Controller Design Research Articles

Global fast terminal sliding mode control for automatic carrier landing with environmental disturbances

Purpose The purpose of this paper is to solve the challenging problem of automatic carrier landing with the presence of environmental disturbances. Therefore, a global fast terminal sliding mode control (GFTSMC) method is proposed for automatic carrier landing system (ACLS) to achieve safe carrier landing control. Design/methodology/approach First, the framework of ACLS is established, which includes flight glide path model, guidance model, approach power compensation system and flight controller model. Subsequently, the carrier deck motion model and carrier air-wake model are presented to simulate the environmental disturbances. Then, the detailed design steps of GFTSMC are provided. The stability analysis of the controller is proved by Lyapunov theorems and LaSalle’s invariance principle. Furthermore, the arrival time analysis is carried out, which proves the controller has fixed time convergence ability. Findings The numerical simulations are conducted. The simulation results reveal that the proposed method can guarantee a finite convergence time and safe carrier landing under various conditions. And the superiority of the proposed method is further demonstrated by comparative simulations and Monte Carlo tests. Originality/value The GFTSMC method proposed in this paper can achieve precise and safe carrier landing with environmental disturbances, which has important referential significance to the improvement of ACLS controller designs.

Refined sinh cosh optimizer tuned controller design for enhanced stability of automatic voltage regulation

Refined sinh cosh optimizer tuned controller design for enhanced stability of automatic voltage regulation

New LMI approach to [formula omitted] control of discrete-time singular systems

This article studies the problem of feedback H∞ control for discrete-time singular system. The objective is to design the controller to make the closed-loop system admissible and satisfy the specified H∞ performance. Firstly, the admissibility of the closed-loop system is analyzed via the Lyapunov function and linear matrix inequality (LMI) method, the admissibility condition of the singular system with H∞ performance is given. Then, applying a useful matrix inequality lemma, the controller design condition for discrete-time singular system is shown by a LMI. Specially, the benefit of the proposed design result is their less conservatism, the validity of the viewpoint is proposed by numeric illustrations.

Adaptive event-triggering mechanism based Takagi-Sugeno fuzzy automatic generation controller design for offshore wind power system

Interconnected offshore wind power system (OWPS) attracts widespread attention due to its special advantage. With the development of wind power technology, higher requirements are put forward for the reliability of networked OWPS. Considering the nonlinear characteristics of OWPS and limited network bandwidth resource, an adaptive multi-event triggering mechanism based distributed Takagi-Sugeno fuzzy robust H∞ control scheme is investigated in this paper. A modeling method for OWPS integrating communication constraints is proposed. By Lyapunov stability theory, the sufficient condition for the asymptotical stability of OWPS has been derived. Compared with traditional control scheme, the proposed Takagi-Sugeno fuzzy controller provides smoother output power, smaller torque oscillation and higher wind energy utilization. Furthermore, the co-design strategy increases the utilization rate of network bandwidth. The feasibility of adaptive multi-event triggering mechanism based Takagi-Sugeno fuzzy controller is demonstrated through a simulation example.

Fast finite-time control for a class of stochastic low-order nonlinear system with uncertainties

This work explores the challenge of adaptive fast finite-time control for stochastic low-order nonlinear systems with uncertainties. It introduces an improved practical finite-time criterion that aims to achieve accelerated convergence. The criterion aims to extend to a larger range of stochastic nonlinear systems, accommodating state uncertainties and integrating Lyapunov functions with varying powers to ensure rapid convergence in the presence of uncertainties. The entire control design approach is based on the backstepping method. Specifically, fuzzy logic systems are employed in the recursive design of controllers and adaptive laws to address complex uncertain terms. Additionally, the desired magnitude target powers of the Lyapunov function in the criterion are effectively obtained by a clever overlapping selection of parameters. Finally, this study demonstrates the practical finite-time stability of stochastic low-order uncertain nonlinear systems and validates the effectiveness of the proposed approach through simulation example, highlighting its ability to achieve fast convergence.

Sampled-data based asynchronous control for persistent dwell-time switched systems: A delay-dependent Lyapunov functional approach

This paper mainly investigates the sampled-data control issue for switched systems with persistent dwell-time under input time delay. Unlike the common controller design, the existence of the delay makes it possible for the subsystem switching to occur during the sampling intervals, which leads to asynchronous problem. By means of the novel functional consisting of piecewise-continuous delay-dependent Lyapunov functional and looped-functionals, some solvability criteria are given under sufficient consideration of the switching frequency. Furthermore, the stability criterion is formulated by the sum of squares program and the global optimal solution is further provided, which exponentially stabilizes the system. Finally, a numerical example as well as an application design of the tunnel diode circuit are studied, both of which demonstrate the proposed method’s validity and feasibility.

Online learning of stable robust adaptive controllers design based on data-dependent feedback linearization with application to rotary inverted pendulum

AbstractThis study introduces an online (supervised) learning method to design nonlinear auto-regressive moving average (NARMA) controllers for feedback-linearized nonlinear single-input single-output (SISO) systems. The algorithm ensures Schur stability of the overall closed-loop system and provides adaptiveness and robustness for the NARMA controllers. The first stage of the method derives, in a data-dependent way, a feedback-linearized model of the nonlinear plant by using its input and output sample pairs. The method’s second stage, which constitutes the novel part of the presented study, builds up an online learning scheme for the linear auto-regressive moving average (ARMA) controller based on an already learned feedback-linearized model of the nonlinear plant. During online supervised learning, ARMA parameters of the feedback-linearized SISO plant model and the closed-loop ARMA model are computed by minimizing the plant identification and the closed-loop system tracking errors. Both errors are defined as $${{\ell}}_{1,{\varvec{\varepsilon}}}$$ ℓ 1 , ε , namely ε-insensitive loss functions that provide NARMA controller the robustness against noise and outliers. The proposed online learning control algorithm is applied to a rotary inverted pendulum model and to a real rotary inverted pendulum setup. The tracking performance of the developed controller is compared with those of the linear quadratic regulator and coupled sliding mode controller in terms of mean square error.

Bifurcation Exploration and Controller Design in a Fractional Oxygen–Plankton Model with Delay

Fractional-order differential equations have been proved to have great practical application value in characterizing the dynamical peculiarity in biology. In this article, relying on earlier work, we formulate a new fractional oxygen–plankton model with delay. First of all, the features of the solutions of the fractional delayed oxygen–plankton model are explored. The judgment rules on non-negativeness, existence and uniqueness and the boundedness of the solution are established. Subsequently, the generation of bifurcation and stability of the model are dealt with. Delay-independent parameter criteria on bifurcation and stability are presented. Thirdly, a hybrid controller and an extended hybrid controller are designed to control the time of onset of bifurcation and stability domain of this model. The critical delay value is provided to display the bifurcation point. Last, software experiments are offered to support the acquired key outcomes. The established outcomes of this article are perfectly innovative and provide tremendous theoretical significance in balancing the oxygen density and the phytoplankton density in biology.

Truncated predictive tracking control design for semi-Markovian jump systems with time-varying input delays

A state tracking control problem for a class of semi-Markovian jump systems (SMJSs) with input delay and disturbances is addressed in this study. In particular, an improved-equivalent-input-disturbance estimator approach (IEIDEA)-based truncated predictive controller is designed to synchronously compensate the effect of unknown external disturbances and time-varying delay. Specifically, by making use of IEIDEA, the unknown disturbances are estimated with high precision and incorporated into the control input channel, which eases the way to achieve the desired tracking performances. Specifically, the state predictor satisfies the delay-free state-space equation and is used in controller design along with prediction. In accordance with linear matrix inequality framework and Lyapunov stability theory, a set of adequate conditions are established to guarantee the state tracking performance of SMJSs. Finally, the numerical examples with simulation results are provided to validate the efficacy and practicability of the established control technique.

Distributed sliding mode control for reactive power sharing in an islanded microgrid

This paper presents a novel microgrid secondary control method based on distributed sliding mode control, addressing the issue of reactive power sharing in islanded microgrids. Acknowledging the complications posed by feeder impedance mismatch, the traditional droop control mechanism fails to facilitate efficient reactive power sharing. Utilizing a distributed framework, this paper initially introduces a reactive power ratio observer, enabling the observation of global average reactive power without the need for precise reactive power calculation as required in a conventional centralized control setup. Subsequently, employing the devised reactive power ratio observer, sliding mode control is integrated to achieve effective reactive power sharing. Furthermore, this paper broadens its scope to encompass both reactive power sharing and voltage restoration, resulting in the design of a comprehensive controller. Notably, the proposed distributed sliding mode controller not only achieves accurate reactive power sharing but also exhibits significant resilience in the face of load disturbances. The simulation results conducted under various scenarios effectively validate the efficacy of the proposed distributed sliding mode control strategy.

Development of a novel model matching decentralized controller design algorithm and its experimental validation through load frequency controller implementation in restructured power system using TMS320F28379D controlCARD

A three-stage decentralized controller design algorithm is developed to achieve setpoint tracking and disturbance rejection in MIMO systems with communication time delay, and nonlinearities such as saturation and dead band. The first stage involves reference model formulation. The second stage comprises equating approximate generalized time moments/approximate generalized Markov parameters of closed-loop system model with reference model at certain expansion points in s-plane to obtain synthesis-like equation, concerning products of unknown controller numerator and denominator polynomials. This process yields simultaneous linear equations, whose solution provides the coefficients of the product polynomials. In the third stage, the controller parameters are extracted from the product polynomials using exact model matching. The proposed method is illustrated by designing load frequency controller in traditional and restructured power systems under scenarios like system parameter uncertainties, random load variation, and time-varying communication delays. Simulation studies reveal the efficacy of the proposed technique over existing techniques. Furthermore, the practical implementation feasibility of the decentralized controller designed for the restructured power system is validated using the TMS320F28379D controlCARD.

Finite-Time H∞ Controllers Design for Stochastic Time-Delay Markovian Jump Systems with Partly Unknown Transition Probabilities.

This paper concentrates on the finite-time H∞ control problem for a type of stochastic discrete-time Markovian jump systems, characterized by time-delay and partly unknown transition probabilities. Initially, a stochastic finite-time (SFT) H∞ state feedback controller and an SFT H∞ observer-based state feedback controller are constructed to realize the closed-loop control of systems. Then, based on the Lyapunov-Krasovskii functional (LKF) method, some sufficient conditions are established to guarantee that closed-loop systems (CLSs) satisfy SFT boundedness and SFT H∞ boundedness. Furthermore, the controller gains are obtained with the use of the linear matrix inequality (LMI) approach. In the end, numerical examples reveal the reasonableness and effectiveness of the proposed designing schemes.

Fuzzy-based collective pitch control for wind turbine via deep reinforcement learning

Wind turbines (WTs) have highly nonlinear and uncertain dynamics due to aerodynamic complexity, mechanical factors, and fluctuations in wind conditions. Turbulence and wind shear add complexity to modelling, especially in constant power region (region 3). Thus, an effective control design demands a deep understanding of the nonlinearities and uncertainties. This paper suggests a novel model-free reinforcement learning (RL) collective pitch angle controller to operate efficiently in region 3. The proposed controller stabilizes generator speed, maximizes power output, and minimizes fluctuations while accommodating system uncertainties, nonlinearity, and pitch limits. The disparity between WT dynamics due to wind speed perturbations and uncertainties is measured using a gap-metric criterion. The controller design adopts a deep deterministic policy gradient (DDPG) algorithm to train six agents in a medium-fidelity WT environment at different mean wind speeds to ensure the controller's robustness. Initially, imitation learning is used for efficient sample collection to fasten training convergence. Afterwards, the agent learns by interacting with the environment. After the training, the pitch control outputs from multi-trained agents are processed by a fuzzy system to have smooth transitions under different operating conditions. The resulting fuzzy DDPG (F-DDPG) controller is deployed to obtain the optimal pitch control action. The performance of the proposed F-DDPG controller is compared to the gain-scheduled PI (GSPI), Linear-Quadratic-Regulator (LQR), and single-DDPG-agent controllers. The controllers are simulated in high-fidelity onshore and offshore 5-MW WT environments using the OpenFAST/MATLAB simulation tools. The results reveal the superiority of the proposed controller in generalizing its optimal performance in different operating conditions.

Quantized feedback control of distributed radar system with packet dropouts and time-varying delays described by 2D system

In this paper, we present quantized state feedback control for the distributed radar with packet dropouts and time-varying delays. The measurements of state signal are quantized by logarithmic quantizer before being communicated. The measurements missing and time delay often occur due to the communication channel failure. The data missing phenomena was modeled by a Bernoulli distributed stochastic variable taking values of 1 and 0, the delays are time-varying. Firstly, the quantized state feedback control design is transformed into stability analysis for a 2D stochastic system. Secondly, we obtain the stability condition based on linear matrix inequality (LMI) of mean-square asymptotic stability of the 2D stochastic system. Additionally, a quantized state feedback controller is designed based on the linear matrix inequality theorem to meet the environmental requirements of the distributed radar system. Compared to existing methods, the proposed controller designed under this method demonstrates robustness and stability in addressing the three challenges of packet loss, time-varying delays, and quantization errors in the distributed radar. Finally, the effectiveness of the proposed method is validated through the simulation example.

Design of MFAC greenhouse temperature controller based on LESO with disturbance estimation and fuzzy controller with parameter tuning

The proper temperature is essential for the growth and development of greenhouse crops. Therefore, it is necessary to determine an appropriate greenhouse temperature automatic control strategy. In this paper, a model-free adaptive greenhouse temperature control strategy is proposed by combining a linear extended state observer and a fuzzy controller. Specifically, the linear extended state observer is introduced to estimate model approximation error generated by pseudo gradient estimation. The fuzzy controller is used to correct the system parameter ρ online. The simulation results show that when there is no disturbance, compared with the typical model-free adaptive scheme, the MAE and RMSE of the greenhouse temperature control scheme proposed in this paper are reduced by 57.55% and 5.40% respectively. In the presence of custom disturbances, the proposed control scheme still has a faster response speed, stronger robustness, and higher control accuracy. And the proposed method can reduce the frequent action of the actuator, thus effectively reducing the energy cost.

Hybrid Data-Driven Active Disturbance Rejection Sliding Mode Control with Tower Crane Systems Validation

This paper proposes a combination of a data-driven algorithm represented by the second-order continuous-time Active Disturbance Rejection Control (ADRC) and a Sliding Mode Control (SMC) algorithm. The purpose of this hybrid controller referred to as ADRC-SMC is to improve the overall control-loop system performance while guaranteeing its stability. This will be done through clear, simple, and transparent steps of controller design in a novel real formulation focused on practical implementation. The parameters of the novel second-order continuous-time ADRC-SMC algorithm are optimally tuned using a metaheuristic slime mould algorithm. The purpose of obtaining the parameters of the ADRC-SMC algorithms in this model-based manner is to reduce the heuristics and further ensure a fair performance comparison of the ADRC-SMC algorithm with that of the popular ADRC algorithm. The data-driven second-order continuous-time ADRC and ADRC-SMC algorithms are validated experimentally validated on tower crane laboratory equipment.

Design, FEM analysis and development of switched reluctance motor electric drive for TRI-wheeler E-vehicle application

Electric vehicles have seen substantial growth in recent years in the emerging economies of the world. The current market scenario of E-vehicles is dominated by PMSM or BLDC motors due to their higher torque-to-power density, and efficiency with added performance metrics of the motor. However, the higher capital cost of permanent magnets makes the motor designers to consider an alternative to magnetic motors, the so-called Switched Reluctance Motors (SRM). With proper FEM analysis and controller design, if SRMs can able to achieve the performance metrics of a PMSM/BLDC, it will be a breakthrough in the automotive sector with affordable prices. Based on the aforementioned facts, this research work focuses on SRM as a viable alternative to the existing PMSM/BLDC motor. The work concentrates on the design and development of an 8/6 SRM for a Tri-wheeler E-vehicle application. The designed motor is fabricated, tested and controlled by WAVECT, an FPGA-based real-time digital controller which is used to control the commutation of the 8/6 SRM. The yielded response satisfactorily proves that the designed 8/6 SRM can be effectively used for the Tri-wheeler E-vehicle application, thereby paving the way for sustainable energy conservation in the automotive sector.

An LMI-based robust state-feedback controller design for the position control of a knee rehabilitation exoskeleton robot: Comparative analysis

Rehabilitation exoskeleton robots play a crucial role in restoring functional lower limb movements for individuals with locomotor disorders. Numerous research studies have concentrated on adapting the control of these rehabilitation robotic systems. In this study, we investigate an affine state-feedback control law for robust position control of a knee exoskeleton robot, taking into account its nonlinear dynamic model that includes solid and viscous frictions. To ensure robust stabilization, we employ the Lyapunov approach and propose three methods to establish stability conditions using the Schur complement, the Young inequality, the matrix inversion lemma, and the S-procedure lemma. These conditions are formulated as Linear Matrix Inequalities (LMIs). Furthermore, we conduct a comprehensive comparison among these methods to determine the most efficient approach. At the end of this work, we present simulation results to validate the developed LMI conditions and demonstrate the effectiveness of the adopted control law in achieving robust position control of the knee exoskeleton robot.

Controller design of coordinated control problems over finite fields via fully actuated approach

This paper discusses coordinated control problems for systems over finite fields via fully actuated approach. Novel types of consensus and synchronization, which are irrelevant to initial states, are proposed. For different coordinated control problems, suitable controllers are designed, which are able to drive the system to the corresponding trajectory in finite steps. Besides, the conclusions are applied to stabilization, and the convergence time is analyzed. As an application, a camera network example is given to illustrate the results.

CBF‐guided iterative learning control performance optimization for switched systems

AbstractThis article studies the control barrier function (CBF) guided tracking performance optimization for switched systems under iterative learning control (ILC). CBF is an effective method for handling control system state safety. If the safe set is designed as expected performance, and the unsafe set is not expected, then this may be a good choice for optimizing control system performance. We propose a tracking performance optimization method based on CBF to speed up the convergence of the tracking error. A safe set is used to describe the expected tracking performance, by utilizing CBF, safety is further transformed into a set of inequality conditions that constrain ILC controller gains, which ensures that the tracking error is always within the safe set. In addition, an event‐triggering mechanism (ETM) is considered to reduce unnecessary data transmission, and sufficient conditions for exponential stability of the tracking error of switched systems and ILC controllers design are obtained. Finally, a simulation example is given to verify the feasibility of the proposed method.