.

This paper presents a unified framework for reliable motion control of permanent magnet linear motors (PMLMs) by integrating fault detection (FD) and fault-tolerant control (FTC). The framework combines a brain emotional learning-based intelligent controller (BELBIC) with a linear extended state observer (LESO) to enable rapid detection and mitigation of abrupt and incipient faults, as well as disturbances and sensor noise that degrade tracking accuracy and system reliability. The LESO is employed to estimate unknown dynamics and lumped disturbances and to generate residuals for reliable fault detection, while BELBIC provides adaptive and robust control actions without requiring prior knowledge of system parameters or explicit fault models. Extensive simulation studies under actuator faults, system dynamics faults, external disturbances, and measurement noise are conducted. Comparative evaluations with benchmark approaches demonstrate improved fault detection speed, tracking accuracy, and robustness of the proposed framework, highlighting its potential for enhancing reliability and operational continuity in high-precision industrial applications.

This paper presents the coverage environment by multi agents using reinforcement learning with Q-learning and Sarsa algorithms. Two environments are explored to study cooperative coverage path planning with these algorithms. The goal is to achieve coverage in the environment by having agents collaborate to reduce energy consumption. Initially, an obstacle-free environment is examined, then an obstacle is considered in the environment. The proposed algorithm involves sharing experiences among agents to cover environments containing obstacles, and compares its convergence speed to scenarios without sharing experiences. The proposed algorithm’s performance is assessed through different simulations.

Controlling nonlinear systems in real-time applications, such as robotics, aerospace, and industrial automation, poses significant challenges due to their complex dynamics, strong nonlinearities, and susceptibility to external disturbances and parameter uncertainties. Traditional intelligent control methods often struggle with these complexities, particularly in uncertain environments, leading to issues with accuracy, stability, and adaptability. Existing approaches, including fuzzy logic-based control and extreme learning machines (ELMs), offer promising solutions but still face limitations such as noise sensitivity, sensitivity to random initialization, lack of robustness in parameter updates, and inadequate robustness in real-time scenarios. This paper presents a novel robust intelligent control framework to tackle challenges in online learning and uncertainty management for nonlinear systems. An online updating method that integrates the Householder block exact QR decomposition-based recursive least squares algorithm is proposed, improving numerical stability and robustness during parameter updates, especially under noisy and uncertain conditions. By applying this method, the model becomes less sensitive to random initialization and more resilient to outliers. Building on this, a new robust online controller based on a sequential type-2 fuzzy wavelet is developed. This controller combines interval type-2 fuzzy logic for uncertainty modeling, wavelet neural networks for time-frequency localization, and an extreme learning machine for fast, one-pass learning. Architecture supports dynamic and robust parameter adaptation while keeping the hidden layer fixed, enhancing efficiency and adaptability in real-time applications. Additional contributions include an adaptive residual weighting strategy, regularized QR decomposition, and a recursive update scheme using Householder transformations. The proposed system significantly reduces sensitivity to random initialization and outliers, offering improved adaptability and performance in real-time applications. The numerical performance indices demonstrate that the ROC-ST2FWELM outperforms existing control methods, including online sequential type-2 fuzzy wavelet extreme learning machine (OS-T2FWELM) and fractional order PID combined with extended state observer (FOPID-ESO), across different real-world and simulated systems, including the Twin Rotor MIMO System (TRMS) and the backhoe hydraulic system. Specifically, the TRMS results show a Root Mean Square Error (RMSE) of the preposed controller for at 0.0988 and 0.0621, OS-T2FWELM at 0.2631 and 0.2334, and FOPID-ESO at 0.3324 and 0.3261 for the yaw angle and the pitch angle, respectively.

This paper aims to propose a new method for reinforcement learning and compare it with a PID controller in the Quadruple-tank system in the presence of uncertainty. We use one of the popular structures called actor-critic and train it using a deep deterministic policy gradient algorithm. These methods are compared in terms of accuracy and rise time to show which one can have better performance if we consider uncertainty. The proposed method represents an approach that considers some changes in observation dimensions by a series of error items consisting of some previous and current errors and then trains the Reinforcement Learning algorithm through these new observations. Finally, the results of these methods are compared by simulations and the proposed method’s performance is evaluated. Which specifies our approach has better performance.