Control Design Techniques Research Articles

Adaptive fuzzy optimal output-feedback fault-tolerant control for the USV system with intermittent actuator faults

This paper presents a novel fuzzy optimal output-feedback fault-tolerant control (FTC) method for uncertain nonlinear unmanned surface vehicle (USV) systems, which are subject to intermittent actuator failures and unmeasured states. The proposed optimal controller is composed of a fuzzy feedforward fault-tolerant output-feedback controller and a fuzzy error feedback optimal controller. The fuzzy feedforward fault-tolerant output-feedback controller is designed by using the fuzzy state observer and backstepping control design technique. The fuzzy optimal feedback controller is given by adopting adaptive dynamic programming (ADP) theory. It is proven that the presented fuzzy optimal FTC scheme is able to ensure that the USV system is uniformly ultimately bounded (UUB), and the optimal control performance is achieved. The validity is confirmed by computer simulation results.

Advanced digital control strategies for switching buck converters: A modeling perspective

This paper highlights the significance of digital controllers over analog counterparts, emphasizing the utilization of optimization techniques such as MPC. It explores the benefits of adaptive controllers compared to MPC, showcasing simulations that dynamically adjust controller parameters to accommodate system and environmental variations. Addressing uncertainties, nonlinearities, and time-varying dynamics, the paper employs MPC-DC technique as an adaptive approach aiming to merge the advantages of digital controllers and MPC.The core concept revolves around leveraging the digital controller’s inherent advantages over analog counterparts and enhancing control precision through MPC, particularly in power converter applications. The study introduces a discrete root locus technique for digital controller design, focusing on DC-DC Buck Converters, with subsequent MPC fine-tuning. Demonstrating efficacy, the approach enhances switching converters’ performance, minimizing passive component size and costs while ensuring robust load and line regulation. Moreover, the proposed MPC based digital controller design methodology extends to other switching converters like Boost, Cuk, and Sepic Converters. Finally, the validation via MATLAB/Simulink simulations underscores the efficacy of the proposed model.

Modified preview repetitive control with equivalent-input-disturbance based on two-dimensional model

This paper deals with the problem of two-dimensional (2D) system-based modified preview repetitive control (MPRC) with equivalent-input-disturbance (EID) for continuous-time systems. First, an EID-based MPRC law is used to improve the tracking performance and disturbance attenuation. Next, by using a new equality constraint, a continuous-discrete 2D system is established and the design problem of the EID-based MPRC law is transformed into the stability problem of the 2D system. Then, we employ the 2D system theory together with the linear matrix inequality (LMI) approach, a sufficient condition is derived to ensure the stability of the closed-loop system. By solving an LMI, the gains of the controller and state observer can be obtained. Finally, two numerical examples are provided to illustrate the effectiveness and superiority of the developed controller design technique.

Fuzzy adaptive asynchronous sampled-data consensus control for nonlinear multi-agent systems under DoS attacks

This article investigates the problem of fuzzy adaptive resilient asynchronous sampled-data consensus control (SDCC) for a class of uncertain nonlinear strict-feedback multi-agent systems (MASs) under denial-of-service (DoS) attacks and with a directed graph. Firstly, the upper bounds on the sampling intervals and a distributed sampled-data observer are proposed to estimate the leader's output and its high-order derivatives under DoS attacks. Then, the sufficient conditions to ensure the asymptotic convergence of the designed distributed sampled-data observer are established. Based on the distributed sampled-data observer and backstepping control design technique, a distributed fuzzy adaptive resilient control algorithm is developed. It is proved that the proposed control scheme can guarantee that the controlled MASs are stable, and the consensus errors converge to a small neighbourhood of zero. Finally, a simulation example on heterogeneous vehicular platoons verifies the feasibility of the proposed control scheme.

Predefined time fuzzy adaptive control for stochastic nonlinear systems with limited time interval output constraints

This article investigates the problem of fuzzy adaptive predefined time tracking control for stochastic nonlinear systems with limited time interval output constraints. Firstly, the considered output constraints occur within a finite time interval after the system starts running. By constructing shift functions and barrier Lyapunov functions (BLFs), the constrained system is converted into an unconstrained system, which solves the problem of function discontinuity resulting from output constraints within limited time intervals. Then, with the help of adaptive backstepping technique and predefined time control method, which reduces the parameters to be designed and removes the limitation of relatively large initial control input in existing approaches. The designed control technique ensures the boundedness of all variables of the stochastic system, the tracking error converges within a predetermined time, and the outputs do not exceed their constraint boundaries in a finite time interval. And this algorithm is applicable to both infinite time constraints and unconstrained outputs cases. Finally, the validity of this approach is demonstrated through simulation examples.

Design and Experimental Validation of an Adaptive Multi-Layer Neural Network Observer-Based Fast Terminal Sliding Mode Control for Quadrotor System

This study considers the numerical design and practical implementation of a new multi-layer neural network observer-based control design technique for unmanned aerial vehicles systems. Initially, an adaptive multi-layer neural network-based Luenberger observer is designed for state estimation by employing a modified back-propagation algorithm. The proposed observer’s adaptive nature aids in mitigating the impact of noise, disturbance, and parameter variations, which are usually not considered by conventional observers. Based on the observed states, a nonlinear dynamic inversion-based fast terminal sliding mode controller is designed to attain the desired attitude and position tracking control. This is done by employing a two-loop control structure. Numerical simulations are conducted to demonstrate the effectiveness of the proposed scheme in the presence of disturbance, parameter uncertainty, and noise. The numerical results are compared with current approaches, demonstrating the superiority of the proposed method. In order to assess the practical effectiveness of the proposed method, hardware-in-loop simulations are conducted by utilizing a Pixhawk 6X flight controller that interfaces with the mission planner software. Finally, experiments are conducted on a real F450 quadrotor in a secured laboratory environment, demonstrating stability and good tracking performance of the proposed MLNN observer-based SMC control scheme.

A Novel CEM-Based 2-DOF PID Controller for Low-Pressure Turbine Speed Control of Marine Gas Turbine Engines

Gas turbine engines have several advantages over piston reciprocating engines, such as higher output per unit volume, reduced vibration, rapid acceleration and deceleration, high power output, and clean exhaust gases. As a result, their use for propulsion in ships has been steadily increasing. However, gas turbine engines exhibit significant parameter variations depending on the rotational speed, making the design of controllers to ensure system stability while achieving satisfactory control performance, a very challenging task. In this paper, a novel CEM-based 2-DOF PID controller design technique is proposed to ensure the stability of a gas turbine engine while improving tracking and disturbance rejection performance. The proposed controller consists of a PID controller focused on enhancing disturbance rejection performance and a set-point filter to improve tracking performance. The set-point filter is composed of gains from the controller and a single weighting factor. When tuning the gains of the controller, the maximum sensitivity is considered to maintain an appropriate balance between system stability and response performance. The key novelty of this study can be summarized in two main points. One is that the controller is designed by matching characteristic equations, and by setting the roots of the desired characteristic equation as multipoles, the gains of the PID controller can be tuned with only one adjusting variable, making the tuning of the 2-DOF controller easier. The other is that the controller parameters are tuned based on maximum sensitivity, thus taking into account the robust stability of the control system. To demonstrate the feasibility of the proposed method, simulations are conducted for four scenarios using various performance indices.

Adaptive neural network dynamic surface optimal saturation control for single‐phase grid‐connected photovoltaic systems

AbstractAn adaptive neural network (NN) based optimal saturation control scheme is investigated for single‐phase grid‐connected photovoltaic (PV) systems by incorporating dynamic surface control (DSC) and adaptive dynamic programming (ADP) based on the backstepping control design framework. For each backstepping step, a critic‐actor architecture is constructed via reinforcement learning (RL), and the PV system is optimized according to the cost function in the architecture. Due to the nonlinearity, it is difficult to solve the Hamilton–Jacobi–Bellman (HJB) equation. The neural networks (NNs) are employed to approximate the solution of the HJB equation such that the optimal virtual control and the actual controller are obtained. By considering control input symmetric saturation nonlinearity link, constraints on pulse width modulation (PWM) are ensured. On this basis, the combination of backstepping control design and dynamic surface technique is used to overcome the shortcomings of “differential explosion” and simplify calculations. Based on the Lyapunov method, the stability analysis proves that all signals of the closed‐loop PV systems are semiglobally uniformly ultimately bounded (SGUUB). Simulation experiments and comparative results are given to verify the efficacy of the studied control strategy.

Adaptive neural optimal tracking control for uncertain unmanned surface vehicle

This article proposes a novel adaptive neural optimal control scheme for the uncertain unmanned surface vehicle (USV) system. The proposed adaptive neural optimal control scheme is composed of a neural feedforward controller and a neural optimal feedback controller. The neural feedforward controller is designed by using neural networks (NNs) and the backstepping control design technique to make the unknown nonlinear dynamics of USV system to be stabilized. The neural optimal feedback controller is designed based on adaptive dynamic programming (ADP) theory. It is proved that the formulated adaptive neural optimal control scheme can achieve all signals in USV system are uniformly ultimately bounded (UUB) and the cost function is minimized. The simulation and comparison results demonstrate the effectiveness of the proposed optimal control scheme.

The Stabilization of a Nonlinear Permanent-Magnet- Synchronous-Generator-Based Wind Energy Conversion System via Coupling-Memory-Sampled Data Control with a Membership-Function-Dependent H∞ Approach

This study presents the coupling-memory-sampled data control (CMSDC) design for the Takagi–Sugeno (T-S) fuzzy system that solves the stabilization issue of a surface-mounted permanent-magnet synchronous generator (PMSG)-based wind energy conversion system (WECS). A fuzzy CMSDC scheme that includes the sampled data control (SDC) and memory-sampled data control (MSDC) is designed by employing a Bernoulli distribution order. Meanwhile, the membership-function-dependent (MFD) H∞ performance index is presented, mitigating the continuous-time fuzzy system’s disturbances. Then, by using the Lyapunov–Krasovskii functional with the MFD H∞ performance index, the data of the sampling pattern, and a constant signal transmission delay, sufficient conditions are derived. These sufficient conditions are linear matrix inequalities (LMIs), ensuring the global asymptotic stability of a PMSG-based WECS under the designed control technique. The proposed method is demonstrated by a numerical simulation implemented on the PMSG-based WECS. Finally, Rossler’s system demonstrates the effectiveness and superiority of the proposed method.

Double-Loop Controller Design of a Single-Phase 3-Level Power Factor Correction Converter

This paper presents a study on the double-loop controller design technique for a single-phase power factor correction (PFC) three-level (TL) boost converter. Designing a double-loop controller using conventional methods is challenging due to the 120 Hz voltage ripple in the output voltage. This study proposes new double-loop control design methods using a band-stop filter and MATLAB SISOTOOL, detailed in a step-by-step sequence. A band-stop filter with a 120 Hz stop band is applied to the double-loop controller design. Modeling based on the state-space equation, applicable to the full duty range, is constructed to obtain the transfer function. The double-loop controller structure is then designed, and optimal gains that satisfy the design requirements are obtained through automatic tuning using the MATLAB SISOTOOL library. The simulation results demonstrate the performance of the proposed method, which is further verified by experimental results.

A note on boundary feedback stabilization for degenerate parabolic equations in multi-dimensional domains

Abstract In this paper, we are concerned with the problem of stabilization of a degenerate parabolic equation with a Dirichlet control, evolving in bounded domain 𝓞 ⊂ ℝ d , d ≥ 2. We apply the proportional control design technique based on the spectrum of the linear operator which governs the evolution equation. The stabilizing feedback control, we design here, is linear, of finite-dimensional structure, easily manageable from the computational point of view.

MRAC based intelligent PID controller design technique for a class of dynamical systems

MRAC based intelligent PID controller design technique for a class of dynamical systems

Efektivitas Layanan Bimbingan Klasikal dengan Metode Diskusi untuk Meningkatkan Motivasi Belajar Siswa

The problem that will be raised in this study is whether there is an effective classical guidance service with the discussion method to increase student motivation at SMA N 3 Pati. Based on the problem formulation, the purpose of this study was to find out how effective the discussion method classical guidance service was to increase student motivation at SMA N 3 Pati. This type of research is experimental. This research method uses a true experimental design with a posttest-only control design technique. The population in this study is class XI MIPA 1, XI MIPA 2, XI MIPA 3, XI MIPA 4, XI MIPA 5, XI MIPA 6, XI MIPA 7, XI MIPA 8, XI MIPA 9, XI IPS 1, XI IPS 2, and XI IPS 3 with a total of 377 students. The sample in this study was selected randomly which was then divided into 2 classes, namely XI MIPA 4 experimental class and XI MIPA 5 control class. The sampling used is cluster random sampling. The results of this research analysis revealed that the independent sample test test obtained post-test data with a value of sig, 0.006 <0.05, which means that there is a difference in the increase in student motivation to learn in the group given treatment or treatment through classical guidance services with the discussion method.

Finite and fixed‐time stabilization of discrete‐time systems using passivity‐based control

AbstractIn this paper, we propose a new passivity‐based controller design technique for discrete‐time fully actuated systems. The controller establishes finite‐time and fixed‐time convergence of dynamical system trajectories to an equilibrium state. A new form of a dissipation function, selected a priori, is introduced to design a feedback control rule to achieve such convergence properties. An energy shaping and damping injection methodology is extended to achieve non‐asymptotic stabilization. A numerical example to validate the proposed methods is provided.

Memory sampled-data control design for attitude stabilization of uncertain spacecraft with randomly missing measurements

This paper focuses on stabilizing the attitude of the rigid spacecraft under the sampled-data control design technique with constant communication delay. The system dynamics with model uncertainty (perturbation) and missing measurements are considered. The aim is to design a desirable attitude control that ensures the stabilization of the rigid spacecraft with an optimal H∞ performance level. A novel Lyapunov–Krasovskii functional is developed, incorporating looped characteristics, for the proposed study on the rigid spacecraft model by developing relevant new terms. Making use of the Lyapunov function as well as free-matrix-based integral inequality, sufficient stability criteria are established to assure the stability of the rigid spacecraft model. Furthermore, the desired sampled-data control gain matrices are acquired through the solution of linear matrix inequalities, which guarantees the asymptotic stability of the rigid spacecraft model. Finally, the numerical simulation demonstrates the efficacy of the theoretical study proposed for the rigid spacecraft model.

Emulation techniques for scenario and classical control design of tokamak plasmas

The optimization of scenarios and design of real-time-control in tokamaks, especially for machines still in design phase, requires a comprehensive exploration of solutions to the Grad–Shafranov (GS) equation over a high-dimensional space of plasma and coil parameters. Emulators can bypass the numerical issues in the GS equation, if a large enough library of equilibria is available. We train an ensemble of neural networks to emulate the typical shape-control targets (separatrix at midplane, X-points, divertor strike point, flux expansion, and poloidal beta) as a function of plasma parameters and active coil currents for the range of plasma configurations relevant to spherical tokamaks with a super-X divertor, with percent-level accuracy. This allows a quick calculation of the classical-control shape matrices, potentially allowing real-time calculation at any point in a shot with submillisecond latency. We devise a hyperparameter sampler to select the optimal network architectures and quantify uncertainties on the model predictions. To generate the relevant training set, we devise a Markov-chain Monte Carlo algorithm to produce large libraries of forward Grad–Shafranov solutions without the need for user intervention. The algorithm promotes equilibria with desirable properties, while avoiding parameter combinations resulting in problematic profiles or numerical issues in the integration of the GS equation.

Sliding-mode-based impulsive control for a class of time-delay systems with input disturbance

This paper addresses the impulsive stabilization problem for a class of time-delay systems in the presence of input disturbances. A novel impulsive control law is constructed, which is based on the discrete-time equivalent control design technique. The sliding-mode-based impulsive control law allows counteracting the disturbance effect using its previous step value. To adapt in real time to the variation in sampling periods, the sliding function is designed as a function of impulse interval. Piecewise Lyapunov functions based on the partition on the maximum impulse interval are introduced to deal with the time-varying structure of the sliding function. Sufficient conditions for exponential input-to-state stability (EISS) of the impulsively controlled systems are derived, in which the EISS gain characterizes the attenuation ability of the proposed impulsive control law on the disturbance estimation error. The disturbance attenuation performance of the proposed impulsive control strategy is demonstrated through the time-delay Chua’s circuit.

Sampled-Data Cooperative Adaptive Cruise Control for String-Stable Vehicle Platooning with Communication Delays: A Linear Matrix Inequality Approach

This study aims to propose a sampled-data control technique, utilizing a linear matrix inequality (LMI) approach, to achieve string-stable vehicle platooning in a cooperative adaptive cruise control (CACC) system with communication delays. To do this, a decentralized sampled-data controller design technique that combines one controller using sensor measurements and another one utilizing vehicle-to-vehicle (V2V) communication, ensuring both individual and string stability, is proposed first. Next, a memory sampled-data control (MSC) approach is presented to account for transmission delays in V2V communication. Additionally, an improved Lyapunov–Krasovskii functional (LKF) is presented to improve computational complexity and sampling performance. The design conditions are formulated as linear matrix inequalities (LMIs) in the time domain, facilitating efficient stability analysis and optimization. Finally, vehicle platooning simulations are provided to validate the effectiveness and feasibility of the proposed technique.

Robust and quantized repetitive tracking control for fractional‐order fuzzy large‐scale systems

SummaryIn this article, the decentralized repetitive tracking controller design for fractional‐order large‐scale Takagi–Sugeno fuzzy system with time delays is developed. We mainly focus on the design of a decentralized repetitive tracking controller based on the Lyapunov stability theory, by which the addressed large‐scale system asymptotically stabilized with performance index. Further, the repetitive control with quantized signal is developed to ensure the good tracking performance with the presence of interconnected model and external disturbances. Specifically, a logarithmic quantizer is used to quantify the control signal which can reduce the data transmission rate in the network. Finally, a numerical example is presented to verify the effectiveness of the proposed controller design technique.