Showing papers on "Robust control published in 2022"

Approximation-Free Robust Synchronization Control for Dual-Linear-Motors-Driven Systems With Uncertainties and Disturbances

Abstract: This article addresses the synchronization control problem for dual-linear-motors-driven systems with model uncertainties and disturbances. An approximation-free robust synchronization control scheme based on cross-coupled control (CCC) frame is proposed to achieve high-precision tracking and synchronization performance, along with excellent uncertainties and disturbances rejection ability. More specifically, the CCC frame is designed to handle the asynchronous motion of two parallel motors. The main advantage is that the proposed method does not require the explicit system model, and any approximations utilized to handle the model uncertainties, such as estimation, identification, and online learning, are not required. Therefore, the computational burden and complexity of the controller are significantly reduced. Considering the importance of the transient and steady-state response, the concept and technology of prescribed performance are adopted to guarantee the control effect and state constraints. In addition, none of the high-order derivatives of desired trajectory, difficult to obtain directly in many applications, are used in the proposed controller. Furthermore, the stability and convergence performance of the closed-loop system are rigorously demonstrated. Finally, comparative experiments show the effectiveness of this study via a dual-driven H-type gantry.

Observer-Based Sliding-Mode Control for Grid-Connected Power Converters Under Unbalanced Grid Conditions

Abstract: This article proposes a novel robust control strategy for three-phase power converters operated under unbalanced grid conditions. A consolidated control objective is obtained in the stationary $\alpha \beta$ frame, which can be flexibly adjusted according to the degree of oscillation in the active and reactive powers and the balance of the three-phase current. Based on the dynamics of the converter and control objective, a control scheme in a cascaded framework is presented, in which an adaptive observer is applied to estimate the positive and negative sequences of the grid voltage. In the current tracking loop, a super-twisting algorithm current controller coupled with a super-twisting differentiator is implemented to track the current references, featuring rapid dynamics, and improved robustness. Additionally, in the voltage regulation loop, an effective composite controller is developed to regulate the dc-link voltage, where a super-twisting observer is used to estimate the load disturbance, thereby improving the performance of the converter. The experimental results are provided to confirm the effectiveness and superiority of the proposed control strategy.

A Robust Controller for Battery Energy Storage System of an Islanded AC Microgrid

Abstract: A battery energy storage system (BESS) can play a critical role in regulating system frequency and voltage in an islanded microgrid. A $\mu$ -synthesis-based robust control has been proposed for dc link voltage regulation of BESS for achieving frequency regulation and voltage quality enhancement of islanded microgrid. Variation in the operating condition of ac microgrid affects the operating condition of the BESS’ converter. This controller synthesis accounts for such uncertain variations as parametric uncertainty. The stability and performance of the proposed controller can be guaranteed for bounded parametric variations. The bounds on parameters are selected based on practical limitations of BESS. In this article, the proposed controller's performance is tested on an islanded CIGRE TF C6:04:02 benchmark low voltage ac microgrid system. The importance of dc link voltage regulation is analyzed based on performance comparison with a benchmark controller. The controller performance is also validated using a real-time Typhoon HIL emulator.

Robust Safety-Critical Control for Dynamic Robotics

Abstract: We present a novel method of optimal robust control through quadratic programs that offers tracking stability while subject to input and state-based constraints as well as safety-critical constraints for nonlinear dynamical robotic systems in the presence of model uncertainty. The proposed method formulates robust control Lyapunov and barrier functions to provide guarantees of stability and safety in the presence of model uncertainty. We evaluate our proposed control design on dynamic walking of a five-link planar bipedal robot subject to contact force constraints as well as safety-critical precise foot placements on stepping stones, all while subject to model uncertainty. We conduct preliminary experimental validation of the proposed controller on a rectilinear spring-cart system under different types of model uncertainty and perturbations.

A Novel Robust Super-Twisting Nonsingular Terminal Sliding Mode Controller for Permanent Magnet Linear Synchronous Motors

Abstract: For accurate position tracking and robust control of permanent magnet linear synchronous motor (PMLSM) servo systems against various uncertainties, such as parameter change, load disturbance, friction force, and so on, this article proposes a novel super-twisting nonsingular terminal sliding mode control method based on a high-order sliding mode observer. First, a dynamic model of PMLSM with uncertainties is established. A novel nonsingular terminal sliding mode function combined with the super-twisting algorithm is then proposed to design a robust super-twisting nonsingular terminal sliding mode controller, which ensures the system convergence to the equilibrium state within a finite time, effectively eliminating the chattering phenomenon and improving the system robustness. To further improve the system tracking performance and anti-interference ability, a high-order sliding mode observer is used to estimate the system uncertainties. The effective tracking performance and robustness of the proposed control method are validated by the experimental results.

Critical Review on Robust Speed Control Techniques for Permanent Magnet Synchronous Motor (PMSM) Speed Regulation

Abstract: The permanent magnet synchronous motor (PMSM) is a highly efficient energy saving machine. Due to its simple structural characteristics, good heat radiation capability, and high efficiency, PMSMs are gradually replacing AC induction motors in many industrial applications. The PMSM has a nonlinear system and lies on parameters that differ over time with complex high-class dynamics. To achieve the excessive performance operation of a PMSM, it essentially needs a speed controller for providing accurate speed tracking, slight overshoot, and robust disturbance repulsion. Therefore, this article provides an overview of different robust control techniques for PMSMs and reviews the implementation of a speed controller. In view of the uncertainty factors, such as parameter perturbation and load disturbance, the H∞ robust control strategy is mainly reviewed based on the traditional control techniques, i.e., robust H∞ sliding mode controller (SMC), and H∞ robust current controller based on Hamilton–Jacobi Inequality (HJI) theory. Based on comparative analysis, this review simplifies the development trend of different control technologies used for a PMSM speed regulation system.

Data-Driven Distributionally Robust MPC for Constrained Stochastic Systems

Abstract: In this letter we introduce a novel approach to distributionally robust optimal control that supports online learning of the ambiguity set, while guaranteeing recursive feasibility. We introduce conic representable risk, which is useful to derive tractable reformulations of distributionally robust optimization problems. Specifically, to illustrate the techniques introduced, we utilize risk measures constructed based on data-driven ambiguity sets, constraining the second moment of the random disturbance. In the optimal control setting, such moment-based risk measures lead to tractable optimal controllers when combined with affine disturbance feedback. Assumptions on the constraints are given that guarantee recursive feasibility. The resulting control scheme acts as a robust controller when little data is available and converges to the certainty equivalent controller when a large sample count implies high confidence in the estimated second moment. This is illustrated in a numerical experiment.

Robust Fixed-Time Stability: Application to Sliding-Mode Control

Abstract: This article deals with robust fixed-time stability and stabilization. First, new global robust fixed-time stability results are proposed for scalar systems by using constant and variable exponent coefficients. Then, they are applied to global robust fixed-time stabilization of a class of uncertain nonlinear second-order systems by using sliding-mode control. All the results are illustrated in simulation.

Robust Nonlinear Control for a Piezoelectric Actuator in a Robotic Hand Using Only Position Measurements

Abstract: A robust nonlinear control based only on position measurements to compensate for the strong hysteresis nonlinearity in a piezoelectrically actuated robotic hand is proposed. Based on a high-gain observer to estimate the hysteresis response and nonlinear control law, locally exponential stable results are obtained. The observer and controller are arranged to conform to an output-feedback scheme, which is simple to implement and does not require additional computation as soon as the model parameters are identified and known. The proposed control technique is valuable for hysteresis that is modeled with the classical Bouc-Wen model. The results provide a closed-loop system that is robust under external disturbances in all the system states. Simulations show the effectiveness of the approach.

Adaptive Interleaved Reinforcement Learning: Robust Stability of Affine Nonlinear Systems With Unknown Uncertainty

Abstract: This article investigates adaptive robust controller design for discrete-time (DT) affine nonlinear systems using an adaptive dynamic programming. A novel adaptive interleaved reinforcement learning algorithm is developed for finding a robust controller of DT affine nonlinear systems subject to matched or unmatched uncertainties. To this end, the robust control problem is converted into the optimal control problem for nominal systems by selecting an appropriate utility function. The performance evaluation and control policy update combined with neural networks approximation are alternately implemented at each time step for solving a simplified Hamilton-Jacobi-Bellman (HJB) equation such that the uniformly ultimately bounded (UUB) stability of DT affine nonlinear systems can be guaranteed, allowing for all realization of unknown bounded uncertainties. The rigorously theoretical proofs of convergence of the proposed interleaved RL algorithm and UUB stability of uncertain systems are provided. Simulation results are given to verify the effectiveness of the proposed method.

Robust Nonlinear Control for a Piezoelectric Actuator in a Robotic Hand Using Only Position Measurements

Abstract: A robust nonlinear control based only on position measurements to compensate for the strong hysteresis nonlinearity in a piezoelectrically actuated robotic hand is proposed. Based on a high-gain observer to estimate the hysteresis response and nonlinear control law, locally exponential stable results are obtained. The observer and controller are arranged to conform to an output-feedback scheme, which is simple to implement and does not require additional computation as soon as the model parameters are identified and known. The proposed control technique is valuable for hysteresis that is modeled with the classical Bouc-Wen model. The results provide a closed-loop system that is robust under external disturbances in all the system states. Simulations show the effectiveness of the approach.

Robust Fuzzy-Fractional-Order Nonsingular Terminal Sliding-Mode Control of LCL-Type Grid-Connected Converters

Abstract: Sliding-mode control (SMC) has been widely used in grid-connected converter system (GCC) systems because of its robustness to parameter variations and external disturbances. However, chattering in SMC may deteriorate the tracking accuracy and can easily excite high-frequency unmodeled dynamics. To solve this problem, this article presents a fuzzy-fractional-order nonsingular terminal sliding-mode controller (Fuzzy-FONTSMC) for the grid current control of LCL–GCCs. First, the system modeling, design of the integer-order NTSMC controller, and state estimation based on the Kalman filter to minimize the sampling sensors are described. Second, the Fuzzy-FONTSMC controller is introduced for optimal fraction-order selection and chattering mitigation, this controller exhibits fast convergence with high tracking accuracy and strong robustness. Finally, the Lyapunov theorem is used to analyze the system stability. Experimental comparisons on a 10-kVA laboratory prototype validate the superior performance and effectiveness of the proposed method under many scenarios.

On Robust Stability and Performance With a Fixed-Order Controller Design for Uncertain Systems

Abstract: Typically, it is desirable to design a control system that is not only robustly stable in the presence of parametric uncertainties but also guarantees an adequate level of system performance. However, most of the existing methods need to take all extreme models over an uncertain domain into consideration, which then results in costly computation. Also, since these approaches attempt rather unrealistically to guarantee the system performance over a full frequency range, a conservative design is always admitted. Here, taking a specific viewpoint of robust stability and performance under a stated restricted frequency range (which is applicable in rather many real-world situations), this article provides an essential basis for the design of a fixed-order controller for a system with bounded parametric uncertainties, which avoids the tedious but necessary evaluations of the specifications on all the extreme models in an explicit manner. A Hurwitz polynomial is used in the design and the robust stability is characterized by the notion of positive realness, such that the required robust stability condition is then successfully constructed. Also, the robust performance criteria in terms of sensitivity shaping under different frequency ranges are constructed based on an approach of bounded realness analysis. Furthermore, the conditions for robust stability and performance are expressed in the framework of linear matrix inequality (LMI) constraints, and thus can be efficiently solved. Comparative simulations are provided to demonstrate the effectiveness and efficiency of the proposed approach.

Robust Formation Coordination of Robot Swarms With Nonlinear Dynamics and Unknown Disturbances: Design and Experiments

Abstract: Coordination of robot swarms has received significant research interest over the last decade due to its wide real-world applications including precision agriculture, target surveillance, planetary exploration, etc. Many of these practical activities can be formulated as a formation tracking problem. This brief aims to design a robust control strategy for networked robot swarms subjected to nonlinear dynamics and unknown disturbances. Firstly, a robust adaptive formation coordination protocol is proposed for robot swarms, which utilizes only local information for tracking a dynamic target with uncertain maneuvers. A rigorous theoretical proof utilizing the Lyapunov stability approach is then provided to guarantee the control performance. Towards the end, real-time hardware experiments with wheeled mobile robots are conducted to validate the robustness and feasibility of the proposed formation coordination approach.

Self-Learning Robust Control Synthesis and Trajectory Tracking of Uncertain Dynamics

Abstract: In this article, we investigate the self-learning robust control synthesis and tracking design of general uncertain dynamical systems. Based on the adaptive critic learning, the robust stabilization method is developed with the help of conducting problem transformation. In addition, by considering the optimal control solution with a discounted cost function, the established method is extended to address the robust trajectory tracking design problem. The Lyapunov stability analysis is also conducted for proving the robustness of the related control plants. Finally, the simulation verification with the three case studies is provided in terms of robust stabilization and trajectory tracking, respectively.

Adaptive Robust Control for a Spatial Flexible Timoshenko Manipulator Subject to Input Dead-Zone

Abstract: This article investigates the adaptive robust spatial vibration control for a flexible Timoshenko manipulator subject to input dead-zone nonlinearity characteristic. The “disturbance-like” terms and dead-zone nonlinearity are first incorporated into the context of control design, and the new boundary robust adaptive control laws are constructed to reduce the shear deformation and elastic oscillation, ensure the expected angle orientation, handle the input dead-zone, and estimate the upper bound of compound disturbances. The convergence of states and the stability of the system are analyzed and proven without simplifying the infinite dimensional dynamics. In the end, the effectiveness of the presented scheme is demonstrated by the result of simulation research.

Data-Driven Control for Linear Discrete-Time Delay Systems

Abstract: The increasing ease of obtaining and processing data together with the growth in system complexity has sparked the interest in moving from conventional model-based control design towards data-driven concepts. Since in many engineering applications time delays naturally arise and are often a source of instability, we contribute to the data-driven control field by introducing data-based formulas for state feedback control design in linear discrete-time time-delay systems with uncertain delays. With the proposed approach, the problems of system stabilization as well as of guaranteed cost and $H_{\infty}$ control design are treated in a unified manner. Extensions to determine the system delays and to ensure robustness in the event of noisy data are also provided.

Robust Formation Control and Obstacle Avoidance for Heterogeneous Underactuated Surface Vessel Networks

Abstract: This article investigates distributed robust formation control and distributed obstacle avoidance problems for networks of heterogeneous underactuated surface vessels without global position measurements. We exploit the cascaded structure of the kinematics and dynamics of generic vessel models to develop structured reduced-order error dynamics for group cooperation. By incorporating graph theory, the supertwisting control technique, and persistence of excitation concept, a distributed robust formation control scheme is developed without requiring global position measurements, where agents in the network may possess completely different dynamic models. It is shown that the stabilization of the reduced-order error dynamics guarantees the stability of the entire vessel error system subject to modeling uncertainties and bounded disturbances. Distributed obstacle avoidance is achieved by surrounding obstacles with stable elliptical limit cycles. During the obstacle avoidance stage, a part of the formation deforms to allow vessels to follow transient trajectories around static and dynamic obstacles. Simulation results are provided to demonstrate that the proposed cooperative control scheme can prevent obstacle and interagent collisions while achieving robust formation in heterogeneous underactuated vessel networks.

A Signal Compensation-Based Robust Swing-Up and Balance Control Method for the Pendubot

Abstract: This article proposes a novel robust swing-up and balance control method using data-driven compensation signals for the Pendubot system, which is underactuated and subject to dynamic friction, backlash, and modeling uncertainty. The method involves three major developments. First, the uncertainty of the system is described by a previous sampled unknown nonlinear term and its changing rate. Compensation signals are then designed to eliminate the influence of the previous sampled unknown nonlinear term and its changing rate on the outputs of the controlled plant. Second, based on the Lyapunov stability theory, a robust swing-up control method is proposed. Third, in order to eliminate the limit cycle phenomenon caused by the uncertainty near the unstable equilibrium point, a proportional-derivative balance controller based on compensation signals is proposed. The stability and tracking error analysis show that the compensation signals can effectively eliminate the influence of dynamic friction, backlash, and modeling uncertainty on the system. Experimental results verify the effectiveness of the proposed method by showing significant improvements in production rate, accuracy, and cost.

Reducing conservatism in robust stability analysis of fractional-order-polytopic systems

Abstract: This paper studies the robust stability of the fractional-order (FO) LTI systems with polytopic uncertainty. Generally, the characteristic polynomial of the system dynamic matrix is not an affine function of the uncertain parameters. Consequently, the robust stability of the uncertain system cannot be evaluated by well-known approaches including LMIs or exposed edges theorem. Here, an over-parameterization technique is developed to convert the main characteristic polynomial into a set of local over-parameterized characteristic polynomials (LOPCPs). It is proved that the robust stability of LOPCPs implies the robust stability of the uncertain system. Then, an algorithm is proposed to explore system’s robust stability through investigating the robust stability of these LOPCPs based on the exposed edges idea. For the sake of feasibility comparison, extensive examples are elaborated that reveal the superiority of the proposed algorithm.