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The heat treatment temperature must precisely meet the required profile to ensure the quality of the workpiece. The model of the heat treatment furnace is not precisely known because it is nonlinear and complex. Considering this aspect, model-free temperature control methods are proposed, which are not based on a model of the process, but only on the input and output data acquired from the control system. Since the controlled temperature must accurately track the reference formed by the ramp and soak signals in the presence of disturbances, model-free iPI controllers were chosen due to the included double-integral component that ensures the steady-state performance for ramp signals. For the tuning of the iPI controller parameters, the iterative feedback tuning method is proposed, and the performance is compared with that obtained using the sliding mode control iPI variant. The proposed iPI control laws are tested for ramp and soak scenarios, using temperature suitable to thermal treatment technological processes, and performance is analyzed using both process-specific indices and classical criteria. [ABSTRACT FROM AUTHOR]

The effective operation of model-based control strategies in modern energy systems, characterized by significant complexity, is contingent upon highly accurate large-scale models. However, achieving such precision becomes challenging in complex energy systems rife with uncertainties and disturbances. Controlling different parts of the energy system poses a challenge to achieving optimal power system efficiency, particularly when employing model-based control strategies, thereby adding complexity to current systems. This paper proposes a novel model-independent control approach aimed at augmenting transient stability and voltage regulation performance in multi machine energy systems. The approach involves the introduction of an optimized model-free fractional-order-based excitation system stabilizer for synchronous generators in a multi machine energy system. To overcome the limitations associated with complex system model identification, which add degrees of simplification at defined operating conditions and assume the system model remains fixed despite high uncertainty and numerous disturbances, an optimal model-independent fractional-order-based excitation control strategy is introduced. The efficacy of the proposed approach is validated through comparative numerical analyses using the MATLAB/Simulink environment. These simulations were conducted on a two-area, 12-bus multi-machine power system. Simulation results demonstrate that the presented excitation system stabilizer outperforms conventional controllers in terms of transient and small-signal stability. It also suppresses the low-frequency electromechanical oscillations within the multimachine energy system. [ABSTRACT FROM AUTHOR]

Soft robotics faces challenges in attaining control methods that ensure precision from hard-to-model actuators and sensors. This study focuses on closed-chain control of a segment of PAUL, a modular pneumatic soft arm, using elastomeric-based resistive sensors with negative piezoresistive behaviour irrespective of ambient temperature. PAUL's performance relies on bladder inflation and deflation times. The control approach employs two neural networks: the first translates position references into valve inflation times, and the second acts as a state observer to estimate bladder inflation times using sensor data. Following training, the system achieves position errors of 4.59 mm, surpassing the results of other soft robots presented in the literature. The study also explores system modularity by assessing performance under external loads from non-actuated segments. [ABSTRACT FROM AUTHOR]

Vector control of an induction machine (IM) is typically performed by using cascade control structures with conventional linear proportional–integral (PI) controllers, the inner loop being designed for current control and the outer loop for rotor flux and speed control. In this paper, starting with the dq model of the IM, advanced control algorithms are proposed for the two control loops of the cascade structure. For the current inner loop, after the decoupling of the two dq currents, predictive control algorithms are employed to independently control the currents, considering the constraints imposed by the electrical signal physics limitations. Since the outer loop has a nonlinear affine multivariable plant model, a homotopy-based variant of feedback linearization is used to obtain a nonsingular decoupling matrix of the feedback transformation even when the rotor flux is zero at the start-up of the motor. During the continuous variation in the homotopy parameter, the plant model is variable and, for this reason, model-free algorithms are used to control the flux and speed of the IM due to their capabilities to manage complex dynamics from data without requiring knowledge of the plant model. The performances of the proposed cascade control strategy with advanced algorithms in the two loops were tested by simulation and compared with those obtained with conventional PI controllers, resulting in better dynamic behavior for predictive and model-free control. [ABSTRACT FROM AUTHOR]

ANNs have become a cornerstone in efficiently managing building energy management systems (BEMSs) as they offer advanced capabilities for prediction, control, and optimization. This paper offers a detailed review of recent, significant research in this domain, highlighting the use of ANNs in optimizing key energy systems, such as HVAC systems, domestic water heating (DHW) systems, lighting systems (LSs), and renewable energy sources (RESs), which have been integrated into the building environment. After illustrating the conceptual background of the most common ANN architectures for controlling BEMSs, the current work dives deep into relative research applications, thereby exhibiting their methodology and outcomes. By summarizing the numerous impactful applications during 2015–2023, this paper categorizes the predominant ANN-based techniques according to their methodological approach, specific energy equipment, and experimental setups. Grounded in the different perspectives that the integrated studies illustrate, the primary focus of this paper is to evaluate the overall status of ANN-driven control in building energy management, as well as to offer a deep understanding of the prevailing trends at the building level. Leveraging detailed graphical depictions and comparisons between different concepts, future directions, and fruitful conclusions are drawn, and the upcoming innovations of ANN-based control frameworks in BEMSs are highlighted. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of attitude tracking in quadrotor unmanned aerial vehicles (UAVs) using a model-free online learning control (MFOLC) scheme. The attitude system, which is represented by unit quaternions, is considered in the presence of uncertain and unknown inertia parameters, time-varying external disturbances, and angular velocity measurement noise. A computationally low-cost control scheme consisting of a model-free baseline controller and a module capable of learning from previous control input is designed. The proposed controller does not require precise inertial parameters and does not involve feedforward terms that use these parameters and true system states. This ensures that the approach can protect the control effort from sensor noise as well as parameter uncertainty. We also show that all the signals in the closed-loop system are uniformly ultimately bounded. Comparative simulations and real-world experiments are conducted for validation, which demonstrate the effectiveness and fine performance of the proposed scheme. [ABSTRACT FROM AUTHOR]

Given the challenges associated with accurately modeling complex nonlinear systems with time delays in industrial processes, this paper introduces an advanced model-free control algorithm that combines data-driven and model-driven approaches. Initially, an enhanced algorithm for multi-innovation model-free control, incorporating error feedback, is presented based on the error feedback principle. Subsequently, a novel control strategy is introduced by delving into PID neural network (NN) recognition and control theory, merging PID NN control with multi-innovation feedback control. Through meticulous mathematical derivation, the proposed strategy is proven to ensure system stability. Compared with traditional NN PID controllers, the convergence rate of the proposed scheme is 50 s faster and the steady-state errors are limited to ±1. [ABSTRACT FROM AUTHOR]

Microrobotics has opened new horizons for various applications, especially in medicine. However, it also witnessed challenges in achieving maximum optimal performance. One key challenge is the intelligent, autonomous, and precise navigation control of microrobots in fluid environments. The intelligence and autonomy in microrobot control, without the need for prior knowledge of the entire system, can offer significant opportunities in scenarios where their models are unavailable. In this study, two control systems based on model-free deep reinforcement learning were implemented to control the movement of a disk-shaped magnetic microrobot in a real-world environment. The training and results of an off-policy SAC algorithm and an on-policy TRPO algorithm revealed that the microrobot successfully learned the optimal path to reach random target positions. During training, the TRPO exhibited a higher sample efficiency and greater stability. The TRPO and SAC showed 100% and 97.5% success rates in reaching the targets in the evaluation phase, respectively. These findings offer basic insights into achieving intelligent and autonomous navigation control for microrobots to advance their capabilities for various applications. [ABSTRACT FROM AUTHOR]