Exogenous Disturbances Research Articles

Adaptive Perturbation Rejection Control for a Class of Converter Systems With Circuit Realization

This article is concerned with the robust adaptive control circuit design for pulse wide modulation (PWM)-based dc–dc buck converters with load variations and exogenous disturbances. A robust adaptive perturbation rejection control strategy is first developed to suppress time-varying and state-dependent perturbations, which are composed of load variations and disturbances. Then, equivalent analog control circuits of the adaptive control strategy are implemented on the basis of the circuit theory. Bounded tracking of the closed-loop converter system in the presence of perturbations is achieved based on the Lyapunov stability theorem. Simulations and experimental results are provided to validate the efficiency of the proposed adaptive perturbation rejection control strategy in a dc–dc buck converter system.

Self‐triggered predictive control of nonlinear systems using approximation model

AbstractThis article studies discrete‐time model predictive control of continuous‐time nonlinear systems with measurement noises and exogenous disturbances. We consider the co‐design of discrete‐time finite horizon optimal control problem (FHOCP) and the associated self‐triggering schemes that schedule the time instants for sampling the state and computing the FHOCP. The state‐dependent nature of the self‐triggered scheduling can not only dynamically adjust the inter‐sampling period according to the system status, but dynamically discretize the model used in the FHOCP as well, in order to reduce the complexity of the FHOCP if designed appropriately. It is shown that the system can be stabilized with uniform ultimate boundedness, as long as the scheduling scheme matches the approximation model such that the one‐step approximation error between the predicted state and the actual state is below a threshold related to the cost function. These results can be applied to most existing model approximation methods with either fixed or time‐varying sampling rates.

A Modified Disturbance-Rejection Approach in Networked Control Systems Based on Adaptive Model Predictive Control and Equivalent-Input-Disturbance

This paper presents an adaptive compensation control strategy for packet losses, time delays, and exogenous disturbances in a networked control system. The structure consists of five parts: a plant, a Luenberger observer, an equivalent-input-disturbance (EID) estimator, an adaptive model predictive controller (AMPC), and a network. The AMPC in the local main control room produces an adaptive tracking gain, which can ensure the effective tracking of the reference signal in the presence of uncertainty and time delays in the plant. The EID estimator at the local site compensates for packet losses and exogenous disturbances through an independently designed state observer and a low-pass filter. A practical application case results show the effectiveness of the presented method compared with the conventional EID approach.

Quantum microgrid state estimation

This paper investigates the feasibility and efficiency of quantum-circuit-based algorithms for microgrid state estimation. Our new contributions include: (1) a general quantum state estimation (GQSE) formulation is devised for swing-bus-contained microgrids through the quantized Gaussian–Newton iteration, (2) a preconditioned quantum linear solver (PQLS) is developed for tackling the ill-conditioned GQSE with limited quantum resources, and (3) an enhanced quantum state estimation (EQSE) algorithm is further established for hierarchical-control-based microgrids with exogenous disturbances. Extensive case studies demonstrate the correctness of GQSE, PQLS and EQSE in two typical microgrids. The robustness and convergence performance of EQSE are also verified.

Finite frequency fault estimation and fault-tolerant control for dynamics of high-speed train based on descriptor systems

In this paper, novel fault estimation and fault-tolerant control methods are proposed for dynamics of high-speed train based on descriptor systems with uncertainties in finite frequency domain. Dynamics of high-speed train is established based on multi-particle model considering that basic resistance is seen as the coefficient of state variables, and additive resistance and the operating noise are seen as multi-source disturbance. Concurrent actuator, sensor faults, and wind gust are considered simultaneously; wind gust is modeled as a disturbance generated by the exogenous system, and an uncertain descriptor system with actuator fault and the exogenous disturbance is established by seeing the sensor fault of high-speed train as the state variables. A robust disturbance-observer-based fault estimation method is proposed to decouple the non-linearity of the descriptor system, so that the combining estimation of the fault and wind gust is implemented. This observer has an unknown input structure, and its gain matrices are formulated as linear matrix inequalities. The observer not only guarantees the augmented state estimation error is asymptotic stable but also the actuator fault estimation and wind gust estimation errors are robust to the multi-source disturbance and the uncertainties. Based on the estimation results, the fault-tolerant controller associated with the state estimation, faults estimation, and wind gust estimation results is proposed to implement a stable close-loop fault-tolerant control for dynamics of high-speed train. Simulation examples are given to illustrate the effectiveness of this method.

Calibrating building simulation models using multi-source datasets and meta-learned Bayesian optimization

Reliable building simulation models are key to optimizing building performance and reducing greenhouse gas emissions. Informed decision making requires simulation models to be accurate, extrapolatable, and interpretable, all of which require calibrating model simulations to ground truth. Complicated building dynamics and highly uncertain exogenous disturbances make the model calibration process challenging and expensive; hence, a scalable and efficient calibration approach is needed to enable actual application. Current automatic calibration algorithms do not leverage data collected from multiple sources: for example, data obtained from previous calibration tasks on other buildings. In this paper, we employ probabilistic deep learning to meta-learn a distribution using multi-source data acquired during previous calibration. Subsequently, the meta-learned Bayesian optimizer accelerates calibration of new, unseen tasks. The few-shot (that is, requiring few model simulations) nature of the proposed algorithm is demonstrated on a Modelica library of residential buildings validated by the United States Department of Energy (USDoE). The proposed algorithm is compared against classical Bayesian optimization-based calibration, and it is shown that ANP significantly sped up the calibration procedure: the optimal model parameters are identified with 40–60% less simulations compared to the baseline.

Neural network-based adaptive fault-tolerant control for load following of a MHTGR with prescribed performance and CRDM faults

A neural network-based adaptive fault-tolerant control scheme (NN-AFTCS) for load following of a modular high-temperature gas-cooled reactor (MHTGR) system possessing parameter uncertainties, exogenous disturbances, and control rod drive mechanism (CRDM) faults, capable of guaranteeing prescribed performance is developed in this paper. From practical point of view, the mathematical model of the MHTGR system is first described with consideration of total CRDM fault effects together with lumped uncertainties that consist of parameter uncertainties, exogenous disturbances, and unmeasured states, where the unknown values of both time-varying total CRDM fault effects and lumped uncertainties are approximated by utilizing the radial basis function neural network (RBFNN) owing to its online learning capability. After that, with the help of the RBFNN, a fault-tolerant controller is proposed to compensate the adverse effects of the CRDM faults and lumped uncertainties, while at the same time achieving prescribed performance guarantees by invoking error transformation technique. Moreover, the dynamic surface control technique is also employed such that the problem of “explosion of complexity” existing in the conventional backstepping is averted. With Lyapunov stability theorem, it is proved that the load following error in the closed-loop MHTGR system would converge to a small residual set within a finite time. At last, simulation and comparison results confirm that the proposed NN-AFTCS exhibits higher load following accuracy, better CRDM fault-tolerant capability, and stronger robustness against lumped uncertainties than a disturbance observer-sliding mode controller and an active disturbance rejection controller.

Quadcopter UAVs Extended States/Disturbance Observer-Based Nonlinear Robust Backstepping Control.

A trajectory tracking control for quadcopter unmanned aerial vehicle (UAV) based on a nonlinear robust backstepping algorithm and extended state/disturbance observer (ESDO) is presented in this paper. To obtain robust attitude stabilization and superior performance of three-dimension position tracking control, the construction of the proposed algorithm can be separated into three parts. First, a mathematical model of UAV negatively influenced by exogenous disturbances is established. Following, an extended state/disturbance observer using a general second-order model is designed to approximate undesirable influences of perturbations on the UAVs dynamics. Finally, a nonlinear robust controller is constructed by an integration of the nominal backstepping technique with ESDO to enhance the performance of attitude and position control mode. Robust stability of the closed-loop disturbed system is obtained and guaranteed through the Lyapunov theorem without precise knowledge of the upper bound condition of perturbations. Lastly, a numerical simulation is carried out and compared with other previous controllers to demonstrate the great advantage and effectiveness of the proposed control method.

Image Based Visual Servoing for Floating Base Mobile Manipulator Systems with Prescribed Performance under Operational Constraints

This paper presents a novel Image-Based Visual Servoing (IBVS) control approach for Floating Base Mobile Manipulator Systems (FBMMSs) that imposes prescribed transient and steady-state response on the image feature coordinate errors while satisfying the visibility constraints that arise owing to the camera’s limited field of view. The proposed control strategy does not incorporate any knowledge on either the FBMMS dynamic model, the exogenous disturbances, or the inevitable camera calibration and depth measurement errors. More specifically, it guarantees: (i) predefined behavior in terms of overshoot, convergence rate, and maximum steady-state error value of the image features and system velocities tracking errors; (ii) satisfaction of camera field of view constraints; (iii) bounded closed-loop control signals, and (iv) reduced design and implementation complexity. Additionally, the performance of the developed scheme is solely determined by certain designer-specified performance functions/parameters, and it is fully decoupled by the control gains selection. The efficiency of the proposed scheme is demonstrated via a realistic simulation study, using an eye-in-hand Underwater Vehicle Manipulator System (UVMS) as a test-bed FBMMS platform.

Robustification of the state-space MRAC law for under-actuated systems via fuzzy-immunological computations.

This paper formulates an enhanced Model-Reference-Adaptive-Controller (MRAC) that is augmented with a fuzzy-immune adaptive regulator to strengthen the disturbance-attenuation capability of closed-loop under-actuated systems. The proposed scheme employs the conventional state-space MRAC and augments it with a pre-configured fuzzy-immune mechanism that acts as a superior regulator to dynamically modulate the adaptation gains of the Lyapunov gain-adjustment law. The immunological computations increase the controller's adaptability to flexibly manipulate the damping control effort under exogenous disturbances. The efficacy of the proposed Immune-MRAC law is comparatively analyzed under practical disturbance conditions by conducting real-time hardware experiments on the QNET rotary pendulum. The experimental outcomes validate the faster transient-recovery behavior and stronger damping effort of the proposed control law against the exogenous disturbances while preserving the system's asymptotic stability and control energy efficiency.

Input-to-State Stabilization of Nonlinear Impulsive Delayed Systems: An Observer-Based Control Approach

This paper addresses the problems of input-to-state stabilization and integral input-to-state stabilization for a class of nonlinear impulsive delayed systems subject to exogenous disturbances. Since the information of plant's states, time delays, and exogenous disturbances is often hard to be obtained, the key design challenge, which we resolve, is the construction of a state observer-based controller. For this purpose, we firstly propose a corresponding observer which is independent of time delays and exogenous disturbances to reconstruct (or estimate) the plant's states. And then based on the observations, we establish an observer-based control design for the plant to achieve the input-to-state stability (ISS) and integral-ISS (iISS) properties. With the help of the comparison principle and average impulse interval approach, some sufficient conditions are presented, and moreover, two different linear matrix inequalities (LMIs) based criteria are proposed to design the gain matrices. Finally, two numerical examples and their simulations are given to show the effectiveness of our theoretical results.

Robust fault detection and isolation for dynamics of high-speed train with uncertainties based on descriptor systems

This paper proposes a new type of robust fault detection and isolation filter for dynamics of HST based on descriptor systems with uncertainties in finite frequency. This filter is designed based on the unknown input filter to decouple the non-linear variables due to the aerodynamic drag pressing on the trains. The exogenous disturbance is partitioned into two parts-the decoupling one is regarded as the augmented variables of the non-linear part of the systems, and the non-decoupling one is seen as the augmented disturbance along with the uncertainties. Concurrent faults of different positions are considered, residual evaluation functions and adaptive threshold are given to judge if the faults occur. Fault isolation is implemented by a set of detection subspaces associated with every different fault which is assigned to its own detection subspace. The residual is not only sensitive to the fault, but also has a robustness against the non-decoupling disturbance and uncertainties in finite frequency. Simulation examples are given to demonstrate the effectiveness of this method.

Disturbance Rejection and Predictive Control for Systems With Input and Output Delays

Disturbance rejection and time-delay compensation are important for control systems in practice. This article presents an observer-and-predictor-based method to reject an unknown exogenous disturbance for an input-delay system in which only delayed output is available. First, the equivalent-input-disturbance method is employed to reconstruct the system state and to estimate the disturbance. A prediction of the disturbance is calculated to reduce the effect of a phase lag caused by the delays. A new predictive scheme is designed for feedback control. Next, the system is divided into two subsystems for analysis and design: one for reference tracking and the other for disturbance rejection. Sufficient conditions are derived to guarantee the system’s stability. An analysis of the method reveals that the disturbance rejection can be viewed as a patch to the predictor-based feedback control. Finally, two case studies are used to show the validity and superiority of the method.

Adaptive fractional-order sliding-mode disturbance observer-based robust theoretical frequency controller applied to hybrid wind–diesel power system

This work presents design and theoretical analysis of an adaptive fractional-order sliding-mode disturbance observer (FO-SM-DOB)-aided fractional-order robust controller for frequency regulation of a hybrid wind–diesel based power system, considering endogenous/exogenous system disturbances. Adaptive FO-SM-DOB is designed to estimate unknown/uncertain lumped system disturbances, including parametric uncertainty and exogenous disturbances. Afterwards, an improved fractional-order sliding mode controller (FOSMC) augmented with the estimated output of FO-SM-DOB is designed and applied to accelerate system dynamics with minimum chattering in the control effort. The Mittag-Leffler stability theorem affirms the finite-time convergence of disturbance estimation error. Moreover, the closed-loop asymptotic stability of the overall control system has been guaranteed by applying Lyapunov argument. The effectiveness of the suggested resilient fractional-order nonlinear frequency controller is theoretically validated by performing an extensive comparative study with SMC, FOSMC (without DOB), state observer-based SMC (SOB-SMC), second-order SMC (without DOB), and conventional integer/fractional-order controllers. Simulation results establish the supremacy of the proposed resilient fractional-order nonlinear frequency controller over its other counterparts concerning fast disturbance rejection, weaker chattering, and a high degree of robustness against unknown lumped system disturbances. Further, to demonstrate the practicability and validate the effectiveness of the proposed control strategy, magnetic levitation system and IEEE 39-bus New England power system are considered and successfully tested on MATLAB platform.

Sparse Filtering Under Norm-Bounded Exogenous Disturbances Using Observers

The paper considers the sparse filtering problem under arbitrary norm-bounded exogenous disturbances. We propose a simple and universal observer-based approach to its solution, based on the LMI technique and the method of invariant ellipsoids; it allows the use of a reduced number of system outputs. From a technical point of view of application, we reduce the original problem to semi-definite programming, which is easily solved numerically. The proposed simple approach is easy to implement and can be equally extended to systems in continuous and discrete time.

Reduced-order intermediate variable observer based fault estimation and fault-tolerant control for fuzzy stochastic systems with exogenous disturbance

This paper studies fault estimation and fault-tolerant control problems for fuzzy systems with exogenous disturbance and stochastic noise. The fuzzy reduced-order intermediate variable observers and the disturbance observers are proposed to reconstruct the sensor fault, the actuator fault and the disturbance. According to the estimated results, the observer based fault-tolerant controllers are devised. Compared with the full-order observer design method, the designed reduced-order observers are with lower dimensions and less cost. Different from the classic intermediate observer, the feedback term of the output estimation error is introduced into our observers, which increases the design freedom. And the obtained linear matrix inequality conditions can ensure that the whole closed-loop systems are mean-square exponentially stable. Finally, two examples are offered to verify the feasibility of the proposed method.

Robust leader-following consensus control of one-sided Lipschitz multi-agent systems over heterogeneous matching uncertainties

This paper presents a novel consensus control methodology for the multi-agent systems (MASs) with one-sided Lipschitz (OSL) nonlinearities under heterogeneous matching uncertainties, non-zero input to the leader, and external perturbations. A leader-following consensus control scheme is considered, and a control law is provided to assure that the followers track the leader successfully to deal with the uncertainties. The present work considers heterogeneous matching uncertainties, and a new approach with different Lyapunov function, rather than using Laplacian matrix term in the Lyapunov function, has been considered for complete elimination of the uncertainties. The proposed strategy is extended to the case where exogenous disturbances are also acting (to deal with un-matching uncertainties) on the nonlinear dynamics, and a robust consensus control approach is revealed. To overcome the chattering problem, a continuous-time consensus control law is also presented to guarantee the convergence of the consensus error to a small bounded set in the presence of external disturbance. To the best of the authors’ knowledge, consensus control of a generalized form of OSL agents for the complete elimination of heterogeneous matching uncertainties and for considering non-zero input to the leader has been explored for the first time. The developed treatment is different from the conventional methods on the linear or Lipschitz nonlinear multi-agents under heterogeneous uncertainties. Finally, an application of the proposed methods to moving agents is furnished to illustrate the strength of the resultant consensualization protocol.

Cooperative robust adaptive control of multiple trains based on RBFNN position output constraints

AbstractThe stability of each train, high control accuracy, and minimum safe separation distance are important indexes to measure the performance of the cooperative control system of multiple trains. In this article, aiming at the problem of low accuracy of multiple trains cooperative control with nonlinear running resistance and external disturbance, the distributed cooperative robust adaptive control scheme for multiple trains with RBFNN position output constraints based on train running curve tracking is proposed. Multiple different control techniques are offered for different trains, and that they are based on local knowledge of position, speed, and acceleration. The leading train's speed and position precisely match the planned operation curve, while the following train keeps the tracking interval at the minimum safe distance between two trains. In order to reduce the influence of the uncertainty of basic resistance parameters and external interference on the cooperative control of multiple trains, the parameter uncertainties are compensated by adding a robust adaptive law to the multiple trains control based on position output constraints. The lumped exogenous disturbances (additional resistance, external interference, measurement noise, etc.) are estimated using an RBFNN approximator for the unknown term of the cooperative system. The stability of the cooperative operation of multiple trains is confirmed using the Lyapunov stability theorem. The performance of the proposed scheme was evaluated by the cooperative control system of multiple trains in predecessor following (PF) and bidirectional control (BC) modes.

Robust attitude tracking on the Special Orthogonal Group [formula omitted] using PD-type state feedback and linear matrix inequalities

The problem of rigid-body attitude tracking in the presence of exogenous disturbances is addressed. Attitude is parameterized using the rotation matrix, an element of the Special Orthogonal Group SO(3), as it provides a singularity-free and unambiguous attitude description. The closed-loop stability and robustness properties of a PD-type state-feedback control law, proposed in literature for attitude tracking using rotation matrices, are investigated using the nonlinear H∞ control framework. Starting from a dissipation inequality, sufficient conditions are derived which ensure that the closed-loop energy gain from bounded, finite-energy exogenous disturbances to a specified error signal respects a given upper bound. Then, the sufficient conditions are reformulated using the state and input matrices for the translational double integrator, and recast as linear matrix inequalities (LMIs). Lastly, the reformulated LMIs are used to synthesize controller gains for the proportional and derivative state-feedback terms in the original SO(3) control law. The controller synthesis problem for a microsatellite is considered as a case study. The controller gains are obtained using the proposed LMI-based procedure, and the tracking and disturbance rejection capabilities of the SO(3) controller are illustrated.

A self-triggered stochastic model predictive control for uncertain networked control system

This paper is concerned with the stochastic model predictive control problem (SMPC) for a class of networked control systems with exogenous disturbances and restrictions on communication frequency. A self-triggered SMPC algorithm with an improved triggering condition is designed which integrates the co-design of both the current control inputs and the maximum sampling interval, with the aim of reducing the amounts of communication transmission while guaranteeing the specific performance. To give consideration to both probabilistic guarantee and computability, chance constraints on both states and inputs are transformed into deterministic ones by leveraging Cantelli's inequality and linearisation techniques, so as to be solvable for the optimisation problem. Meanwhile, the nonconvex terms in dynamics of covariance propagation are averted through introducing the covariance upper bound control approach. The formulated online optimisation problem is convex to all decision variables. The theoretical analysis on recursive feasibility and closed-loop stability is addressed for the proposed self-triggered SMPC scheme. Finally, the efficacy and achievable performance of the proposed algorithm are showcased through numerical simulations.