IFAC Journal of Systems and Control最新文献

The performance of feedforward control depends strongly on its ability to compensate for reproducible disturbances. The aim of this paper is to develop a systematic framework for artificial neural networks (ANN) for feedforward control. The method involves three aspects: a new criterion that emphasizes the closed-loop control objective, inclusion of preview to deal with delays and non-minimum phase dynamics, and enabling the use of an iterative learning algorithm to generate training data in view of addressing generalization errors. The approach is illustrated through simulations and experiments on an industrial flatbed printer.

In an earlier work, the Limited Communication-Distributed Model Predictive Control (LC-DMPC) scheme for controlling networks with dynamically coupled and locally constrained linear systems is presented. The scheme has an iterative and cooperative structure in which the systemwide optimum point is achieved by the distributed controllers requiring only coupled agents to cooperate. For assessing the network convergence, it is essential to possess complete information pertaining to all subsystems which has to be available to a central monitor. The current work endeavors to investigate this challenging point by distributing the network convergence within the local agents. With the new version of the algorithm, the convergence of the network is now guaranteed through the dissipativity of the local information exchange dynamics in the iteration domain. This is accomplished by introducing a set of free design variables into the distributed problems which are utilized by the agents to fulfill a simple local LMI problem employing local information only. Despite that the new approach is eliminating the necessity for a centralized observer, it may result in suboptimal local solutions. This is because the convergence of the information sharing loop between the coupled subsystems is insured by the small gain theorem. The new algorithm exhibits enhanced modularity due to the novel introduced convergence condition, implying that any updates to a subsystem physicals or design parameters do not require corresponding updates to neighboring subsystems or the network. The presented concepts are demonstrated by simulating a network of eight interconnected tanks.

Patient-ventilator asynchrony is one of the largest challenges in mechanical ventilation and is associated with prolonged ICU stay and increased mortality. The aim of this paper is to automatically detect and classify the different types of patient-ventilator asynchronies during a patient’s breath using the typically available data on commercially available ventilators. This is achieved by a detection and classification framework using an objective definition of asynchrony and a supervised learning approach. The achieved detection performance of the near-real time framework on a clinical dataset is a significant improvement over current clinical practice, therewith and, this framework has the potential to significantly improve the patient comfort and treatment outcomes.

In this work, an adaptive neuro-fuzzy (ANF) with a nonlinear proportional integral derivative (NLPID) controller (ANF-NLPID) has been proposed to solve the speed-tracking problem in electric vehicles (EVs) with brushless DC motor (BLDC). The ANF-NLPID controller eliminates the external disturbances caused by environmental or internal issues and uncertainties caused by parameter variations that lead to insufficient speed-tracking performance and increased energy consumption in EVs. An improved particle swarm optimization based on the chaos theory (IPSO-CT) algorithm is introduced to obtain the parameters of the fuzzy logic controller membership function and nonlinear PID controller and present the optimal performance for the EV. Employing the chaos technique with PSO helps to prevent the system from being trapped in the local minimum or optimum problem. The performance of the IPSO-CT algorithm is tested using a numerical comparison with other existing works. The outstanding performance of the ANF-NLPID controller has been evaluated by measuring the speed-tracking performance for the new European driving cycle (NEDC) and circular trajectories. Three case studies have been presented based on measuring the ANF-NLPID controller performance without disturbances, with disturbances, with disturbances, and uncertainties effects, respectively. Furthermore, the ANF-NLPID controller has been employed in different EV models to study the performance of this type of controller. Each of the three cases includes other existing works along with the ANF-NLPID controller to provide an insightful comparison using statistical functions to obtain each controller’s overall objective function value. The other existing works are fuzzy fractional order PID (Fuzzy FOPID), fuzzy integer order PID (Fuzzy IOPID), and integer order PID (IOPID) controllers. A sensitivity analysis has been conducted to test the proposed controller’s ability to present high speed-tracking performance while changing the disturbances and uncertainty rates. The results demonstrate that the ANF-NLPID controller is superior in speed-tracking control regulation for the new European cycle drive (NEDC) and circular speed trajectories and overcomes the external disturbances and uncertainties problem with low error results. In the end, the results reveal that the ANF-NLPID controller is more efficient than the fuzzy FOPID, fuzzy IOPID, and IOPID controllers in each case.

High performance solar photovoltaic models require precise knowledge of solar PV cell parameters. Numerous methods based on both deterministic and meta-heuristics have been developed for identifying solar cell parameters. However, the presented methods in the literature have a heavy computational load and limited ability to extract crucial parameters due to nonlinear dynamics of solar PV systems. In addition, because they rely on approximations to determine the objective function, the preceding state-of-the-art parameter estimation techniques do not provide accurate results. Thus, a novel chaos-inspired invasive weed optimization (CIIWO) has been developed for accurate solar PV system parameter estimation. Adding a chaotic map to IWO improves the performance of suggested method by expanding the search space globally. Moreover, to cope with the inadequacy in state-of-art objective functions, Newton Raphson approach has been combined with proposed CIIWO algorithm. The suggested approach for solar cell parametric identification has been tested on one-diode, two-diode, and three-diode models. By contrasting the outcomes with nine contemporary optimization strategies for parameter estimation, the superiority of the suggested algorithm has been demonstrated. Commercial PV cell RTC France has been used for the experimental validation. Comprehensive study of experimental data validates the efficacy and stability of the suggested algorithm.

The Routh–Hurwitz criterion is one of the most popular methods to study the stability of polynomials with real coefficients, given its simplicity and ductility. However, when moving to polynomials with complex coefficients, some generalization exist but are either incorrect or inapplicable to most practical cases. To fill this gap, we hereby propose a directed generalization of the criterion to the case of complex polynomials, broken down in an algorithmic form, so that the method is now easily accessible and ready to be applied. Then, we demonstrate its use to determine the external stability of a system consisting of the interconnection between a rotating shaft and a PI-regulator, obtaining the necessary and sufficient conditions to achieve stabilization of the system.

Magnetic levitation systems (MLS) provide friction-less, dependable, quick, and affordable operations in a variety of real life applications. One of the often-employed control strategies for the MLS is traditional cascade control, which uses internal model control (IMC)-based proportional–integral–derivative (PID) and proportional–integral (PI) controllers in its primary and secondary loops, respectively. Though this control structure succeeds in achieving levitation, it falls short in terms of set point tracking and disturbance rejection performance. To overcome this limitation, a bi-loop frequency shifted IMC proportional–derivative (FSIMC-PD) strategy is used in the primary loop retaining the conventional IMC-based PI controller in the secondary loop. Routh stability constraints are used to build the PD controller for stabilizing the MLS. Once the MLS is stabilized, an FSIMC-based PID controller for reference tracking is designed for the outer loop. In addition to simulation-based performance comparison of IMC-based PID-PI cascade scheme and the proposed scheme, experimental validation is also carried out on a laboratory scaled MLS setup. Furthermore, performance evaluation based on metrics like the integral of absolute error, integral of square error, integral of time weighted absolute error, total variation of the control signal and its maximum value are also carried out to vindicate the effectiveness of the suggested design. Robust stability analysis is carried out in addition to Nyquist stability considerations to vindicate that the FSIMC-based design is capable of yielding stable closed-loop response amid uncertainties in plant model parameters.

We define and analyze the operations of addition and intersection of linear time-invariant systems in the behavioral setting, where systems are viewed as sets of trajectories rather than input–output maps. The classical definition of addition of input–output systems is addition of the outputs with the inputs being equal. In the behavioral setting, addition of systems is defined as addition of all variables. Intersection of linear time-invariant systems was considered before only for the autonomous case in the context of “common dynamics” estimation. We generalize the notion of common dynamics to open systems (systems with inputs) as intersection of behaviors. This is done by proposing trajectory-based definitions. The main results of the paper are (1) characterization of the link between the complexities (number of inputs and order) of the sum and intersection systems, (2) algorithms for computing their kernel and image representations and (3) a duality property of the two operations. Our approach combines polynomial and numerical linear algebra computations.

This paper addresses the stabilization problem of linear time-varying delay systems with unmeasurable states. A novel augmented Lyapunov–Krasovskii functional (LKF) is proposed that effectively accounts for the impact of time delays, and an observer based stabilization controller is developed employing linear matrix inequality (LMI) based optimization technique. The utilization of extended reciprocally convex matrix inequality (ERCMI) is employed in this work to establish less conservative stabilization conditions within the framework of linear matrix inequalities (LMIs). By formulating a convex optimization problem, the observer gain and controller gains are determined. Simulation results are used to validate the design, and two numerical examples are considered to prove the usefulness of the proposed method over existing methods.

This article addresses the problem of perturbation in Unmanned Air Vehicle (UAV) quadrotors. Three subsystems are designed to provide a continuous and precise estimation of perturbation and residual perturbation. The three subsystems form a Hierarchical Perturbation Compensator (HPC), which is built to compensate for system dynamics uncertainties, non-modeled dynamics, and external disturbances. The nonlinear control Exponential Reaching Law Sliding Mode (ERLSM) is utilized with the HPC. Lyapunov stability analysis proves the stability of the entire compensator-controller system. This system has the ability to decrease unknown perturbation either external or internal. It also has the ability to maintain full control of the six-degree-of-freedom quadrotor. The system performance for position, altitude, and attitude control is demonstrated by analysis, simulation, and experiments.