This paper investigates a model-free unified chassis controller (UCC) for simultaneous regulation of vehicle side-slip angle, yaw rate, and roll angle during abrupt maneuvers. The proposed strategy combines iPD-based model-free controllers with a control allocation architecture and is evaluated in a MATLAB/Simulink®-CarSim® co-simulation environment under a sine-with-dwell maneuver for three road friction conditions. The comparison includes the open-loop vehicle, the built-in CarSim® ESC baseline, a fixed-parameter 2PID+fuzzy controller, and a gain-scheduled LQR benchmark. No benchmark minimizes every nominal metric at all friction levels. However, the proposed controller provides the most consistent robustness across conditions. It is the only strategy that completes the low-friction case while keeping lateral and roll responses bounded and meaningful, whereas the other baselines either lose practical stability before the end of the maneuver or exhibit unacceptable state excursions. This robustness is associated with a more active realized brake pressure pattern, while the anti-roll torque remains within moderate bounds. These findings indicate that the proposed MFC-based UCC offers a favorable compromise between regulation quality, practical closed-loop stability, and actuator demand under friction variations.

The autonomous navigation of robotic systems is one of the main problems of this industry today. Due to the simplicity of its implementation and the ability to follow trajectories, the Pure Pursuit controller has become a handy and popular tool among researchers. However, the controller's performance depends directly on the adjustment of its parameters, which must be selected following the track's characteristics. This study investigates the Particle Swarm Optimization (PSO) algorithm application for automatically tuning multiple parameters within the Pure Pursuit controller, to improve trajectory tracking accuracy on a complex closed-loop circuit. For this purpose, the track is segmented according to the smoothness of the curves present, and the controller parameters are automatically adjusted according to the characteristics of each segment. The results of this work show that segmentwise parameter optimization of a Pure Pursuit controller using the PSO algorithm enables the vehicle to successfully complete complex tracks, whereas both empirical and globally optimized fixed-parameter configurations fail early in the trajectory. The proposed approach demonstrates significant improvements in trajectory tracking accuracy, suggesting that PSO-based parameter optimization can be a valuable tool for enhancing the performance of Pure Pursuit controllers in real-world autonomous navigation applications.

Sudden cardiac death (SCD) represents a critical public health challenge, emphasizing the need for predictive techniques that model complex physiological dynamics. Studies indicate that the “V-trough” pattern in sympathetic nerve activity (SNA) could act as an early indicator of potentially fatal cardiac events, which can be effectively modeled using a modified version of Chua’s chaotic system, incorporating the variables of heart rate (HR), SNA, and blood pressure (BP). This paper introduces a Chua circuit with delay, and proposes a novel control design technique based on Lurie-type control systems theory combined with mixed-sensitivity H∞ (S/KS/T) methodology. The proposed controller enables precise regulation of HR in Chua’s circuit, both with and without delay, paving the way for the development of advanced devices capable of preventing SCD. Furthermore, the developed theory allows for the project of robust controllers for delayed Lurie systems within the single-input–single-output (SISO) framework. The presented theoretical framework, supported by numerical simulations, demonstrates the effectiveness of the conceptualization, marking a considerable advance in the understanding and early intervention of SCD through robust and nonlinear control systems.

Active chassis systems play a crucial role in enhancing vehicle stability and safety, complementing technologies like Electronic Stability Control (ESC). Among these, active anti-roll bars and semi-active electronic dampers have gained attention for their ability to improve handling and ride comfort while operating on relatively simple principles. In this context, this paper extends the Modular Modelling Methodology (MMM) to incorporate these components into a multibody vehicle model, enabling advanced control strategies for improved dynamic performance. A half-car system, conceived as a multibody system, is used as a case study, considering different configurations for the anti-roll bar (passive and active) and semi-active electronic dampers. The modelling of the passive anti-roll bar is performed using the Finite Element Method (FEM), while the nonlinear dynamics of the semi-active electronic dampers are derived using the continuity equation and the flow-through-orifice equation. The results obtained from the model derived through the MMM are compared to those of commercial multibody system analysis software. A new Reduced Order Model (ROM) is proposed for the half-car system to synthesize control algorithms for vertical nonlinear dynamics. This ROM accounts for important effects generally neglected, such as spring and tire pretension and the variation of the damping coefficients of the semi-active electronic dampers. A time-varying Model Predictive Control (MPC) is designed based on the proposed ROM. The MPC is tested through numerical simulations, considering the multibody model derived through the MMM and coupled with the anti-roll bar and semi-active electronic dampers as the real plant in two scenarios: (1) a roll test and (2) a fishhook maneuver. The performance is compared with different controllers, such as PD, LQR, and LQG. The comparisons reveal the superior performance of the MPC in attenuating the roll angle (up to 61.4

In this Topic, 34 (thirty-four) papers were selected and published on the following sub-themes: Modeling and Nonlinear Systems (Contributions 1–5); Control, Synchronization, and Optimization (Contributions 6–13); Chaos and Hyperchaos (Contributions 3 and 14–20); Complex Systems (Contributions 21–25); and Applications to Engineering and Sciences (Contributions 22 and 26–32) [...]

The field of Soft Robotics proposes the use of flexible and compliant materials in the fabrication of soft actuators that can interact with humans in a safer way than conventional robots. While soft pneumatic actuators operate with positive pressure inflating their chambers and creating movement, vacuum actuators operate with negative pressure collapsing their chambers to generate movement. The latter have the benefit that they do not burst due to excessive pressure during contraction and since they shrink in size when activated, they can fit into tight spaces and are therefore more compact, aside from possibly being able to expand with positive pressure. This work presents a soft pneumatic linear actuator designed as a bellow-shaped structure made of silicone rubber that operates with negative pressure to contract and with positive pressure to extend. A Finite Element Method dynamic analysis was performed, using the SIMULIA Abaqus software, to predict the actuator’s behavior. From the definition of the geometry, molds composed of modular pieces were designed and 3D printed to fabricate the actuator prototype. A test platform was designed to perform tests to characterize the actuator and then implement a position and pressure cascade control system, designed with a fuzzy pressure controller in the inner loop and a PI position controller in the outer loop, to regulate both the contraction and the extension of the actuator.

Alzheimer's disease (AD) is a degenerative neurological condition that impacts millions of individuals across the globe and remains without a healing. In the search for new possibilities of treatments for this terrible disease, this work presents the improved Alzheimer-like disease (IALD) model for memory failure and connects it to a new control technique that establishes a cure for the memory lost, either in biological or in artificial neural networks. For the IALD model, continuous Hopfield neural networks (HNN) with time delay are used. From the healing side, a robust control technique is used, which is based on new discoveries in Lurie control systems. In addition, this paper reviews the development of Alzheimer-like disease (ALD) model, as well as, the relationship of HNN with Lurie system. Simulations are executed to validate the model and to show the efficacy of applying a new theorem from Lurie problem. With the results presented, this work proposes a new conceptual paradigm that could potentially be applied in memory failure treatments in AD, as well as in hardware implemented HNN under adversarial attacks or adverse environmental conditions.

Real problems in control engineering usually involve many uncertainties and delays. In the search for solutions which deal more adequately with these problems, this paper presents contributions to the theory of the mu-analysis and synthesis applied to uncertain linear fixed time time-delay systems by using Pade approximations. A new necessary and sufficient condition for robust stability for a class of uncertain time-delay systems is presented. From this condition, a novel robust controller synthesis technique is obtained. Furthermore, contributions are presented on the convergence theory of Pade approximations applied to the mu-theory via parametrized optimization technique. In order to better understand the theory presented and verify the effectiveness of the results, examples and comparisons are proposed. Finally, in the conclusions, new lines of research and application are pointed out.

This paper provided a review of the Lurie problem and its applications to control as well as modeling problems in the medical and biological fields, highlighting its connection with robust control theory, more specifically the works of Doyle, Skogestad, and Zhou. The Lurie problem involved the study of control systems with nonlinearities incorporated into the feedback loop. Providing a simpler and broader approach, this review returned to the Lurie problem, covering basic stability concepts and Aizerman's conjecture, establishing it as a special instance of the Lurie problem. The paper also explained the connection between the Lurie problem and robust control theory, which resulted in the establishment of new conditions for the Lurie problem. The principal contribution of this paper was a comprehensive review, utilizing the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology of the applications of the Lurie problem in the medical and biological fields, demonstrating its significance in various domains such as medical device controllers, mechanical ventilation systems, patient-robot-therapist collaboration, tele-surgery, fluid resuscitation control, nanobiomedicine actuators, anesthesia systems, cardiac mechanics models, oncology cell dynamics, epidemiological models, diabetes modeling, population dynamics and neuroscience, including artificial neural networks (ANN). This article seeked to present the latest advancements in the Lurie problem, offering an update for researchers in the area and a valuable starting point for new researchers with several suggestions for future work, showcasing the importance of Lurie-type systems theory in advancing medical research and applications.

An Electronic Stability System (ESC) is an automotive system that helps to improve the stability in the handling of the vehicle to avoid accidents and provide a better driving experience. The development, validation, and implementation of a practical ESC as part of the vehicle safety system have involved the work of many major automotive companies and authors in recent years. In this paper, a PID controller is designed for the ESC system of a general car. For this purpose, Matlab® and Carsim software are used together to model, develop and simulate the ESC system. The result is a PID controller with hysteresis that acts on the brakes of the car to provide yaw stability control and minimize the side-slip angle caused by a sudden driving maneuver. This controller is effective and easy to implement so that its production and set-un on a real vehicle are simple and feasible.

In this paper, a stability control via differential braking is developed. The Model Predictive Control (MPC) is considered based on a linearized four wheels model. The performance is evaluated considering model-in-the-loop tests in the software CarSim. The maneuvers Double Lane Change and Sine Dwell are considered. Results show the efficiency of MPC in keeping the vehicle stability with hard maneuvers.

The goal of this paper is to present a different approach to the analysis of the absolute stability of Lurie type systems in the single-input-single-output (SISO) case using robust control theory. The proposed technique enables the design of controllers via [Formula: see text] mixed-sensitivity (S/KS/T), where, besides making the system absolutely stable, the performance problem can also be solved. In addition, it is also demonstrated that it is possible to make use of this new approach in time-delay Lurie type systems. Thus, through a new methodology, this work paves the way to the study of the absolute stability of multiple-inputs-multiple-outputs (MIMO) systems, aiming at a better generalization of the theory and enabling applications in other areas, such as neural networks. Examples, numerical simulations and application in Chua’s circuit are given to illustrate the results.

Assistance and rehabilitation exoskeletons with soft robotic actuators are possible substitutes for the ones with rigid mechanical structures that might compromise the therapy process due to heavy weight, joint misalignment and discomfort. Modelling and controlling soft actuators, however, are challenging tasks due to the non-linearities inherent to properties of the materials used, such as silicone rubber and fabric, as well as the different possible geometries, the interaction with the human body and possible disturbances. A systematic review was performed to analyze and synthesize the state-of-the-art of recent designs and developments of upper limb exoskeletons with soft pneumatic actuators. The selected works were classified into two groups, of hand and wrist exoskeletons, and elbow and shoulder exoskeletons. A brief description of each work reviewed is presented alongside a discussion about the modelling and control strategies implemented, with the aim to contribute as a reference to future works on soft exoskeletons and assistive devices.

This work proposes a mobile robot control applied to a seeding task using inertial sensors. The position estimation through these sensors is solved using an autoencoder neural network trained in simulation. The control strategy is tested in the Webots simulation environment. The control strategy uses two PID controllers, one for position and other for direction. Theses controllers are optimized during trajectory using the bat algorithm to update its parameters aiming limited energy cost. The optimization objective involves position and energy cost estimated using the data fusion autoencoder as well as speed imposed to the wheels. The results present a trajectory tracking for a straight path in a rectangular environment.

This paper presents a hybrid computational model based on regression techniques, machine learning and physicomathematical algorithms developed for assistance in locating victims in the Brumadinho tragedy in 2019. The physicomathematical model, which provided results to help search teams, is based on integral and vector calculus, and fluid mechanics concepts. In addition, from data provided by the physicomathematical algorithm, two hybrid model were developed. One of them uses regression statistical and the other one uses support vector regression which is a type of machine learning. With good prospects of the advances in research, it is expected in future work, a more accurate model that can be used in other possible situations of dam-break. Moreover the model can be applied to situations involving computational fluid dynamics in general

In this paper, we study and construct mathematical models for force control of an electrostatic microgripper grasping a microparticle. Firstly, we analyse a linear model in the literature for this microgripper and deduce some properties. Then we perform a system identification based on an experimental setup. Finally, we design and simulate a closed-loop force control law. The control theory to be used in this paper is the \(H_\infty \) mixed sensitivity. The microparticle is modelled by a spring as it is not significantly deformable.

A proposta deste trabalho é através de simulações, desenvolver um amortecedor eletrônico em malha fechada, cujo controle das forças de amortecimento será realizado por meio de uma válvula proporcional. A válvula irá controlar a vazão que atravessa o seu o