1,256 publications from this institution
This paper investigates the problem of quantized filtering for a class of continuous‐time Markovian jump linear systems with deficient mode information. The measurement output of the plant is quantized by a mode‐dependent logarithmic quantizer, and the deficient mode information in the Markov stochastic process simultaneously considers the exactly known, partially unknown, and uncertain transition rates. By fully exploiting the properties of transition rate matrices, together with the convexification of uncertain domains, a new sufficient condition for quantized performance analysis is first derived, and then two approaches, namely, the convex linearization approach and iterative approach, to the filter synthesis are developed. It is shown that both the full‐order and reduced‐order filters can be obtained by solving a set of linear matrix inequalities (LMIs) or bilinear matrix inequalities (BMIs). Finally, two illustrative examples are given to show the effectiveness and less conservatism of the proposed design methods.
This paper addresses the problem of state and fault estimation for linear continuous-time systems with actuator failures. A new estimation technique is presented to deal with this design problem. In the proposed approaches, the original system is first augmented in a descriptor system, and a new sliding mode observer is introduced to obtain accurate estimations of both system states and actuator faults. A numerical example is presented to illustrate the effectiveness and applicability of the proposed technique.
A mixed H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> /H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">infin</sub> control design methodology is presented for second-order neutral linear systems with time-varying state and input delays. Delay-dependent sufficient conditions for the design of a desired control are given in terms of LMIs. A controller, which guarantees asymptotic stability and a mixed H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> /H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">infin</sub> performance for the closed-loop system of the second-order neutral linear system, is then developed directly instead of coupling the model to a first-order neutral system based on a Lyapunov-Krasovskii method. A numerical example with simulation results illustrates the effectiveness of the proposed methodology.
Industry 5.0 aims to prioritize human operators, focusing on their well-being and capabilities, while promoting collaboration between humans and robots to enhance efficiency and productivity. The integration of collaborative robots must ensure the health and well-being of human operators. Indeed, this paper addresses the need for a human-centered framework proposing a preference-based optimization algorithm in a human–robot collaboration (HRC) scenario with an ergonomics assessment to improve working conditions. The HRC application consists of optimizing a collaborative robot end-effector pose during an object-handling task. The following approach (AmPL-RULA) utilizes an Active multi-Preference Learning (AmPL) algorithm, a preference-based optimization method, where the user is requested to iteratively provide qualitative feedback by expressing pairwise preferences between a couple of candidates. To address physical well-being, an ergonomic performance index, Rapid Upper Limb Assessment (RULA), is combined with the user's pairwise preferences, so that the optimal setting can be computed. Experimental tests have been conducted to validate the method, involving collaborative assembly during the object handling performed by the robot. Results illustrate that the proposed method can improve the physical workload of the operator while easing the collaborative task.
This paper is concerned with the mobile robot localization problem subject to filter gain uncertainty under dynamic event‐triggered communication mechanism, and meanwhile, the H ∞ filtering performance and the error variance constraint are guaranteed. For saving the sensor energy, a dynamic event‐triggered communication mechanism is introduced to manage the transmission of the measurement data from the sensor to the filter. To characterize the possible fluctuations of the desired filter gain, a resilient filter is constructed for the mobile robot localization. The aim of this paper is to find a solution to the mobile robot localization problem by designing a nonlinear resilient filter such that the filtering error dynamics satisfies both the H ∞ performance requirement and the error variance constraint over a finite time horizon simultaneously. By resorting to the Lyapunov theory and the stochastic analysis technique, the sufficient conditions are established to guarantee that the error dynamic system satisfies both the H ∞ performance requirement and the error variance constraint. Then, a recursive linear matrix inequality (RLMI) approach is employed to design the desired filter. Based on the proposed filter design scheme, the corresponding localization algorithm is presented. Finally, an experiment is conducted in the simulation environment to verify the effectiveness of the proposed localization algorithm.
This paper studies delay-dependent exponential dissipative and l2 - l∞ filtering problems for discrete-time switched neural networks (DSNNs) including time-delayed states. By introducing a novel discrete-time inequality, which is a discrete-time version of the continuous-time Wirtinger-type inequality, we establish new sets of linear matrix inequality (LMI) criteria such that discrete-time filtering error systems are exponentially stable with guaranteed performances in the exponential dissipative and l2 - l∞ senses. The design of the desired exponential dissipative and l2 - l∞ filters for DSNNs can be achieved by solving the proposed sets of LMI conditions. Via numerical simulation results, we show the validity of the desired discrete-time filter design approach.
Based on analyzing principle about the smart car’s speed control system, the system mathematical model is built. Considering the control optimization, a novel control scheme is proposed based on internal model control, and the internal model controller of speed control system is established. Regarding this subject, the internal control theory is introduced to verify the control performance; the traditional PID control method is employed in the experiment. The experiment indicates that the proposed method based on internal model control is easier to determine parameter and has a well robust and good control result of smart car’s speed.
In this paper, a control strategy to mitigate the vibrational response of adjacent buildings under seismic excitation is presented. The proposed strategy combines inter building passive actuators with active actuators placed in the building stories. The main ideas are presented by means of a simplified two-building model; however, a semi-decentralized overlapping approach via the inclusion principle has been used to impose a proper information exchange structure suitable for wireless control of large buildings. Numerical simulations have been carried out to assess the performance of the proposed methodology with promising results. The overall control system exhibits some degree of fault-tolerance since only partial degradation of the control performance results from partial failures of the control system.
The non-fragile sliding mode tracking control design problem for a flexible air-breathing hypersonic vehicles (FAHVs) is investigated in this paper. A linearized model is constructed with respect to a trim point for the nonlinear, dynamically coupled simulation model of the FAHVs with external disturbance, then a reference model is designed and a tracking error model is proposed. An integral sliding surface is designed, and then the non-fragile H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> controller design problem for the sliding mode dynamics is discussed. sufficient condition is derived for the existence of such a controller in the presence of unmatched disturbance. A SMC law is derived to guarantee the reachability of the specified sliding surface. Finally, An illustrative simulation on the nonlinear dynamics of FAHVs is given to show the effectiveness of the proposed control methods.
Mobile receiver complexity persists as one of the key areas of concern in the adoption of MIMO technologies for systems such as high-speed downlink packet access (HSDPA) in UMTS. This is particularly the case for high-order modulation schemes such as 16-QAM and 64-QAM, where optimum a posteriori probability (APP) detection becomes too complex, and sub-optimal detection based on serial or parallel interference cancellation performs poorly. The above issues are successfully tackled by introducing a judicious layered encoding process at the transmitter, which once appropriately exploited at the receiver, allows considerable reductions in computational complexity in addition to improvements in performance.
The problem of coordinated control for multiple marine vessels in the presence of external disturbances is considered in this paper. A robust coordinated control algorithm is proposed for multiple marine vessels. The proposed robust coordinated control algorithm is divided into two parts. The first part develops an extended state observer to estimate the disturbances of marine vessels. The second part presents a robust coordinated control algorithm based on the output of the extended state observer. Furthermore, the robust coordinated control algorithm is designed using the dynamic surface control method. In light of the leader-follower strategy, the trajectory for each vessel is defined according to the desired trajectory of the assigned leader and the relative distance with respect to the leader. The effectiveness of the proposed coordination algorithm is demonstrated by the simulation results.
In real engineering environments, the time-varying speed condition is very common. However, most fault diagnosis methods take the constant speed into account, thus ignoring the fault signal changes under time-varying speeds. In this paper, we propose a weighted feature fusion framework based on convolutional neural network (CNN) and graph convolutional network (GCN) to achieve mechanical fault diagnosis. First, CNNs and GCNs are adopted to extract graph and long-range features. Then, a weighted fusion strategy is utilized to integrate the output of the two networks to obtain more diagnostic results. Finally, extensive experiments conducted on a time-varying speed dataset are used to validate the superiority and effectiveness of the proposed method compared to the other methods.
