Search Results (34)

Robust stability/stabilization for discrete-time time-varying Markovian jump linear systems subject to block-diagonal stochastic parameter perturbations is addressed in this paper. Using a scaling technique, we succeed in effectively addressing the multi-perturbations case. We obtain an estimation of the lower bound of the stability radius in terms of the unique bounded and positive semidefinite solutions of adequately defined parameterized backward Lyapunov difference equations. In the time-invariant case, we show that such a lower bound is actually the exact value of the stability radius. Using the obtained result, we effectively address the state-feedback robust stabilization problem. Full article

Despite significant advances in understanding neuronal development, a fully quantitative framework that integrates intracellular mechanisms with environmental cues during axonal growth remains incomplete. Here, we present a unified biophysical model that captures key mechanochemical processes governing axonal extension on micropatterned substrates. In these environments, axons preferentially align with the pattern direction, form bundles, and advance at constant speed. The model integrates four core components: (i) actin–adhesion traction coupling, (ii) lateral inhibition between neighboring axons, (iii) tubulin transport from soma to growth cone, and (iv) orientation dynamics guided by substrate anisotropy. Dynamical systems analysis reveals that a saddle–node bifurcation in the actin adhesion subsystem drives a transition to a high-traction motile state, while traction feedback shifts a pitchfork bifurcation in the signaling loop, promoting symmetry breaking and robust alignment. An exact linear solution in the tubulin transport subsystem functions as a built-in speed regulator, ensuring stable elongation rates. Simulations using experimentally inferred parameters accurately reproduce elongation speed, alignment variance, and bundle spacing. The model provides explicit design rules for enhancing axonal alignment through modulation of substrate stiffness and adhesion dynamics. By identifying key control parameters, this work enables rational design of biomaterials for neural repair and engineered tissue systems. Full article

This work proposes a nonlinear modeling of a permanent magnet synchronous motor (PMSM) based on state space neural networks. The state space neural network is trained and the state variables (currents in a direct–quadrature frame and the rotational speed) are estimated by considering a robust Unscented Kalman Filter (UKF). Two contributions are presented in this work: the fist one is a nonlinear modeling structure for a PMSM based on a state space neural network that allows real-time parameter identification, and the second one is PMSM neural network training and state estimation based on a robust UKF. The robustness of the UKF is obtained by using a singular value decomposition of the covariance matrix. A comparison analysis is performed over a real PMSM motor by considering the proposed approach and a linear approximation of the nonlinear model where the states and parameters are computed by using an Extended Kalman Filter. The identified model is validated in closed loop by considering a nonlinear control strategy based on state feedback linearization. Full article

Background/Objectives: For healthcare institutions developing a robotic programme, delivering value for patients, clinicians, and payers is key. However, the impact on the surgeon, training pathways, and logistics are often overlooked. We conducted a study on the impact of robotic surgery on surgeons, access to robotic surgical training, and factors associated with developing a successful robotic programme. Method: In our international mixed-methods study, a customised web-based survey was circulated to gynaecological oncologists. The Wilcoxon rank-sum test and Fisher’s exact test, tested the hypothesis of the differences in continuous and categorical variables. Multiple linear regression was used to model the effect of variables on outcomes adjusting for gender, age, and postgraduate experience. Outcomes included situational awareness, surgeon fatigue/stress, and the surgical learning curve. Qualitative data were collected via in-depth semi-structured interviews using an inductive theoretical framework to explore access to surgical training and logistical considerations in the development of a successful robotic programme. Results: In total, 94%, 45%, and 48% of survey respondents (n = 152) stated that robotic surgery was less physically tiring/mentally tiring/stressful in comparison to laparoscopic surgery. Our data suggest gender differences in the robotics learning curve with men six times more likely to state robotic surgery had negatively impacted their situational awareness in the operating theatre (OR = 6.35, p ≤ 0.001) and 2.5 times more likely to state it had negatively impacted their surgical ability due to lack of haptic feedback in comparison to women (OR = 2.62, p = 0.046). Women were more risk-averse in case selection, but there were no self-reported differences in the intra-operative complication rates between male and female surgeons (OR = 1, p = 0.1). In total, 22/25 robotically trained surgeons interviewed did not follow a structured curriculum of learning. Low and middle income country centres had less access to robotic surgery. The success of robotic programmes was measured by the number of cases performed per annum, with 74% of survey respondents stating that introducing robotics increased the proportion of surgeries performed by minimal access surgery. There was a distinct lack of knowledge on the environmental impact of robotic surgery. Conclusions: Whilst robotic surgery is considered a landmark innovation in surgery, it must be responsibly implemented through effective training and waste minimisation, which must be a key metric in measuring the success of robotic programmes. Full article

The consensus problem of a multi-agent system with nonlinear second-order underactuated agents is addressed. The essence of the approach can be outlined as follows: the output is redesigned first so that the agents attain the minimum-phase property. The second step is to apply the exact feedback linearization to the agents. This transformation divides their dynamics into a linear observable part and a non-observable part. It is shown that consensus of the linearizable parts of the agents implies consensus of the entire multi-agent system. To achieve the consensus of the original system, the inverse transformation of the exact feedback linearization is applied. However, its application causes changes in the dynamics of the multi-agent system; a way to mitigate this effect is proposed. Two examples are presented to illustrate the efficiency of the proposed synchronization algorithm. These examples demonstrate that the synchronization error decreases faster when the proposed method is applied. This holds not only for the states constituting the linearizable dynamics but also for the hidden internal dynamics. Full article

Aiming to address the problem of pursuit and interception for spacecraft using multiple evasion strategies, a pursuit strategy involving the use of an interactive multiple-model filter (IMM) in a pursuit–evasion game is considered, where the Evader adopts a switchable evasion strategy based on a linear quadratic method and zero-effort miss method. In this case, an improved interactive multiple-model feedback-learning filter method based on a long short-term memory network (LSTM-IMML) is proposed to estimate the Evader’s strategy mode, with the resulting estimation allowing the Pursuer to then switch its own strategy to the appropriate pursuit strategy to intercept the Evader. Also, the improved interactive multiple-model feedback learning filter can feed back the fusion estimation of the last-time state to the next-time state to improve estimation accuracy. An LSTM-based probability estimation network is designed to accurately estimate the probability of different modes. The proposed LSTM-IMML method can be used in the pursuit–evasion game when the Evader is able to switch its evasion strategy. The simulation results show that the LSTM-IMML method has better state estimation accuracy, and the mode probability estimation of the Evader is more exact and stable. Full article

This paper presents novel chattering-free robust control strategies for addressing disturbances and uncertainties in a two-degree-of-freedom (2-DOF) unmanned aerial vehicle (UAV) dynamic model, with a focus on the highly nonlinear and strongly coupled nature of the system. The novelty lies in the development of sliding mode control (SMC), integral sliding mode control (ISMC), and terminal sliding mode control (TSMC) laws specifically tailored for the twin-rotor MIMO system (TRMS). These strategies are validated through both simulation and real-time experiments. A key contribution is the introduction of a uniform robust exact differentiator (URED) to recover rotor speed and missing derivatives, combined with a nonlinear state feedback observer to improve system observability. A feedback linearization approach, using lie derivatives and diffeomorphism principles, is employed to decouple the system into horizontal and vertical subsystems. Comparative analysis of the transient performance of the proposed controllers, with respect to metrics such as settling time, overshoot, rise time, and steady-state errors, is provided. The ISMC method, in particular, effectively mitigates the chattering issue prevalent in traditional SMC, improving both system performance and actuator longevity. Experimental results on the TRMS demonstrate the superior tracking performance and robustness of the proposed control laws in the presence of nonlinearities, uncertainties, and external disturbances. This research contributes a comprehensive control design framework with proven real-time implementation, offering significant advancements over existing methodologies. Full article

This work proposes a novel method for the real-time identification and nonlinear control of a permanent-magnet synchronous motor (PMSM) based on a Physics-Informed Neural Network (PINN) and the exact feedback linearization approach. The proposed approach is presented in a direct-quadrature framework, where the quadrature current and the rotational speed are selected as outputs and the direct and quadrature voltages are selected as inputs. A nonlinear difference equation is selected to describe the physical dynamics of the PMSM, and a PINN is designed based on the aforementioned structure. A simplified training scheme is designed for the PINN based on a least-squares structure to facilitate online training in real time. A nonlinear controller based on exact feedback linearization is designed by considering the nonlinear model of the system identified based on the PINN. Therefore, the proposed approach involves identification and control in real time, where the PINN is trained online. In order to track the reference for the rotational speed, a nonlinear controller with integral action based on exact feedback linearization is designed based on a linear quadratic regulator. As a result, the proposed approach can be used to identify the system to be controlled in real time, and it is able to track any small change in the real model; in addition, it is robust to both external and internal disturbances, such as variations in torque load and resistance. The proposed approach is evaluated through simulation and using a real PMSM, and the results of reference tracking are evaluated under disturbances. The identification performance is evaluated by using a Taylor diagram under closed-loop and open-loop structures, where ARX and NARX structures are used for comparison. It is thereby verified that this novel proposed control approach involving a PINN-based model can adequately track the dynamics of a PMSM system, where the performance of the proposed nonlinear control is maintained even when using the identified model based on the PINN. Full article

The mathematical model of a flexible multistate switch (FMSS) exhibits nonlinear and strong coupling characteristics, whereas traditional power decoupling control makes it difficult to completely decouple the output power. The traditional proportional–integral control parameters are difficult to adjust, and their robustness and dynamic performance are poor, which affects the stability of the voltage of the power distribution network and feeder power. To address these problems, this study first converted the original system into a linear system via coordinate transformation using feedback-accurate linearization to decouple active and reactive currents. Thereafter, a super-twisting sliding mode control (ST-SMC) algorithm was introduced, and an adaptive quasi-super-twisting sliding mode control (AQST-SMC) algorithm comprising the quasi-sliding mode function and adaptive proportional term was proposed. An FMSS double closed-loop controller was designed to achieve improved vibration suppression and convergence speed. A three-port FMSS simulation model was developed using MATLAB/Simulink, and the simulation results show that the proposed control strategy enhances the robustness and dynamic performance of the system. Full article

To effectively eliminate the impacts of both matched and mismatched power disturbances in a three-phase PWM rectifier, this paper proposes a robust direct power control (RDPC) method with a single-loop control structure. Firstly, a nonlinear power model of the three-phase PWM rectifier is established. Then, using the exact feedback linearization method, a linearized power model including matched and mismatched power disturbances is derived and achieves the decoupling of active and reactive power. Secondly, to regulate the DC bus voltage, a sliding-mode controller (SMC) combined with a nonlinear disturbance observer (NDO) is proposed. The proposed SMC combined with an NDO (SMC + NDO) method features a single-loop control structure, which enables a faster response and simpler structure compared to the dual-loop DPC method. By incorporating estimated mismatched power disturbance into the sliding-mode surface, it overcomes the SMC’s defect in incompletely suppressing mismatched disturbances and enables the simultaneous regulation of voltage and active power. Additionally, it effectively reduces sliding-mode chattering. To regulate reactive power, a sliding-mode controller based on the exponential convergence law is designed to suppress matched reactive power disturbances. Finally, the simulation and experimental comparative results demonstrate that the proposed controller exhibits stronger robustness against matched and mismatched power disturbances, as well as a better performance under the constant power load (CPL). Full article

The inherent negative impedance characteristics of a Constant Power Load (CPL) pose a potential threat to the stability of the bus voltage in a DC microgrid consisting of a symmetrical parallel boost converter. We suggest an adaptive feedback control technique using the input–output exact feedback linearization theory for a boost converter integrated into a DC microgrid to improve the stability of the DC bus voltage. This approach involves a transformation of the model into a Brunovsky canonical form, effectively addressing the nonlinear challenges arising from the CPL and the nonminimum phase characteristics of the boost converter. Subsequently, guided by the Lyapunov approach, an adaptation law is established to fine-tune the controller’s gain vector, facilitating the tracking of a predefined linearizing feedback control. We methodically create a method to choose the gains of the adaptive controller in order to guarantee an adequate output response. We validate our suggested controller’s performance using simulation. Full article

It is inevitable that the characteristics of a robot system change inaccurately or cannot be accurately determined during movement and are affected by external disturbances. There are many adaptive control methods, such as the exact linearization method, sliding control, or neural control, to improve the quality of trajectory tracking for a robot’s motion system. However, those methods require a great deal of computation to solve the constrained nonlinear optimization problem. This article first presents some techniques for determining the online learning function parameters of an intelligent controller, including two circuits: the inner circuit is an uncertain function component estimator to compensate for the robot’s input, and the outer circuit is an iterative learning controller and does not use a mathematical model of the robot with optimal online learning function parameters. The optimal condition is based on the model in the time domain to determine the learning function parameters that change adaptively according to the sum of squared tracking errors of each loop. As for the intelligent online learning function parameters, they closely follow the general model to stabilize the robot system, based on the principle of intelligent estimation of the uncertainty component and total noise. This method is built on Taylor series analysis for the state vector and does not use a mathematical model of the system at all. It allows feedback linearization, as well as intelligent stabilization of the system. This article’s content uses a 2-DOF flat robot implemented on MatlabR2022b software to verify the theory. These findings indicate that superior tracking performance is achievable. Full article

In order to solve the problem of the dynamic positioning control of large ships in rough sea and to meet the need for fixed-point operations, this paper proposes a dynamic positioning controller that can effectively achieve large ships’ fixed-point control during Level 9 sea states (wind force Beaufort No. 10). To achieve a better control effect, a large ship’s forward motion is decoupled to establish a mathematical model of the headwind stationary state. Meanwhile, the closed-loop gain shaping algorithm is combined with the exact feedback linearization algorithm to design the speed controller and the course-keeping controller. This effectively solves the problem of strong external interferences impacting the control system in rough seas and guarantees the comprehensive index of robustness performance. In this paper, three large ships—the “Mariner”, “Taian kou”, and “Galaxy”—are selected as the research objects for simulation research and the final fixing error is less than 10 m. It is proven that the method is safe, feasible, practical, and effective, and provides technical support for the design and development of intelligent marine equipment for use in rough seas. Full article

The proposed hierarchical secondary voltage control is a spatially distributed control system using communication networks which are disturbed by both a time delays and packet data dropouts. A state feedback integral control is adopted to eliminate the effect of non-zero disturbance and provide exact tracking of the references of the pilot points and alignment of the reactive powers of the generators that participate in the control. The system is modeled as a discrete-time switched system, and the control gains are synthesized by solving LMIs for a stability condition based on a state-dependent Lyapunov function. For that, the cone complementarity linearization (CCL) algorithm is used. The effectiveness of the proposed control strategy in preventing time delays and packet losses is simulated, considering the model of a realistic electric power grid under typical operational conditions using MATLAB. Full article

The United Nations has predicted the growth of the human population to reach 8.405 billion by mid-2023, which is a 70% increase in global food demand. This growth will significantly affect global food security, mainly marine resources. Most marine resources exist within complex biological food webs, including predator–prey interactions. These interactions have been researched for decades by mathematicians, who have spent their efforts developing realistic and applicable models. Therefore, this paper systematically reviews articles related to predator–prey models considering the harvesting of resources in marine protected areas. The review identifies future remodeling problems using several mathematical tools. It also proposes the use of feedback linearization consisting of both the approximation and exact methods as an alternative to Jacobian linearization. The results show that in an optimal control analysis, adding a constraint in the form of population density greater than or equal to the positive threshold value should be considered to ensure an ecologically sustainable policy. This research and future developments in this area can significantly contribute to achieving the Sustainable Development Goals (SDGs) set for 2030. Full article