International Journal of Control Automation and Systems最新文献

In this paper, tracking control of a class of series elastic actuators subject to unknown time-varying input delay and additive disturbances is investigated. To address the input delay, a novel predictor-like method is proposed in the control input which uses a predictor to compensate for the delay. Lyapunov-Krasovskii functions are applied within Lyapunov-based stability analysis to show semi-globally uniformly ultimately bounded tracking errors. The control scheme is extended to the case that the input delay is unknown and time-varying. A constant estimate of the delay is determined to establish uniformly ultimately bounded convergence of the tracking error. Numerical simulation results illustrate the performance of the developed robust controller.

This paper addresses the challenge of data-driven control of nonlinear systems, focusing on the limitations and capabilities of model reference Gaussian process regression (MR-GPR) and its evolved counterpart, model reference neural networks (MR-NN). MR-GPR, based on Gaussian processes renowned for their adaptability to diverse data structures, encounters scalability issues especially when handling large datasets. To address these limitations, this paper introduces MR-NN, an extension of MR-GPR, leveraging neural networks (NN) to manage large datasets and capture complex nonlinear dynamics effectively. We present a comprehensive evaluation of both methods through a classical control problem of the inverted pendulum, a system well-recognized for its nonlinear behavior. Numerical experiments are conducted to compare the methods in terms of control performance, computational efficiency, and reliability.

This paper mainly discusses the impacts from the time delay of advertising and the reference price of consumer onto the cooperative advertising decisions. In our model, both the retailer’s platform goodwill (RPG) and the manufacturer’s brand goodwill (MBG) involved with their own advertising efforts are modeled by the delay differential equations. In addition, the RPG and the MBG have positive effects on the consumer’s reference price. The equilibrium advertising strategies of two channel members are firstly derived within the centralized and decentralized scenarios by resorting to the optimal control theory. Besides, the corresponding equilibrium advertising strategies and optimal channel profits are compared, which shows that the centralization brings higher profit to whole channel only when the time delays locate within a specified geometric region and the existence of time delay caused by manufacturer’s advertising also implies smaller advertising participation rate provided by the retailer. Subsequently, a new mechanism is designed to coordinate the supply chain system in the decentralized case under the consignment mode. Finally, a numerical example is employed to illustrate the impacts from key system parameters onto the coordination results when the supply chain is coordinated.

The robust control problem of permanent magnet synchronous motor (PMSM) is discussed in this paper. Both iron loss and voltage saturation are considered in the analysis process in order to close to the actual projects. A controller design method, which is called the Hamiltonian-based method, is proposed for the PMSM control system. The responses of each current component and speed are analyzed when load torque disturbance exists. The proposed controller enables the closed-loop system to have robust performance criteria without violating saturation. It is worth noting that a more general truncation-inequality technique is used to deal with saturation, which reduces the conservatism of parameter selection. In addition, the corresponding results are given for two special cases. When there is no saturation and no load torque disturbance, the system is asymptotically stable at the equilibrium point. Simulation results and experimental study show that the proposed method has good load disturbance suppression ability and can ensure the robust performance of the PMSM in EV driving systems.

A fault-tolerant control approach is proposed, for a pumping airborne wind energy system (AWES) comprising a tethered fixed-wing aircraft with integrated propellers for vertical take-off and landing (VTOL). First, the flight control design for the traction phase of the system, when the tethered aircraft has to fly in loops using the rudder, is presented. Then, the presence of the propellers, that are normally not used in the traction phase, is exploited to obtain a fault tolerant controller in case of rudder malfunctioning. The approach detects a possible discrete control surface fault and compensates for the loss in actuation by using the VTOL system. A sophisticated model of the system is used to analyse the performance of the proposed technique. The main finding is that the approach is able to handle abrupt rudder faults with high tolerance.

This paper investigates the event-triggered quantized stabilization of uncertain neutral systems. Specifically, based on the suitable Lyapunov functional and relax quadratic function negative determination property, event-triggered quantized controllers and other variable parameters are co-designed, allowing neutral network systems with or without uncertainties to stabilize asymptotically via variable separation approaches. Additionally, the feasible criteria are characterized as convex optimization problems. An example demonstrates the effectiveness and less conservativeness of the proposed method.

Iterative learning control (ILC) has been considered as a promising alternative for the control of pneumatic muscle actuator (PMA). However, this controller suffers from a challenge that it is difficult to deal with the complex nonlinear characteristics of PMA. To solve this problem, a novel iterative learning model predictive control (ILMPC) approach, by utilizing the data-driven model, is designed and analyzed in this article. Firstly, the dynamics of PMA is converted into Takagi-Sugeno (T-S) fuzzy nonlinear auto-regression with exogenous inputs (NARX) model, and the differential evolution (DE) estimation algorithm is applied to estimate parameters of the NARX model by utilizing the input and output data. Secondly, the controller of ILMPC is designed and the convergence performance of the controller is verified through theoretical analysis. Finally, the capability of this control method is confirmed via experimental study. Experimental results demonstrate that the proposed ILMPC can achieve satisfactory tracking control and exhibits robustness against load varying.

This article investigates a heterogeneous vehicular platoon control problem, in which prescribed tracking performance can be achieved, it is assumed that the vehicle subjects to asymmetric nonlinear actuator saturation, dead-zone nonlinearity and actuator faults. Based on improved exponential spacing policy, an adaptive fault-tolerant sliding-mode control scheme is proposed to guarantee individual vehicle stability, string stability and traffic flow stability. The hypothesis that the spacing errors at initial time are zero is removed by employing the novel exponential spacing policy. Furthermore, to attenuate the harmful effects of actuator faults, saturation and dead-zone nonlinearity, a compensation system based on radial basis neural network (RBFNN) is established. Finally, the effectiveness of the proposed control scheme is verified by simulation results.

In this article, a predefined-time control framework is presented to accomplish the leader-following consensus for Euler-Lagrange (EL) systems, in which a dynamical leader is considered and only a subset of the followers can obtain the information from leader. In order to address the aforementioned problem, a predefined-time control algorithm has been proposed. To be specific, a set of distributed estimators are firstly established to acquire the estimated states of the leader at a predefined time. Then, by applying the designed controller based on sliding surface control, the predefined-time leader-following consensus (PTLFC) problem has been addressed. Moreover, based on Lyapunov argument and input to state stability criterion, sufficient conditions for predefined-time stability are derived. Finally, a large number of simulation examples are given to illustrate the validity of the main results.

This paper investigates the problem of input-constrained optimal control for nonaffine nonlinear discrete-time systems in the presence of an inaccurate model. To address this problem, a bounded function is used to convert the input-constrained optimal control problem into an unconstrained counterpart. Additionally, an optimal stage cost function is introduced to quantify the discrepancy between the inaccurate model and the true system dynamics. Subsequently, a policy iteration based stage cost learning (PISCL) algorithm is proposed to obtain the optimal stage cost function and the convergence of the algorithm is proved. The proposed approach provides a new framework for addressing input-constrained control problems of nonaffine nonlinear systems with an inaccurate model, bridging the gap between model-based and data-based approximate dynamic programming techniques. Numerical experiments validate the effectiveness of the PISCL algorithm in obtaining the constrained optimal control policies for nonaffine nonlinear discrete-time systems without precise system models.