IFAC Journal of Systems and Control最新文献

Utilizing passive filters such as L, LC and LCL is preferred to cancel out high-frequency harmonics caused by pulse width modulation of voltage source inverters. However, the LC and LCL filters have shown better harmonic attenuation than the conventional L filter. Nevertheless, the control process of LC and LCL filters is more complicated due to their higher-order dynamics. The problem gets more challenging in the presence of uncertainties such as load and grid impedance variations. To overcome these challenges, two novel adaptive neural dynamic surface controllers are proposed for LC and LCL filters in the load and grid-connected modes, respectively. Meanwhile, the issue of computational complexity inherent in the conventional backstepping method is avoided here by utilizing the dynamic surface control technique. Furthermore, the matched and unmatched uncertainties of LC/LCL filters are approximated via multi-input multi-output radial basis function neural networks. Stability of the closed-loop systems is guaranteed by converging the tracking errors to a small neighborhood of the origin. Simulations are given to illustrate the effectiveness and potential of the proposed adaptive neural dynamic surface control methods under the load and grid impedance changes.

This paper addresses the problem of controlling a stand-alone photovoltaic (PV) energy conversion system integrated with a battery energy storage system. The study focuses on a series association of PV panels, a DC/AC converter, a Li-ion battery, and a DC load. The intermittent nature of PV power and frequent variations in load demand decrease battery lifetime and its charging performance. To mitigate these issues, an efficient battery charge controller is proposed to instantaneously balance the PV power flow delivered to the DC load and the battery, ensuring optimal utilization of PV power and appropriate battery charging. Based on available solar power, battery state of charge (SOC), and DC load demand The controller adapts to three charging modes, namely, maximum power point tracking (MPPT) charging mode, constant current (CC) charging mode, and constant voltage (CV) charging mode. Additionally, a novel energy management algorithm is designed to ensure battery safety and determine the system’s mode of operation, considering weather conditions and load demand variations. Interestingly, no solar irradiation or battery SOC sensors are required for the implementation of the control system. Nonlinear and robust controllers are developed to provide the necessary control input laws for the management algorithm. The robustness and stability of the system are verified using the Lyapunov theory. Furthermore, the paper quantifies the performance of the proposed strategy through a comparative analysis using integral of absolute error (IAE) indices against two conventional control approaches. Simulation results validate the effectiveness of the proposed controller strategy, demonstrating its high performance and ability to meet the specified objectives. This work presents an innovative approach to enhance the efficiency and reliability of stand-alone PV energy conversion systems with battery storage, offering promising prospects for sustainable energy applications.

Electro-hydraulic actuators (EHAs) have become a preferred alternative to traditional hydraulic actuators with valve control systems due to their numerous advantages, making them an ideal choice for applications requiring high-precision force or position control. However, the highly complex nonlinear nature of EHAs makes modelling and controlling them a challenging task. To address this challenge, a new position control approach has been proposed for an EHA system using a combination of a feedforward online-tuning PID (FOPID) controller and an adaptive grey predictor (AGP), known as the feedforward online-tuning adaptive grey predictor (FOAGP). The FOPID controller is constructed based on PID controller and fuzzy logic algorithm to control the EHA system towards referred trajectory, while an updating rule that consists of robust checking terms optimizes its parameters online to minimize control error. The AGP predictor is an important aspect of the proposed approach. It consists of a self-tuning step size mechanism, which estimates the performance of the plant to tune the parameters of the controller and create an additive control signal that is used to counteract environment noises and perturbations. This approach significantly improves control performance by reducing the effect of disturbances and sensor noises on the system. The FOAGP approach was tested in simulation to investigate its effectiveness. The results showed that the proposed approach outperformed other existing control methods, with a higher accuracy and better control performance. One of the significant advantages of the FOAGP approach is its ability to learn and adapt to changing system dynamics. The learning mechanism used in the FOPID controller allows the system to optimize its parameters online, which is especially useful in systems with varying operating conditions. The AGP predictor also continuously adjusts its parameters to accurately estimate the system output, making it an effective tool for controlling EHAs. The proposed approach offers a significant improvement in control performance, making it a better alternative to traditional control methods. This approach can be applied to various EHA systems, including those used in aerospace, automobile, and robotic applications, among others.

This paper proposes model-based and model-free control approaches to monitor the feeding rate and water quality for fish-growth tracking in aquaculture systems. The representative fish-growth model is revisited, which describes the total biomass change by incorporating the fish population density and mortality. Due to the challenging task of measuring the total fish biomass and population data, the new dynamic population model is validated with individual fish-growth data for tracking control. Ammonia exposure is a significant challenge in the fish-population growth tracking problem, affecting fish health and survival. To address this challenge, traditional and optimal controllers are first designed to track the weight reference within suboptimal temperature and dissolved oxygen (DO) profiles under various un-ionized ammonia (UIA) exposure levels by manipulating relative feeding. Then, a Q-learning approach is proposed to learn an optimal feeding-control policy from simulated data on fish-growth weight trajectories while managing ammonia effects. The proposed Q-learning feeding control prevents fish mortality and achieves good tracking errors for fish weight under UIA levels. However, it maintains a relative food consumption that potentially underfeeds fish. Finally, an optimal predictive algorithm that includes the temperature, DO, and UIA is proposed to optimize the feeding and water quality of the dynamic fish-population growth process, indicating that fish mortality is decreased and food consumption is reduced in all cases of UIA exposure.

This paper presents the stability analysis of integrated energy systems under network environment. The networked-control integrated energy systems involve time-delay in the control loop. These time-delays are time-invariant or time-varying in nature. Further, it affects the stability and dynamic performance of the integrated energy systems. In this paper stability analysis of networked-control integrated energy systems are done using Lyapunov–Krasovskii functional and linear matrix inequality techniques. The maximum amount of time-delay that establishes the stability of the integrated energy systems is determined and controller is designed with concern to the time-delay. The effect of electric vehicles and battery energy storage system in stability delay margins of integrated energy systems is also addressed and the numerical simulations are done to verify the effectiveness of the presented results.

This work proposes a solution for power system stability by utilizing prediction-based event-triggered control (ETC) in the discrete-time domain. The proposed control method can handle a large sampling period, and the event-triggered mechanism (ETM) is applied in both controller and actuator loops to reduce the network’s computational and communication burden. Input and output (I/O) quantizers are used to avoid the quantization error that arises due to sampling and are also included in the stability analysis. The proposed control strategy is evaluated under various load scenarios using three-area interconnected power systems. The results demonstrate that the proposed approach saves 25.5%, 22%, and 23.5% of channel bandwidth in each area, as compared to the conventional time-triggered control approach. A comparative study shows that the proposed work outperforms recently reported works in terms of better event triggering number, average inter-event time, and performance indices. The effectiveness of the proposed control schemes is further validated by considering uncertainty in system parameters and typical power system nonlinearities. The study also illustrates the integration of renewable energy resources (RERs) and electric vehicles (EVs). The closed-loop system stability is proved theoretically using uniform ultimate boundedness and validated through simulations in MATLAB R2018a.

Nonlinear control for autonomous reentry space vehicles has been a topic of intensive research during the last years in the area of aerospace science and technology. The associated dynamic model is obtained by expressing position variables and orientation angles of the space vehicle in different coordinate frames, namely an earth-fixed, an earth rotating and a body fixed frame. In this article, a nonlinear optimal control approach is proposed for the dynamic model of reentry space vehicles. It is proven that the longitudinal motion dynamic model of reentry space vehicles is differentially flat and a flatness-based controller is designed about it. Next, in the nonlinear optimal control approach, the dynamic model of the reentry space vehicle undergoes approximate linearization around a temporary operating point that is recomputed at each time-step of the control method. The linearization relies on Taylor series expansion and on the associated Jacobian matrices. For the linearized state-space model of the reentry space vehicle a stabilizing optimal (H-infinity) feedback controller is designed. This controller stands for the solution to the nonlinear optimal control problem under model uncertainty and external perturbations. To compute the controller’s feedback gains an algebraic Riccati equation is repetitively solved at each iteration of the control algorithm. The stability properties of the control method are proven through Lyapunov analysis. The proposed nonlinear optimal control approach achieves fast and accurate tracking of reference setpoints under moderate variations of the control inputs and a minimum dispersion of energy.

Some of the mechanisms that generate neuronal signals are known at the cellular level and rest on a balance of excitatory and inhibitory interactions within and between populations of neurons. Neural mass models assume that a neuronal population can be approximated using very few state variables, generally limited to mean membrane currents, potentials, and firing rates. This article deals with nonlinear parametric identification problems in neurophysiologically based models simulating brain effective connectivity. We propose a novel approach which utilizes optimal control theory for partially flat stochastic differential systems. The optimization-based approach to effective connectivity characterization has been tested through simulation experiments and compared with the extended and unscented Kalman filters. A variety of case studies have been successfully used for connectivity parameter identification: constant functions, step functions, periodic functions and random functions.

In this paper the satellite system in which fluctuating aerodynamic torque and the radial perturbation about the pitch motion of orbit is analyzed. The objectives of the paper are three-fold. The first objective is the ‘Carleman linearization representation into the nonlinear stochastic evolution of the Markov process’. The second is to filter process and measurement noises in the satellite dynamics parameters in the Carleman setting. The third is to find the stability and convergence condition of satellite dynamics. The first is achieved by unifying the generating function, Carleman embedding, Itô stochastic differential rules and the finite closure with Kronecker algebra. Concerning the second objective, we recast the finite-dimensional Stochastic Differential Equations (SDE) of the satellite dynamics into a finite system of bilinear SDE via the Carleman embedding. The third objective is to achieve the Lyapunov function and asymptotic stability condition for the satellite stochastic dynamics involving the ‘Stratonovich differential’. In this paper, we demonstrate the utility of the satellite dynamics filtering in the Carleman-based filtering via ‘its convergence analysis as well as its superiority with available methods’, i.e., the benchmark extended Kalman filter, Gaussian second-order filter and Kushner–Stratonovich higher-order filter. From simulation performed it can be said that the Carleman-based filtering is superior than other benchmark filters in terms of their Absolute Filtering Error (AFE) of conditional means and conditional variances of the satellite dynamics states.

The control problem for the nonlinear dynamics of robotic and mechatronic systems with electropneumatic actuation is solved with the use of a flatness-based control approach which is implemented in successive loops. The state-space model of these systems is separated into a series of subsystems, which are connected between them in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input–output linearized flat systems. In this chain of $i=1,2,\dots ,N$ subsystems, the state variables of the subsequent ($i+1$th) subsystem become virtual control inputs for the preceding ($i$th) subsystem, and so on. In turn, exogenous control inputs are applied to the last subsystem and are computed by tracing backwards the virtual control inputs of the preceding $N-1$ subsystems. The whole control method is implemented in successive loops and its global stability properties are also proven through Lyapunov stability analysis. The validity of the control method is confirmed in two case studies: (a) control of an electropneumatic actuator, (ii) control of a multi-DOF robotic manipulator with electropneumatic actuators.