Showing papers on "Robust control published in 2019"

Disturbance-Observer-Based Control for Air Management of PEM Fuel Cell Systems via Sliding Mode Technique

Abstract: In this paper, a model-based robust control is proposed for the polymer electrolyte membrane fuel cell air-feed system, based on the second-order sliding mode algorithm. The control objective is to maximize the fuel cell net power and avoid the oxygen starvation by regulating the oxygen excess ratio to its desired value during fast load variations. The oxygen excess ratio is estimated via an extended state observer (ESO) from the measurements of the compressor flow rate, the load current, and the supply manifold pressure. A hardware-in-loop test bench, which consists of a commercial twin screw air compressor and a real-time fuel cell emulation system, is used to validate the performance of the proposed ESO-based controller. The experimental results show that the controller is robust and has a good transient performance in the presence of load variations and parametric uncertainties.

Adaptive population extremal optimization-based PID neural network for multivariable nonlinear control systems

Abstract: The connection weights parameters play important roles in adjusting the performance of PID neural network (PIDNN) for complex control systems. However, how to obtain an optimal set of initial values of these connection weight parameters in a multivariable PIDNN called MPIDNN is still an open issue for system designers and engineers. This paper formulates this issue as a typical constrained optimization problem firstly by minimizing the cumulative sum of the product of exponential time and the system errors, and a real-time penalty function for overshoots of the system outputs, and then proposes an adaptive population extremal optimization-based MPIDNN method called PEO-MPIDNN for the optimal control issue of multivariable nonlinear control systems. The simulation results for two typical multivariable nonlinear control systems have demonstrated the superiority of the proposed PEO-MPIDNN to real-coded genetic algorithm (RCGA) and particle swarm optimization (PSO)-based MPIDNN, traditional MPIDNN with back propagation algorithm, and population extremal optimization-based multivariable PID control algorithm in terms of transient-state, steady-state, and robust control performance.

Boundary Adaptive Robust Control of a Flexible Riser System With Input Nonlinearities

Abstract: This paper focuses on adaptive robust vibration control for flexible riser systems affected by input nonlinearities and unknown external disturbances. An auxiliary system is constructed and adjusted to develop a boundary adaptive robust control for restraining the vibrational offset and eliminating the effect of input nonlinearities. Besides, an adaptive law of boundary disturbance upper-bound is constructed together with the vibration control strategy to estimate the magnitude of unknown boundary disturbance. Further, the convergence of states and stability of the system are ensured with the developed control scheme. By choosing the proper control parameters, the control performance is verified with the obtained simulation results.

A Robust Consensus Algorithm for Current Sharing and Voltage Regulation in DC Microgrids

Abstract: In this paper, a novel distributed control algorithm for current sharing and voltage regulation in DC microgrids is proposed. The DC microgrid is composed of several distributed generation units, including buck converters and current loads. The considered model permits an arbitrary network topology and is affected by an unknown load demand and modeling uncertainties. The proposed control strategy exploits a communication network to achieve proportional current sharing using a consensus-like algorithm. Voltage regulation is achieved by constraining the system to a suitable manifold. Two robust control strategies of sliding mode type are developed to reach the desired manifold in a finite time. The proposed control scheme is formally analyzed, proving the achievement of proportional current sharing, while guaranteeing that the weighted average voltage of the microgrid is identical to the weighted average of the voltage references.

Precision Motion Control of Permanent Magnet Linear Synchronous Motors Using Adaptive Fuzzy Fractional-Order Sliding-Mode Control

Abstract: The aim of this study is to develop an adaptive fuzzy fractional-order sliding-mode control (AFFSMC) strategy to control the mover position of a permanent magnet linear synchronous motor (PMLSM) system. First, the mathematical model of the PMLSM is investigated by using the principle of field oriented control. Subsequently, a fractional-order sliding-mode control (FSMC) is designed by means of a new fractional-integral sliding surface. Because it is difficult to determine the hitting control gain for the FSMC in practice, the AFFSMC is further developed in which an uncertainty observer is developed to observe uncertainties while an adaptive fuzzy reaching regulator is designed to concurrently compensate for observation deviations and suppress the chattering phenomenon. The adaptive laws are derived to tune the control parameters online based on the Lyapunov stability theorem. Thus, the uncertainty bound information is not required while the chattering can be attenuated. Finally, experiments demonstrate that the proposed AFFSMC system performs the robust control performance and precise tracking response for the PMLSM drive system against the parameter variations and external disturbances.

Introduction to Feedback Control Theory

Abstract: From the Publisher: There are many feedback control books out there, but none of themcapture the essence of robust control as well as Introduction to Feedback Control Theory. Written by Hitay Ozbay, one of the top researchers in robust control in the world, this book fills the gap between introductory feedback control books and advanced robust control books The book covers basic concepts such as dynamical systems modeling, performance objectives, the Routh-Hurwitz test, root locus, Nyquist criterion, and lead-lag controllers. It introduces more advanced topics including Kharitanov's stability test, basic loopshaping, stability robustness, sensitivity minimization, time delay systems, H-infinity control, and parameterization of all stabilizing controllers for single input single output stable plants. This range of topics gives insight into the key issues involved in designing a controller Introduction to Feedback Control Theory covers the basics of robust control and incorporates new techniques for time delay systems, as well as classical and modern control. Researchers, professionals, and students can use this reference to find basic and advanced information, and up-to-date techniques. It occupies an important place in the field of control theory

Low-Complexity Tracking Control of Strict-Feedback Systems With Unknown Control Directions

Abstract: This paper focuses on the problem of output tracking with prescribed transient and steady-state performance for strict-feedback systems with unknown nonlinear functions and unmatched disturbances. In lieu of Nussbaum gain techniques, parameter estimation algorithms and switching control strategies, a continuous static low-complexity control solution is provided by means of a novel combination of smooth orientation functions and error transformation functions. The proposed method possesses inherent robustness against model uncertainties, disturbances, and virtual control signal derivatives, thus eliminating the needs to introduce extra robust control schemes and compute analytic derivatives. Comparative simulation results further illustrate the above theoretical findings.

Adaptive Critic Designs for Event-Triggered Robust Control of Nonlinear Systems With Unknown Dynamics

Abstract: This paper develops a novel event-triggered robust control strategy for continuous-time nonlinear systems with unknown dynamics. To begin with, the event-triggered robust nonlinear control problem is transformed into an event-triggered nonlinear optimal control problem by introducing an infinite-horizon integral cost for the nominal system. Then, a recurrent neural network (RNN) and adaptive critic designs (ACDs) are employed to solve the derived event-triggered nonlinear optimal control problem. The RNN is applied to reconstruct the system dynamics based on collected system data. After acquiring the knowledge of system dynamics, a unique critic network is proposed to obtain the approximate solution of the event-triggered Hamilton–Jacobi–Bellman equation within the framework of ACDs. The critic network is updated by using simultaneously historical and instantaneous state data. An advantage of the present critic network update law is that it can relax the persistence of excitation condition. Meanwhile, under a newly developed event-triggering condition, the proposed critic network tuning rule not only guarantees the critic network weights to converge to optimums but also ensures nominal system states to be uniformly ultimately bounded. Moreover, by using Lyapunov method, it is proved that the derived optimal event-triggered control (ETC) guarantees uniform ultimate boundedness of all the signals in the original system. Finally, a nonlinear oscillator and an unstable power system are provided to validate the developed robust ETC scheme.

Robust Speed Control of PMSM Using Sliding Mode Control (SMC)—A Review

Abstract: Permanent magnet synchronous motors (PMSMs) are known as highly efficient motors and are slowly replacing induction motors in diverse industries. PMSM systems are nonlinear and consist of time-varying parameters with high-order complex dynamics. High performance applications of PMSMs require their speed controllers to provide a fast response, precise tracking, small overshoot and strong disturbance rejection ability. Sliding mode control (SMC) is well known as a robust control method for systems with parameter variations and external disturbances. This paper investigates the current status of implementation of sliding mode control speed control of PMSMs. Our aim is to highlight various designs of sliding surface and composite controller designs with SMC implementation, which purpose is to improve controller’s robustness and/or to reduce SMC chattering. SMC enhancement using fractional order sliding surface design is elaborated and verified by simulation results presented. Remarkable features as well as disadvantages of previous works are summarized. Ideas on possible future works are also discussed, which emphasize on current gaps in this area of research.

Path Following Control of Autonomous Ground Vehicle Based on Nonsingular Terminal Sliding Mode and Active Disturbance Rejection Control

Abstract: Due to the strong nonlinearity, coupling characteristics, external disturbance, and complex driving conditions, it is difficult to establish an accurate mathematical model for the autonomous ground vehicle (AGV). This requires the AGV path following controller to have strong robustness. In this paper, a robust AGV path following control strategy that is based on nonsingular terminal sliding mode (NTSM) and active disturbance rejection control (ADRC) is presented. First, the complex path following problem is simplified to a simple yaw angle tracking problem by constructing a desired yaw angle function that satisfies that the displacement deviation of AGV converges to zero when the actual yaw angle approaches the desired yaw angle. Second, an NTSM-ADRC controller is designed for the system, which uses the extended state observer to estimate and compensate the unmodeled dynamics and unknown external perturbations of the system in real time. In order to improve response characteristics of the controller, the nonlinear error feedback control law is designed by combining the NTSM and exponential approximation law. In contrast to the existing work, the improved controller can use the simple two-degree-of-freedom linear vehicle dynamic model to provide good performance in a range of driving conditions. Finally, the CarSim–Simulink simulation results of typical conditions show that the proposed control strategy can make the AGV follow the reference path quickly and accurately while ensuring the stability of the vehicle and has strong robustness.

Constrained Sampled-Data ARC for a Class of Cascaded Nonlinear Systems With Applications to Motor-Servo Systems

Abstract: In this paper, sampled-data adaptive robust control is proposed for a class of uncertain cascaded nonlinear system with states and inputs constraints. The systematic design procedure can be divided into two steps: i) design a sampled-data adaptive robust controller for the plant to not only stabilize the closed-loop system but also track the desired command although there are a variety of uncertainties and disturbances in the system; ii) design a reference governor for the control system to avoid the states and inputs violating their limits. Finally, the proposed method is employed in Motor-servo system to demonstrate the effectiveness.

Sampled-Data-Based Event-Triggered Active Disturbance Rejection Control for Disturbed Systems in Networked Environment

Abstract: This paper develops a methodology on sampled-data-based event-triggered active disturbance rejection control (ET-ADRC) for disturbed systems in networked environment when only using measurable outputs. By using disturbance/uncertainty estimation and attenuation technique, an event-based sampled-data composite controller is proposed together with a discrete-time extended state observer. Under the presented new framework, the newest state and disturbance estimates as well as the control signals are not transmitted via the common sensor-controller network, but instead communicated and calculated until a discrete-time event-triggering condition is violated. Compared with the periodic updates in the traditional time-triggered active disturbance rejection control, the proposed ET-ADRC scheme can remarkably reduce the communication frequency while maintaining a satisfactory closed-loop system performance. The proposed discrete-time control scheme provides the engineers with a manner of direct and easier implementation via networked digital computers. It is shown that the bounded stability of the closed-loop system can be guaranteed. Finally, an application design example of a dc–dc buck converter with experimental results is conducted to illustrate the efficiency of the proposed control scheme.

Driver-Automation Cooperation Oriented Approach for Shared Control of Lane Keeping Assist Systems

Abstract: This paper presents a novel shared control concept for lane keeping assist (LKA) systems of intelligent vehicles. The core idea is to combine system perception with robust control so that the proposed strategy can successfully share the control authority between human drivers and the LKA system. This shared control strategy is composed of two parts, namely an operational part and a tactical part. Two local optimal-based controllers with two predefined objectives (i.e., lane keeping and conflict management) are designed in the operational part. The control supervisor in the tactical part aims to provide a decision-making signal which allows for a smooth transition between two local controllers. The control design is based on a human-in-the-loop vehicle system to improve the mutual driver-automation understanding, thus reducing or avoiding the conflict. The closed-loop stability of the whole driver-vehicle system can be rigorously guaranteed using the Lyapunov stability argument. In particular, the control design is formulated as an LMI optimization which can be easily solved with numerical solvers. The effectiveness of the proposed shared control method is clearly demonstrated through various hardware experiments with human drivers.

A New Frequency Control Strategy in an Islanded Microgrid Using Virtual Inertia Control-Based Coefficient Diagram Method

Abstract: Renewable energy sources (RESs) are growing rapidly and highly penetrated in microgrids (MGs). As a result of the replacement of the synchronous generators with a large amount of RESs, the overall system inertia might be dramatically reduced which negatively affected the MG dynamics and performance in face of uncertainties, leading to weakening of the MG stability, which considers being a serious challenge in such grids. Therefore, in order to cope with this challenge and benefit from a maximum capacity of the RESs, robust control strategy must be applied. Hence, in this paper, a new application of robust virtual inertia control-based coefficient diagram method (CDM) controller is proposed in an islanded MG considering high-level RESs penetration for enhancement the system’s validity and robustness in face of disturbances and parametric uncertainties. The proposed controller’s proficiency has been checked and compared with H-infinite controller using MATLAB/Simulink which approved that the CDM controller achieved superior dynamic responses in terms of accurate reference frequency tracking and disturbance reduction over H-infinite in all test scenarios. Thus, the proposed controller alleviates the difficulties of H-infinite controller such as the experience and necessary abilities to design the form of the weighting functions for the system. Consequently, the frequency stability is improved and approved that the proposed CDM-based virtual inertia controller can significantly support a low-inertia islanded microgrid against RESs and load fluctuations.

Learning robust and high-precision quantum controls

Abstract: Robust and high-precision quantum control is extremely important but challenging for the functionalization of scalable quantum computation. In this paper, we show that this hard problem can be translated to a supervised machine learning task by thinking of the time-ordered quantum evolution as a layer-ordered neural network (NN). The seeking of robust quantum controls is then equivalent to training a highly generalizable NN, to which numerous tuning skills matured in machine learning can be transferred. This opens up a door through which a family of robust control algorithms can be developed. We exemplify such potential by introducing the commonly used trick of batch-based optimization, and the resulting batch-based gradient algorithm is numerically shown to be able to remarkably enhance the control robustness while maintaining high fidelity.

Disturbance Observer-based Robust Control and Its Applications: 35th Anniversary Overview

Abstract: Disturbance Observer has been one of the most widely used robust control tools since it was proposed in 1983. This paper introduces the origins of Disturbance Observer and presents a survey of the major results on Disturbance Observer-based robust control in the last thirty-five years. Furthermore, it explains the analysis and synthesis techniques of Disturbance Observer-based robust control for linear and nonlinear systems by using a unified framework. In the last section, this paper presents concluding remarks on Disturbance Observer-based robust control and its engineering applications.

A Guide to Design Disturbance Observer

Abstract: The goal of this paper is to clarify the robustness and performance constraints in the design of control systems based on disturbance observer (DOB). Although the bandwidth constraints of a DOB have long been very well-known by experiences and observations, they have not been formulated and clearly reported yet. In this regard, the Bode and Poisson integral formulas are utilized in the robustness analysis so that the bandwidth constraints of a DOB are derived analytically. In this paper, it is shown that the bandwidth of a DOB has upper and lower bounds to obtain good robustness if the plant has non-minimum phase zero(s) and pole(s), respectively. Besides that the performance of a system can be improved by using a higher-order disturbance observer (HODOB); however, the robustness may deteriorate, and the bandwidth constraints become more severe. New analysis and design methods, which provide good robustness and predefined performance criteria, are proposed for the DOB based robust control systems. The validity of the proposals are verified by simulation results.

A Decentralized H-Infinity Unscented Kalman Filter for Dynamic State Estimation Against Uncertainties

Abstract: The widely used traditional Kalman filter-type power system dynamic state estimator is unable to address the unknown and non-Gaussian system process and measurement noise as well as dynamical model uncertainties. To this end, this paper proposes a decentralized H-infinity unscented Kalman filter that leverages the strength of the H-infinity criteria developed in robust control for handling system uncertainties with the advantage of the UKF for addressing strong model nonlinearities. Specifically, the statistical linerization approach is used to derive a linear-like batch-mode regression model similar to the linear Kalman filter. This allows us to resort to the linear H-infinity Kalman filter framework for the development of the proposed H-infinity UKF in the Krein space. An analytical form is also derived to tune the parameter of the H-infinity criterion. Two decoupled models are presented to enable the decentralized implementation of the H-infinity UKF using the local PMU measurements. Extensive simulation results carried out on the IEEE 39-bus system show that the proposed H-infinity UKF is able to bound the influences of various types of measurement and model uncertainties while obtaining accurate state estimates.

Robust Tube-Based Model Predictive Control for Lateral Path Tracking

Abstract: This paper presents a path following application for vehicles based on a simple linear and time invariant single-track model, which is calculated based on a constant nominal longitudinal speed. In order to consider the differences in vehicle dynamics between the real vehicle and this constant nominal model, a tube-based robust Model Predictive Control (MPC) approach is introduced. The proposed control algorithm is designed in order to guarantee proper path tracking, not only considering lateral error, but also orientation error to the target trajectory. Additionally, strict constraints are considered in the control signal and the lateral path following error. The control approach is designed in order to guarantee certain comfort standards for a wide range of velocities, with guaranteed stability.

An improved approach for robust control of dynamic voltage restorer and power quality enhancement using grasshopper optimization algorithm.

Abstract: This paper presents a novel contribution of a low complexity control scheme for voltage control of a dynamic voltage restorer (DVR). The scheme proposed utilizes an error-driven proportional-integral-derivative (PID) controller to guarantee better power quality performance in terms of voltage enhancement and stabilization of the buses, energy efficient utilization, and harmonic distortion reduction in a distribution network. This method maintains the load voltage close to or equal to the nominal value in terms of various voltage disturbances such as balanced and unbalanced sag/swell, voltage imbalance, notching, different fault conditions as well as power system harmonic distortion. A grasshopper optimization algorithm (GOA) is used to tune the gain values of the PID controller. In order to validate the effectiveness of the proposed DVR controller, first, a fractional order PID controller was presented and compared with the proposed one. Further, a comparative performance evaluation of four optimization techniques, namely Cuckoo search (CSA), GOA, Flower pollination (FBA), and Grey wolf optimizer (GWO), is presented to compare between the PID and FOPID performance in terms of fault conditions in order to achieve a global minimum error and fast dynamic response of the proposed controller. Second, a comparative analysis of simulation results obtained using the proposed controller and those obtained using an active disturbance rejection controller (ADRC) is presented, and it was found that the performance of the optimal PID is better than the performance of the conventional ADRC. Finally, the effectiveness of the presented DVR with the controller proposed has been assessed by time-domain simulations in the MATLAB/Simulink platform.

Research on Active Disturbance Rejection Control With Parameter Autotune Mechanism for Induction Motors Based on Adaptive Particle Swarm Optimization Algorithm With Dynamic Inertia Weight

Abstract: An active disturbance rejection control (ADRC) of induction motor based on an adaptive particle swarm optimization (APSO) algorithm is proposed in this paper, in order to realize the precise decoupling of induction motor and the disturbance compensation. The novel control method employs APSO as the automatic tune mechanism for ADRC controller. According to the feedback information of induction motor, an optimal solution can be achieved via the optimization mechanism and self-learning ability of APSO, so the reliance of ADRC controller on parameters can be reduced. In order to obtain the better optimization solution more efficient, the aggregation degree and the evolution speed are introduced into the APSO to dynamically modify the inertia weight based on the practical optimization process. Experimental results indicate that the robustness of the proposed optimal design method for ADRC is better than the conventional ADRC when the disturbances occur, and the method is feasible and effective.

Robust local and string stability for a decentralized car following control strategy for connected automated vehicles

Abstract: This paper presents a robust car-following control strategy under uncertainty for connected and automated vehicles (CAVs). The proposed control is designed as a decentralized linear feedback and feedforward controller with a focus on robust local and string stability under (i) time-varying uncertain vehicle dynamics and (ii) time-varying uncertain communication delay. The former uncertainty is incorporated into the general longitudinal vehicle dynamics (GLVD) equation that regulates the difference between the desired acceleration (prescribed by the control model) and the actual acceleration by compensating for nonlinear vehicle dynamics (e.g., due to aerodynamic drag force). The latter uncertainty is incorporated into acceleration information received from the vehicle immediately ahead. As a primary contribution, this study derives and proves (i) a sufficient and necessary condition for local stability and (ii) sufficient conditions for robust string stability in the frequency domain using the Laplacian transformation. Simulation experiments verify the correctness of the mathematical proofs and demonstrate that the proposed control is effective for ensuring stability against uncertainties.