Controller Design For Systems Research Articles

Electric Vehicle Steering System Fuzzy Modeling and Controller Design

Electric vehicle (EV) steering control becomes much more important in many senses such as driving comfort and safety. Unlike the traditional internal combustion engine powered vehicles that possess only one control input related to the steering wheel, the so‐called direct yaw moment control is yet another control input in EVs thanks to the dispersedly located in‐wheel motors. In view of controller design crucially depending on a proper mathematical model, this paper first focuses on the modeling of EV steering system with uncertainty in the system considered for the latter robust controller design. Then a Takagi‐Sugeno (T‐S) fuzzy model is obtained based on the EV's model, which is capable of varying vehicle velocity in a range. Finally, a robust controller with inputs constraint is designed based on the model, and the effectiveness is verified by computer simulations. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Intelligent Structure Identification and Robust Control Implementation

This study outlines a comprehensive strategy for designing and implementing robust controllers tailored for intelligent structures. This study presents a robust control-based structural identification technique that uses the input/output data of the system to construct a state-space mode and frequency domain. To reduce vibrations, a robust controller is created using the control Simulink model. The identification and robust control of smart structures using Simulink involve a combination of system identification techniques and control design within the MATLAB Simulink environment. The key challenge is dealing with uncertainties and variations in system dynamics. Robust control methods have been employed to suppress the vibrations during dynamic disturbances. These methods are important for mechanical systems operating under stochastic loading conditions.

A new method for model reduction and controller design of large-scale dynamical systems

A new method for model reduction and controller design of large-scale dynamical systems

Output feedback command filtered fuzzy controller design for uncertain strict-feedback nonlinear systems with unmodeled dynamics and event-triggered strategy

An adaptive control problem is considered for a class of nth-order uncertain nonlinear systems with unmodeled dynamics. Compared with the traditional backstepping method, this article improves the systems performance by introducing command filtered and event-triggered. The former primarily enhances the flexibility of controller design, while the latter employs a variable threshold triggering mechanism to achieve savings in communication resources. Simultaneously, the estimation error is compensated by the introduced hyperbolic tangent function. In addition, fuzzy logic system (FLS) is primarily used for observer design and approximation of unknown functions. The conclusion that the closed-loop signals are bounded is obtained through stability analysis. Finally, the numerical simulation results provide a comparative validation of the effectiveness of the proposed control scheme.

ClosedLoop Control of Pulse-Density-Modulated Wireless Power Transfer with Fast MEPT

Efficient wireless power transfer (WPT) requires closedloop control to perform maximum efficiency point tracking (MEPT) against the dynamic changes of operating conditions. In this paper, we took the latest deltasigma pulse-density-modulated (PDM) WPT system as the plant and presented a systematic control design procedure. The WPT system was modeled by the reducedorder dynamic phasor method and further decoupled under the tuned condition. It was identified that the coupled resonators behave like a secondorder system, and its natural frequency is much lower than the resonant frequency. This was considered in the closedloop design to derive a suitable loop gain and ensure stability. The nonlinearity caused by the dualside PDM control is limited to a small inner loop so that the entire loop is linear and simple. Experimental results showed that the closedloop voltage regulation and MEPT took only about 10 ms after load stepping. The system DC output voltage was always tightly regulated, and the steadystate efficiency was very close to the theoretical maximum value under different coupling conditions.

Model-free compensation learning control of asymmetric hysteretic systems with initial state learning

In this article, a model-free compensation learning control scheme is presented for asymmetric hysteretic systems to achieve high-precision output tracking, where the effects of asymmetric input hysteresis nonlinearities are denoted by the asymmetric Prandtl–Ishlinskii model (APIM). In the presented control scheme, the feedforward iterative learning technique is generalized to the control design of asymmetric hysteretic systems. To improve the robustness and dynamic performance of the existing feedforward learning control for hysteretic systems, both the feedback action and the differential term are utilized to design a discrete-time PD-P open-closed-loop learning control law for simultaneously compensating the asymmetric input hysteresis nonlinearities and the linear dynamics effects without constructing compensators based on their models. The initial state error of such a system is also considered, a modified initial state learning strategy is proposed to ensure the initial state error tends to a prescribed level with the increasing of iterations. By fully analyzing the properties of the APIM, the convergence conditions with respect to the input error, the state error, and the tracking error along the iteration domain are given. The simulation and experimental results are provided to demonstrate strong robustness and excellent tracking accuracy with the proposed model-free compensation learning control scheme.

Control and mathematical design of PV-based renewable energy systems for LCL-SR filter-based power quality improvement

Power quality challenges arise when photovoltaic (PV) systems are integrated into power networks. This research work examines the control and mathematical design of an LCL-SR (series resistance) filter for power quality improvement in PV-based sustainable energy systems to meet this difficulty. Power quality may be improved via an LCL filter in grid-connected systems. A non-linear load-connected PV system incorporates a variety of power electronics controllers, including an MPPT controller, battery controller, and inverter current controller, which introduce harmonics into the grid. A passive filter, LCL-SR, is suggested for PV applications that interact with voltage source inverters and the utility grid or electric load to reduce harmonics. A detailed mathematical modeling is presented to design an LCL-SR filter, and its performance is evaluated under different load conditions in terms of THD (Total Harmonics Distortion) and efficiency. LCL-SR filter has 4.32% THD but in the case of LCL filter with parallel R is less THD, which is 3.61%. In terms of efficiency and THD LCL-SR is best among all compared filters which have THD is 4.32% along with an efficiency is 75.95%. The results are compared without a filter, and different configurations of LCL-SR are simulated to show the effectiveness of the proposed filter for PV & residential applications.

Simulink Model for LMV and Conceptualization of 2 Speed E-Axle

Heavy-duty trucks are the primary contributor to the global emissions of greenhouse gases. Because of this, electric haulage has seen significant growth in popularity. Electric heavy-duty trucks aren't widely adopted because of their limited range, payload capacity, and high prices. Electric axle systems, sometimes known as "E-Axles," are capable of electrifying heavy-duty vehicles effectively. The motor, power electronics, and gearbox are all combined into one unit in an E-axle. E-axles that are devoid of motors and gearboxes have the potential to boost performance while also reducing weight and fuel consumption. Heavy-duty automobiles are similar to them. E-axles have the potential to increase the fuel efficiency, flexibility, and emissions of heavy-load trucks. The e-axles are being inspected. E-axles for heavy-duty trucks bring both issues and opportunities concerning finances, infrastructure, and costs. Examine the performance standards as well as the technology behind batteries. This paper provides an introduction to MATLAB Simulink, which focuses on engineering and science. Simulink facilitates the design, simulation, and evaluation of systems. This article demonstrates the fundamental principles, features, and benefits of Simulink through the application of system modeling, control design, and dynamic simulation. It presents Simulink methodologies, libraries, and blocks, as well as interactive simulations.
 Keywords: MATLAB, Simulink, EV Control systems, EV simulation, EV powertrain modeling, EV test and validation Simulink, WLTP drive cycle

Current Controller Design of Grid-Connected Inverter with Incomplete Observation Considering L-/LC-Type Grid Impedance

This paper presents a current control design for stabilizing an inductive-capacitive-inductive (LCL)-filtered grid-connected inverter (GCI) system under uncertain grid impedance and distorted grid environment. To deal with the negative impact of grid impedance, LC-type grid impedance is considered in both the system model derivation and controller design process of an LCL-filtered GCI system. In addition, the integral and resonant control terms are also augmented into the system model in the synchronous reference frame to guarantee the reference tracking of zero steady-state error and good harmonic disturbance compensation of the grid-injected currents from GCI. By considering the effect of grid impedance on the control design process, an incomplete state feedback controller will be designed based on the linear-quadratic regulator (LQR) without damaging the asymptotic stabilization and robustness of the GCI system under uncertain grid impedance. By means of the closed-loop pole map evaluation, the asymptotic stability, robustness, and resonance-damping capability of the proposed current control scheme are confirmed even when all the system states are not available. In order to reduce the number of required sensors for the realization of the controller, a discrete-time current-type full-state observer is employed in this paper to estimate the system state variables with high precision. The feasibility and effectiveness of the proposed control scheme are demonstrated by the PSIM simulations and experiments by using a three-phase GCI prototype system under adverse grid conditions. The comprehensive evaluation results show that the designed control scheme maintains the stability and robustness of the LCL-filtered GCI when connecting to unexpected grids, such as harmonic distortion and L-type and LC-type grid impedances. As a result, the proposed control scheme successfully stabilizes the entire GCI system with high-quality grid-injected currents even when the GCI faces severe grid distortions and an extra grid dynamic caused by the L-type or LC-type grid impedance. Furthermore, low-order distortion harmonics come from the background grid voltages and are maintained as acceptable limits according to the IEEE Std. 1547-2003. Comparative test result with the conventional one also confirms the effectiveness of the proposed control scheme under LC-type grid impedance thanks to the consideration of LC grid impedance in the design process.

Zero/low overshoot conditions based on maximally‐flatness for PID‐type controller design for uncertain systems with time‐delay or zeros

AbstractThis paper extends the characteristic ratio approach using novel inequalities to ensure zero/low overshoot for linear‐time‐invariant systems with zeros. The extension provided by this paper is based on the maximally‐flatness property of a transfer function, where the square‐magnitude of the transfer function is ensured to be a low‐pass filter. In order to be able to design low‐order/fixed structure controllers, a partial pole‐assignment approach is used instead of the full pole‐assignment used in the Characteristic Ratio Assignment (CRA) method. The developed inequalities and additional stability conditions are combined into an optimization problem using time domain restrictions when necessary. Although the method given in the paper is general, particular inequalities are developed for PI and PI‐PD controller cases, due to their frequent use in industrial applications. Similarly, First‐Order‐Plus‐Delay‐Time (FOPDT) and Second‐Order‐Plus‐Delay‐Time (SOPDT) systems are considered specifically, since most of the practical systems can be approximated by one of these types. The study is extended to plants with uncertainties where a theorem is developed to decrease computation time dramatically. The benefits of the proposed methods are demonstrated by several examples.

Opposition-based manta ray foraging algorithm for global optimization and its application to optimize nonlinear type-2 fuzzy logic control

Interval Type-2 Fuzzy Logic Control (IT2FLC) possesses a high control ability in a way that it can optimally handle the presence of uncertainty in a system dynamic. However, the design of such a control scheme is a challenging task due to its complex structure and nonlinear behavior. A Manta Ray Foraging Optimization (MRFO) is a promising algorithm that can be applied to optimize the control design. However, MRFO still suffers the local optima problem due to unbalance exploration-exploitation of the MRFO agents and hence limiting the performance of the desired control. In this paper, Standard, Quasi, Super, and Quasi-Reflected opposition strategies are integrated into the MRFO structure. Each strategy enhances the exploration-exploitation capability and offers different approaches of varying agent’s step size relative to the algorithm’s iteration. The proposed opposition-based MRFO (OMRFO) algorithms are applied to optimize the IT2FLC control design for a laboratory-scaled inverted pendulum system. Moreover, as the algorithms are also promising strategies to other problems, they are applied to solve 50D of 30 IEEE CEC14 benchmark functions representing problems with different features. Performance analysis of the algorithms is statistically conducted using Wilcoxon sign rank and Friedman tests. The result shows that the performance of MRFO and Quasi-Reflected-OMRFO are equal, while all other OMRFO variants show a significant improvement and better rank over the MRFO. The Super and Quasi OMRFO-IT2FLC schemes acquired the best responses for the cart and pendulum, respectively.

A new approach for dynamic output feedback control design of time-delayed nonlinear systems

This paper introduces a full-order dynamic output-feedback (DOF) controller for discrete-time nonlinear systems with time-varying delays represented by Linear Parameter-Varying (LPV) systems. The controller design is carried out by developing delay-dependent Linear Matrix Inequality based conditions using Lyapunov–Krasovskii stability arguments. A notable characteristic of the proposed approach is that the dynamic controller gains are directly obtained, without the need for variable transformations, equality constraints, or iterative algorithms. This feature sets it apart from many existing approaches in the literature and simplifies the design process. Furthermore, the proposed approach guarantees the local asymptotic stability of the origin of the closed-loop system and provides an estimated admissible initial condition region. The correct operation of the system is ensured once the state trajectories initiated within the admissible initial condition region remain enclosed in the validity domain of the LPV model. Numerical examples are provided to illustrate the potential and effectiveness of the proposed conditions.

Dynamic Modelling and Feed-forward Control of an Overhead Crane

The article presents: modelling, simulation, and controller design of a laboratory overhead crane system. A nonlinear model was developed using Lagrange equation and linearization process is performed for the purpose of analysis and controller design. Accuracy of the linear model is assessed by comparing simulation results of nonlinear and linear dynamic equations. A feed-forward control based on a low-pass filter was designed based on the crane dynamics. The controller performance is discussed in terms of trolley position response and payload sway reduction. The proposed control approach is able to suppress the payload sway, but at the expense of a slight delay in the trolley motion

On the assessment of meta-heuristic algorithms for automatic voltage regulator system controller design: a standardization process

On the assessment of meta-heuristic algorithms for automatic voltage regulator system controller design: a standardization process

Networked explicit hybrid predictive control of an industrial benchmark process

Hybrid systems have acquired deep attention in research as many systems in industries and laboratories manifest hybrid dynamic behaviors. Optimal control design for hybrid systems exhibits challenges for controlling the interacting continuous and discrete dynamics. A double-tank hybrid system (DTHS) is proposed in this paper with a mixed-integer actuation mechanism. The proposed DTHS aims to control the liquid level of the two tanks simultaneously by a networked hybrid predictive controller. Hybrid frameworks are provided to investigate the system through reachability analysis. An explicit hybrid model predictive control (EHMPC) has been developed to control the levels of the DTHS. Hardware-In-the-Loop (HIL) simulation is performed for assessing the performance of the EHMPC with a hardware real-time networked controller. The networked real-time controller is constructed and implemented using the Node.js frameworks. The simulation and realtime results are presented and discussed to evaluate the hybrid controller performance.

Event-triggered $$H_{\infty }$$ controller design for uncertain fractional-order systems with time-varying delays

Event-triggered $$H_{\infty }$$ controller design for uncertain fractional-order systems with time-varying delays

Robust-optimal control design for current-controlled electromagnetic levitation system with unmatched input uncertainty

Robust-optimal control design for current-controlled electromagnetic levitation system with unmatched input uncertainty

Adaptive region-reaching control for a non-holonomic mobile robot via system decomposition approach

This article focuses on the region-reaching control for a non-holonomic mobile robot system with two actuated wheels. The region-reaching control task consists of three basic control objectives: reaching an objective region, maintaining a rest state in the objective region, and stabilising at a desired posture. The constraints imposed on the motion of the mobile robot system are not integrable and have no inverse resolution, and thus increase the complexity of the controller design for non-holonomic systems. A system decomposition approach is proposed to convert the non-holonomic constraint control system to the cascade holonomic constraint control subsystems, and an adaptive control gain technique is presented to guarantee the stability of the non-holonomic system. Then, a novel torque controller is presented to implement the region-reaching control task. Specially, the objective region can be considered as a parking spot for a mobile robot.

The Influence of Current Magnitudes and Profiles on the Sedimentation of Magnetorheological Fluids: An Experimental Work

Magnetorheological fluids (MRFs) are widely used for various kinds of controllable devices since their properties can be controlled by an external magnetic field. Despite many benefits of MRFs, such as fast response time, the sedimentation arisen due to the density mismatch of the compositions between iron particles and carrier oil is still one of bottlenecks to be resolved. Many studies on the sedimentation problem of MR fluids have been carried out considering appropriate additives, nanoparticles, and several carrier oils with different densities. However, a study on the effect of current magnitudes and profiles on the sedimentation is considerably rare. Therefore, this study experimentally investigates sedimentation behaviors due to different current magnitudes and different magnitude profiles such as square and sine waves in different diameters. The evaluation was performed by visual observation to obtain the sedimentation rate. It was found that the average sedimentation rate of the square type of current is slower compared to the sinusoidal type. It has also been identified that the higher intensity of the applied current results in a stronger electromagnetic field, which could slow down the sedimentation. The results achieved in this work can be effectively used to reduce particle sedimentation in the controller design of various application systems utilizing MRFs in which the controller generates a different magnitude and different profile of the external magnetic field.

Adaptive robust control for fuzzy underactuated mechanical systems: A Stackelberg game-theoretic optimization approach

This article proposes a Stackelberg game-theoretic optimization approach for adaptive robust controller design of fuzzy underactuated mechanical systems (UMSs). As a commonly encountered challenge, several factors render it difficult to further improve the control performance of these UMSs, such as the coupling effects and nonlinearities resulting from the underactuated structure. Meanwhile, various sources of unexpected time-varying uncertainties can further add to the difficulty in the controller design procedure pipeline. To overcome the above-mentioned issues and further enhance the system performance, a dual-parameter optimization framework is presented here for adaptive robust controller design, leveraging fuzzy set theory and Stackelberg game. In this development, the time-varying uncertainty is characterized via suitable membership functions; and thus the appropriate fuzzy UMS is established. Then, an adaptive robust controller with a leakage-type adaptation law is presented to tackle the uncertainty appropriately. To further improve the system performance considering the trade-off among the transient performance; the steady-state performance; and the control cost; a Stackelberg game-based dual-parameter optimization method is proposed to determine the Stackelberg strategy. It is rigorously proved in the developments here that the proposed controller design and parameters optimization method guarantees the desired deterministic system performance, i.e., uniform boundedness (UB) and uniform ultimate boundedness (UUB). Moreover, a series of numerical simulations based on a flywheel inverted pendulum (FIP) are performed, which illustrate the effectiveness and superiority of the proposed method.