Explore the vast range of titles available.

In many applications, add-on type repetitive controllers have been reported to have prominent capability of attenuating periodic disturbances and/or tracking periodic reference commands. However, the effective information such as performance weighting functions for the design of feedback controllers has not been considered sufficiently on the design of repetitive controllers. In this study, we deal with a problem of a robust repetitive controller design for an uncertain feedback control system using its explicit performance information. We first show that a robust stability condition of repetitive control systems has a similar form with the well-known robust performance condition of general feedback control systems. The repetitive controller is designed using the performance weighting function for the design of the robust feedback controller. It is also shown that a steady-state tracking error of the repetitive control system is described in a simple form without time-delay term. This result yields that the repetitive control system has a much larger loop gain in the steady state than the feedback control system. Moreover, this paper provides sufficient conditions ensuring that the power of the steady-state tracking error in the repetitive control system is less than or equal to that of the feedback control system. Based on the obtained results, we present repetitive controller design method using the design information of the feedback control system. Finally, application studies on the track-following control system of optical disk drives are performed to show the validity of the proposed method.

With the acceleration of urban development, the population density of urban cities has increased. As the spatial characteristics of multi-unit housing (MUH) perfectly fit this developmental trend and, simultaneously, have high energy efficiency, the number of MUHs has increased rapidly in recent decades. Although many studies have proposed high energy efficiency strategies, weather uncertainty leads to errors between the operational performance of building energy and simulated values. This study introduces a robust optimization framework that incorporates uncertainty considerations into the optimization process to suppress energy consumption fluctuations and improve the average building energy consumption performance. Neural networks are used to model the uncertainty of multiple weather elements as normal distributions for each hour, and the accuracy of the uncertainty model is validated by calculating the mean absolute percentage error (MAPE) between the mean values of the distribution and the measurement values, which ranges from 3% to 13%. The clustering algorithm is proposed to replace the sampling method to complete the sampling work from the normal distribution space of the weather elements to serve the subsequent optimization process. Compared with the traditional method, the sampling results of the clustering algorithm show better representativeness in the sample space. The robust optimization results show that the average energy consumption of the optimal scheme decreases by 13.4%, and the standard deviation decreases by approximately 17.2%, which means that the optimal scheme, generated by the robust optimization framework proposed in this study, has lower average energy consumption results and a more stable energy consumption performance in the face of weather uncertainty.

This paper proposes a robust integral of sign error (RISE) based tracking control method with asymptotic prescribed performance for two-wheeled self-balancing mobile robot (TSBR) in presence of exogenous disturbances and modeling uncertainties. First, a velocity planner is designed to provide the desirable longitudinal speed and yaw rate based on the TSBR’s kinematics model. Afterwards, a modified prescribed performance function (MPPF) is devised to restrain all tracking errors of the TSBR within the predefined range without requiring the accurate initial values of tracking errors. Besides, the radial basis function neural network (RBFNN) based on minimum parameter learning approximator is utilized to attenuate the impact of exogenous disturbances and modeling uncertainties of the TSBR. Then, the MPPF and RBFNN are implanted into the RISE scheme to form an expected trajectory tracking controller for the TSBR, which can guarantee the control continuity and system asymptotic stability. Finally, comparative simulations are conducted to verify the feasibility and effectiveness of the proposed MPPF-RISE controller.

Electric Power Assisted Steering (EPAS) systems provide vehicle stability and safety under various driving conditions. Previous studies often applied only one or several traditional algorithms to control the performance of EPAS systems and ignored the influence of external disturbances. This increases the signal tracking error and causes other adverse effects on the system. In this article, we propose designing a nonlinear robust control mechanism that combines Sliding Mode Control (SMC) and Nonlinear Active Disturbance Rejection Control (NADRC) techniques to solve the existing issues. The article’s novelty lies in utilizing a Nonlinear Extended State Observer (NESO) and Nonlinear Tracking Differentiator (NTD) to improve the performance of the proposed control mechanism. In addition, ideal assisted characteristic curves have been innovated based on nonlinear functions to improve the vehicle’s driving comfort and stability, which is considered the second new contribution. The simulation results show that most of the steady-state errors of the proposed controller are only about 2% ( v 1 = 30 km/h) and no more than 3.5% ( v 2 = 70 km/h) except for steering motor current. The observed errors of the state variables are less than 1.4%, while the disturbance error is only about 6.9%. Finally, it is claimed that common issues like overshoot, chattering, and sensor noise do not affect the EPAS system when the proposed method is used to control it.

Floating wind turbines (FWTs) are now recognized as one of the most effective and affordable renewable energy sources. However, their performance is strongly influenced by dynamic environmental conditions, particularly sea waves under significant oscillatory conditions. Ocean wave and wind disturbance affect turbine positioning, underscoring the critical essential for adaptive and robust control mechanisms to manage the unpredictable external inputs. In this context, we present an innovative method based on federated deep learning for training capsule networks to detect disturbances and enable adaptive robust control of FWTs among the environmental uncertainty. Through the proposed technique, a unique mixture of sliding mode control and deep reinforcement learning (DRL) yields in the extraction of wide features and modeling of spatial relationships between sensor data in the capsule networks framework. Furthermore, by employing federated learning, the capsule-net model is trained in a distributed manner across multiple wind turbines. Therefore, enhanced accuracy and effectiveness of disturbance detection are guaranteed. Simulation results reveal effective identification of disturbances which in turn improves the performance and stability of FWTs under the coarse environmental situation. The global Lyapunov stability analysis proves the FWTs' closed-loop stability. Performance of the superior DRL is evaluated in comparison with a radial basis function neural network (RBFNN) estimation. The innovative DRL method represents a significant advancement in the control of FWTs as a high potential of development for intelligent management of similar systems. As a final aim, this research work finds out the reliability and efficiency of FWTs in variable weather conditions (short-term) and erratic ocean environments (long-term). Moreover, the control system makes a substantial impact on the sustainable development of the wind and renewable energy sector.

This study is concerned with robust finite-time stability and stabilisation of a class of switched positive systems. By using the multiple linear copositive Lyapunov function approach, sufficient conditions of finite-time stability and finite-time boundedness are constructed, respectively. l1-gain is used to analyse the disturbance attenuation performance of the systems, and a finite-time weighted l1-gain is obtained under bounded exogenous disturbances. Then, the problem of robust finite-time stabilisation of non-autonomous systems with a weighted l1-gain is solved. All the proposed conditions are formulated in linear programming. Finally, two illustrative examples are given to show the validity of the theoretical results.

Helicopter noise poses significant challenges to pilot health and flight safety. To address this issue, our research team has developed an intelligent noise recognition and suppression system. This system uses optimized sensor arrangements and efficient signal processing methods to accurately extract noise features. The deep learning model achieved an accuracy of 95.8% in noise classification tasks, and ensemble learning strategies further enhanced system robustness. The active noise control component achieved an average suppression effect of 18.7 dB in the 100-500Hz frequency range. The system demonstrated stable and reliable performance under various flight conditions and environmental settings, significantly improving the acoustic environment of the cockpit. This achievement provides technical support for improving helicopter pilot safety and comfort and offers valuable insights for other noise control applications.

The application of plug‐in electric vehicles (PEVs) is assumed to be wide in power system in regulating the system frequency in the near future. This paper provides an aggregate model of an interconnected multi‐area thermal system with the incorporation of PEVs for frequency control in each of the three areas. Two degree of freedom proportional–integral–derivative (2DOF‐PID) controller has been utilised for robust secondary control in all the three control areas. An optimisation technique inspired by nature named as wind‐driven optimisation (WDO) technique is employed to decide the optimal values of the controller gains. The effectiveness of WDO optimised 2DOF‐PID controller is verified by performing various comparative analysis with conventional PID controller under nominal system condition and random loading condition. An analysis has also been carried out to evaluate the system performance with variation in state of the integrated PEVs. The impact of introducing PEVs in the system in frequency regulation has been vigorously studied under different system conditions such as nominal, random loading condition, and simultaneous perturbation in two and three areas to testify their advantages. The comparisons reveal that with the integration of PEVs in the system, the system dynamics gets enhanced to a large extent.

In order to improve the position high-precision synchronization performance of multi-motor synchronous control, a multi-motor position synchronization control method based on non-singular fast terminal sliding mode control (NFTSMC) combined with an improved deviation coupling control structure (Improved Deviation Coupling Control(IDCC), NFTSMC+IDCC). Firstly, this paper designs a sliding mode controller using a non-singular fast terminal sliding mode surface with a Permanent Magnet Synchronous Motor (PMSM) as the control object. Secondly, the deviation coupling is improved to enhance the coupling between multiple motors and achieve position synchronization. Finally, the simulation results show that the total error of multi-motor position synchronization under NFTSMC control is 0.553r in the simulation of multi-motor synchronization control under the same working conditions, which is 2.873r and 1.772r less than that of SMC and FTSMC in terms of speed error, and the anti-disturbance performance is 83.68% and 76.22% higher than that of both of them, respectively. In the subsequent simulation of the improved multi-motor position synchronization structure, the total error of the multi-motor position is in the range of 0.56r-0.58r at three speeds, which is much smaller than the synchronization error under the Ring Coupling Control (RCC) structure and Deviation Coupling Control (DCC) structure, showing a better The synchronization error is much smaller than that of the RCC structure and DCC structure, which shows better position synchronization performance. Therefore, the multi-motor position synchronization control method proposed in this paper has a good position synchronization effect and achieves the control effect of small displacement error and fast convergence of the multi-motor position synchronization control system after being disturbed, the control performance is significantly improved.

The present study proposes two schemes for simultaneously estimating actuator and sensor faults for a class of uncertain non-linear systems. In the first scheme, two sliding mode observers (SMOs) are designed to estimate actuator and sensor faults, respectively, under the assumption that the matching condition holds. In the second scheme, the assumption of matching condition is relaxed and an adaptive observer has been designed to estimate the sensor fault instead of using an SMO. The effects of the system uncertainties on the estimation errors of states and faults are reduced by integrating a prescribed ℋ∞ disturbance attenuation level into the proposed schemes. The sufficient condition for the existence of the proposed observers with ℋ∞ tracking performance is derived and expressed as a linear matrix inequality optimisation problem such that the ℒ2 gain between the estimation errors and system uncertainties is minimised. Finally, a simulation study is presented to illustrate the effectiveness of the proposed schemes.