Asian Journal of Control最新文献

Fuel cell stack (FCS) is a practical power source for new energy vehicle applications, and fuel economy is a problem that many researchers are concerned about. In this paper, an adaptive real-time control strategy aiming at improving fuel efficiency is proposed; the control purpose is to distribute the power requirement between the FCS and the battery to achieve good fuel economy. First, the FCS model is built according to experiment data, and in order to reflect the affection of the temperature to the proposed control strategy, the thermal model of the battery is established. Then the future power requirement is predicted via Bayes inference analysis. Based on the FCS model, the battery model, and the predicted power requirement, the real-time control strategy is designed and solved with minimization principle optimization over the receding horizon. The proposed control strategy is validated both through simulation and hardware-in-loop (Hil) experiments on a 40 kW FCS. The results compared with the rule-based (RB) strategy and the loss minimum strategy (LMS) show that the proposed control strategy can effectively reduce fuel consumption by 4%, and at the same time, it can help extend the life span of the battery by considering the temperature affection.

The problem of recursive set-membership filter design for two-dimensional (2-D) systems subject to FlexRay communication protocol and hybrid cyber attacks (HCAs) is investigated in this article. The FlexRay protocol that integrates time-triggered and event-triggered mechanisms and involves a series of pre-defined communication cycles based on bidirectional metrics is developed to alleviate the network bandwidth load. Furthermore, the envisioned system is exposed to false data injection and denial-of-service attacks that occur in a randomized manner. Subsequently, the dynamic filtering error system (FES) subject to bidirectional evolutionary HCAs and FlexRay scheduling protocol is constructed. Then, sufficient conditions are obtained such that the dynamic FES consistently resides within an ellipsoidal set by utilizing double mathematical induction and recursive linear matrix inequalities (RLMIs). Moreover, the optimal filtering algorithm is given by minimizing the ellipsoidal constraints from the perspective of the traces of the matrix. The effectiveness of the presented recursive set-membership filter design approach is validated by a long-distance transmission line example.

In this paper, trajectory tracking control is investigated for a wheeled mobile robot with one unpowered trailer using an extended state observer (ESO). The unpowered trailer is added to improve load capacity, which results in a large impact on trajectory tracking as a slow-varying and large disturbance. A backstepping controller is proposed to generate desired velocities in an outer loop of a double closed-loop structure. The ESO is employed in an inner loop to estimate the slow-varying and large disturbance from the unpowered trailer. An integral sliding mode controller is also designed in the inner loop to track the desired velocities from the outer loop. Stability analysis for the ESO, the backstepping controller and the integral sliding mode controller is conducted via Lyapunov methods. Simulation results are provided to show the effectiveness of the trajectory tracking control for a wheeled mobile robot with one unpowered trailer.

This paper proposes a fixed-time convergence adaptive sliding mode fault-tolerant controller (ASFTC) to address the air cushion vehicle (ACV) trajectory tracking problem under unknown environmental disturbances and actuator faults. The introduced method enhances the robustness and reduces the chattering of the controller, by proposing an initial state-independent fixed-time convergence method combined with a global sliding mode surface which has the advantage of quickly reaching the “sliding mode”. The model knowledge neural network (MKNN) method is employed to eliminate uncertain parameter effects, and it adjusts disturbance and fault estimates in real time based on tracking errors without the need for upper-bound disturbance information and additional observer compensation. Finally, simulations validate the effectiveness of the proposed adaptive fault-tolerant control system.

In this article, we explore the group output tracking consensus problem for discrete-time strict-feedback $��n�$-order nonlinear multiagent systems that run repeatedly on finite time $��\{�0�,�\dots �,�T�\}�$. A novel distributed adaptive iterative learning group consensus protocol is designed, which consists of two main components. The first component is based on time-varying neural networks, which is used to approximate the unknown nonlinear function in the $��n�$-step ahead predictor. In general, not all followers can access the information regarding the leader, which complicates the design of iterative learning protocols for MASs. Therefore, the second component of the protocol addresses this challenge by treating the leader's output as a time-varying parameter and designing a time-varying auxiliary term to compensate the leader's output information. Parameter updating laws and initial state learning laws are also proposed via the cooperative-competitive relationship between the agents. We demonstrate the group consensus with sufficient small errors can be achieved at time $��\{�n�,�\dots �,�T�\}�$, as the number of iterations proceed to infinity. Then, the results are extended to the case of multisubgroups and multileaders. Finally, two simulations validate the findings of this article.

This article considers the filtering problem for 2-D Markov jump Roesser systems, in which the measurement signal is transmitted via analog fading channels. The time-varying amplitude attenuation of the analog fading channels is described by the fading gain modeled as a random variable. The hidden Markov model is used to deal with the mode mismatch phenomenon between the plant and the filter, then a full-order asynchronous filter is designed for 2-D Markov jump Roesser systems under analog fading channels. The purpose is to introduce a filter to guarantee that the filtering error system is not only asymptotically mean square stable, but also satisfies the $��{�H�}_{�\infty �}�$ disturbance attenuation performance. An improved decoupling method is proposed to acquire the 2-D filter parameters such that the conservatism of the 2-D filter designed law can be effectively reduced. Finally, a verification example and Darboux equation are given to illustrate the validity of the introduced asynchronous filter design law.

This paper proposes a super-twisting controller based on a PI-based surface for a buck converter with matched disturbance. Standard sliding mode control (SMC) has some well-known drawbacks: the chattering effect and parameter restriction, which determines how quickly the controller accesses the sliding surface and the steady-state error. First, the super-twisting algorithm is employed in the control rule of the proposed controller to suppress the chattering effect. Then, parameter limitation, another deficiency of the SMC, has been eliminated by employing a PI-based surface. The PI-based surface also improves the controller's adjustment parameters by developing adaptability in parameter choices. The buck converter's dynamic response is enhanced by the addition of a novel parameter to the sliding surface of the suggested controller. Furthermore, Lyapunov stability analysis is described in order to evaluate the theoretical stability of the proposed controller. The efficacy of the suggested controller has been demonstrated through an array of simulations and an experimental configuration across various evaluation conditions. The simulation and experimental results show that the reference-tracking capability is effective in the presence of disturbances. Furthermore, the proposed switching rule effectively eliminates the chattering effect. The suggested controller outperforms existing techniques in terms of both dynamic performance and disturbance robustness.