Controller Design For Systems Research Articles

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.

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.

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.

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.

Adaptive Prescribed-Time SOSM Controller Design for Nonlinear Systems With Prescribed Performance

Adaptive Prescribed-Time SOSM Controller Design for Nonlinear Systems With Prescribed Performance

Anti-lock braking system control design using a non-linear multi-body dynamic model for a two wheeled vehicle

This paper investigates the design of an Antilock Braking System for a Two Wheeled Vehicle using a non-linear multi-body dynamic model developed by an approach widely used in robotics. The Antilock Braking System is controlled using Matlab/Simulink by applying the Bang-Bang, Proportional-Integral-Derivative and Fuzzy Logic controllers in order to set the longitudinal slip to a desired value. The developed model is used to study the braking performances of a Two Wheeled Vehicle in a straight-line maneuver under different road conditions: dry, wet and a transition between both. The simulation results indicate that the non-linear dynamic model can effectively simulate the behavior of the Two Wheeled Vehicle under emergency braking conditions and with the different types of controllers. The braking performance of the Two Wheeled Vehicle with Antilock Braking System was improved by decreasing the stopping distance, avoiding wheel lockup and therefore contributes to ensure the vehicle control and stability and to reduce the risk of falling. Also, it can be seen through comparative analysis that the Fuzzy logic is one of the most efficient controllers that improve the braking safety.

LQR controller design for affine LPV systems using reinforcement learning

In this paper, a data-driven sub-optimal state feedback is designed for a continuous time linear parameter varying (LPV) system using reinforcement learning. Time-varying parameters lie in a poly top and the system matrix has an affine representation for the parameters. Two novels, on-policy and off-policy algorithms, are proposed using available data from vertex systems of polytop to minimise a performance index and admit a common Lyapunov function (CLF). A convex optimisation problem is derived for each iteration based on Lyapunov inequality. Algorithms yield stabilising feedback gain in each iteration and convergence to a common lyapunov function. We demonstrate the efficacy of the proposed method by simulation of two case studies.

Nonlinear feedforward controller design of gasoline engine air-path system for reducing engine torque overshoot

In gasoline engines, the amount of fresh air in the cylinder becomes temporarily excessive owing to the slow response in exhaust gas recirculation (EGR) gas under a high EGR ratio. This phenomenon should be avoided, as it causes an engine torque overshoot because the amount of fuel also increases to follow the stoichiometric ratio, and can cause discomfort to the driver. In this study, we investigate reducing torque overshoot using a feedforward controller, which is a simple and effective method for improving the response of a control system. The feedforward controller is typically designed based on the inverse of the plant model. However, designing a feedforward controller for a high-order and nonlinear plant, such as an engine air-path system, can be challenging. Therefore, we exploit the fact that if a given nonlinear system satisfies the flatness property, a feedforward controller based on the inverse model can be easily obtained. The effectiveness of the proposed feedforward controller is evaluated through simulations.

Command filter-based adaptive neural tracking control of nonlinear systems with multiple actuator constraints and disturbances

In this paper, the adaptive practical finite-time tracking control problem for a class of strictly feedback nonlinear systems with multiple actuator constraints is investigated using backstepping techniques and practical finite-time stability theory. The effects of deadband and saturated nonlinear constraints on the controller design of nonlinear systems are addressed by the equivalent transformation method. The problem of complexity explosion due to the derivatives of virtual control signals is solved by using the virtual control signals as inputs to the command filters and using the outputs of the command filters to perform the corresponding control tasks. An adaptive neural network tracking backstepping control strategy based on the command filter technique and the backstepping design algorithm is proposed by approximating an unknown nonlinear function using a neural network. The control strategy ensures the boundedness of all variables in the closed-loop system, and the output tracking error fluctuates in a small region near the origin. Finally, simulations verify the effectiveness of the control strategy designed in this paper.

A linear matrix inequality approach to optimal reset control design for a class of nonlinear systems

AbstractIn this article, the problem of the optimal reset control design for Lipschitz nonlinear systems is addressed. The reset controller includes a base linear controller and a reset law that enforces resets to the controller states. The reset law design is strongly dependent on the appropriate design of the base controller. For this reason, in this article, the base controller and reset law are simultaneously designed. More precisely, an optimal dynamic output feedback is considered as the base controller which minimizes the upper bound of a quadratic performance index, and a reset law is used to improve the transient response of the closed‐loop system. This design is done in a full offline procedure. The problem is transformed into a set of linear matrix inequalities (LMIs), and the reset controller is obtained by solving an offline LMI optimization problem. Finally, two examples are presented to illustrate the effectiveness and validity of the proposed method.