Presence Of Input Disturbances Research Articles

Sliding-mode-based impulsive control for a class of time-delay systems with input disturbance

This paper addresses the impulsive stabilization problem for a class of time-delay systems in the presence of input disturbances. A novel impulsive control law is constructed, which is based on the discrete-time equivalent control design technique. The sliding-mode-based impulsive control law allows counteracting the disturbance effect using its previous step value. To adapt in real time to the variation in sampling periods, the sliding function is designed as a function of impulse interval. Piecewise Lyapunov functions based on the partition on the maximum impulse interval are introduced to deal with the time-varying structure of the sliding function. Sufficient conditions for exponential input-to-state stability (EISS) of the impulsively controlled systems are derived, in which the EISS gain characterizes the attenuation ability of the proposed impulsive control law on the disturbance estimation error. The disturbance attenuation performance of the proposed impulsive control strategy is demonstrated through the time-delay Chua’s circuit.

Waypoint Navigation of Quadrotor using Deep Reinforcement Learning

This paper proposes a Reinforcement Learning (RL) based technique to develop a simple neural network controller for the task of waypoint navigation in quadrotors. In this paper, the application of Twin Delayed Deep Deterministic (TD3) Policy Gradient algorithm for high and low-level control implementation for quadrotors is discussed. The proposed methods are tested on high fidelity Gym-Pybullet-Drones simulator. The effectiveness of the methods developed is validated through numerical simulations. The simulation results indicate that both control policies are successful in navigating through the assigned waypoint, with the low-level controller being accurate in the nominal flight conditions. In the presence of disturbance inputs, the high-level controller performs better when compared to the low-level controller.

Distributed robust adaptive output-feedback synchronization

This paper addresses the output synchronization of heterogeneous uncertain linear systems in the presence of input disturbance. The synchronization problem is solved by introducing the distributed model reference adaptive control. The distinguishing features of the proposed approach are the capability to synchronize the entire network output by using only the control input and the output of neighbouring systems. It also provides synchronization in the presence of bounded control input disturbance. The control input disturbance is handled by extending the proposed method with the sigma-modification. By using the distributed matching condition assumptions, the coupling gains between the agents can be updated adaptively. The distributed robust adaptive controller is first analysed by using the distributed Lyapunov function. Its effectiveness is validated in the simulation of the cart inverted pendulum systems stabilization problem.

Design of robust control strategy in drug and virus scheduling in nonlinear process of chemovirotherapy

Since the injection of the oncolytic viruses in the virotherapy process reduces the toxicity and drug resistance inherent in the chemotherapy; chemovirotherapy as a novel combination therapy has become an efficient cancer treatment. The primary purpose of this paper is to design a robust optimal control strategy for the chemovirotherapy through which the tumor density decreases to its stable condition with limited drug and virus delivery. This desired treatment should be responsive in the presence of input disturbances and parametric uncertainties. In this regard, an ODE (Ordinary Differential Equation) mathematical model of the chemovirotherapy process presenting the connection between the avascular tumor cells, immune cells, and treatment materials is utilized. H-infinity and DK iteration controllers are designed and implemented to the uncertain model, through the continuous and discrete procedures. By comparing the closed-loop simulations, discrete injection is found to be more effective (while is also more realistic in practice). The characteristics of an optimal treatment including the treatment frequency, drug and virus dosage, and suggested start time will be determined as the final results of this research.

Data-Driven Modeling and Design of Multivariable Dynamic Sliding Mode Control for the Underground Coal Gasification Project Thar

The energy output per unit time is an important performance metric to determine the potential of an underground coal gasification (UCG) site for electricity production. The energy output per unit time is a function of heating value and flow rate of syngas, and therefore, it is essential to devise a multivariable closed-loop system to enhance the efficiency of the UCG process. In this work, a model-based, multivariable dynamic sliding mode control (DSMC) has been designed for the cavity simulation model (CAVSIM), parameterized with the operating parameters and coal properties of the UCG Project Thar (UPT) field. The model-based control of CAVSIM is not possible due to its complex and multidimensional dynamics, and thus, a simple linear multivariable model is identified by employing a subspace (N4SID) system identification technique. The regular form of the linear model is formulated to design the model-based DSMC. Moreover, the stability of zero dynamics is shown on the approximate model of CAVSIM. Consequently, the designed controller is implemented on CAVSIM, and simulation results are compared with conventional sliding mode control (SMC). It has been observed that both the controllers have achieved the tracking objectives in the presence of input disturbance and modeling uncertainties. However, DSMC utilizes lesser control energy to achieve the desired objectives. Furthermore, the continuous control inputs in DSMC significantly reduce chattering.

Dual-mode global stabilization of high-order saturated integrator chains: LMI-based MPC combined with a nested saturated feedback

This paper considers the problem of high-performance global stabilization of an integrator chain via a bounded control at the presence of input disturbance. While nested saturated feedback (NSF) is known as the most inspiring existing solution in the literature, we shall highlight the inherent shortcomings of this approach which cause a poor performance in terms of convergence rate. Then, a novel dual-mode control scheme combining an improved NSF law with a linear matrix inequality (LMI)-based model predictive controller (MPC) is developed to overcome the weaknesses of pure NSF. By offline calculations, a set of nested robust invariant attraction regions and their attributed feedback gains are computed respecting the known bound for disturbance. In online implementation, an improved NSF law is adopted (as the first mode) to bring states within the largest invariant set. The proposed LMI-based state feedback law is applied thereafter (as the second mode) to bring state vector toward the origin with a fast convergence rate. In both modes, the well-known extended state observer (ESO) is employed to effectively suppress the external disturbance. The superiority of the proposed globally stabilizing scheme in terms of faster convergence and improved disturbance rejection ability is demonstrated comparing performances of foremost existing solutions in case of a triple integrator chain.

Observer-based sensor fault estimation in nonlinear systems

Sensor bias faults and sensor gain faults are two important types of faults in sensor. Simultaneous estimation of these sensor faults in nonlinear systems in the presence of input disturbance and measurement noise is challenging and has not been adequately addressed in literature. Hence, this article develops an observer-based sensor fault estimation method for generalized sector-bounded nonlinear systems in the presence of input disturbance and measurement noise. A generalized sector-bounded nonlinearity was chosen because it encompasses a wide range of nonlinearities including Lipschitz, positive real, and dissipative. This article presents necessary and sufficient conditions to achieve a suboptimal cost for a cost function consisting of the sum of the square integrals of the estimation errors to the square integrals of the disturbances in the form of linear matrix inequality. The linear matrix inequality can be solved offline to explicitly calculate observer gain, and the resulting observer simultaneously estimates the system states as well as both bias and gain faults in the sensors. Compared to previous literature, the proposed methodology is designed to work in the presence of both input disturbance and measurement noise. Additionally, this article considers a generalized sector-bounded nonlinearity which encompasses a variety of different physical nonlinearities. Furthermore, the observer does not require the online solution of the Riccati equation and is thus computationally less intensive compared with the methods of extended Kalman filtering. The observer design procedure is demonstrated through two illustrative examples consisting of a fourth-order double spring–mass system and a third-order wind turbine power transmission mechanism.

Dynamic modeling and vibration control of a flexible aerial refueling hose

In this study, we consider a boundary control problem of a flexible aerial refueling hose in the presence of input disturbance. To provide an accurate and concise representation of the hose's behavior, the flexible hose is modeled as a distributed parameter system described by partial differential equations (PDEs). Boundary control is proposed based on the original PDE dynamics to regulate the hose's vibration. A disturbance observer is designed to estimate the input disturbance. Then based on Lyapunov's direct method, the state of the system is proven to converge to a small neighborhood of zero by appropriately choosing design parameters. Finally, the results are illustrated using numerical simulations for control performance verification.

Adaptive attitude and position control of an insect-like flapping wing air vehicle

This study describes an adaptive sliding mode technique for attitude and position control of a rigid body insect-like flapping wing model in the presence of uncertainties. For this purpose, a six-degrees-of-freedom nonlinear and time-varying dynamic model of a typical hummingbird is considered for simulation studies. Based on the quasi-steady assumptions, three major aerodynamic loads including delayed stall, rotational lift and added mass are presented and analyzed, respectively. Using the averaging theory, a time-varying system is then transformed into the time-invariant system to design the adaptive controller. The controller is designed so that the closed-loop system will follow any desired trajectory without prior information about uncertainties. In the final stage, in order to find the wing kinematic parameters and to ensure the feasibility of the control commands, two feedforward artificial neural networks are developed and trained using aerodynamic forces and moments from the open-loop simulation data. In comparison with non-adaptive technique, it is shown that the adaptive sliding mode controller is able to stabilize the vehicle in the presence of input disturbances and model uncertainties.

Adaptive nonlinear observer for state and unknown parameter estimation in noisy systems

This paper proposes a novel adaptive observer for Lipschitz nonlinear systems and dissipative nonlinear systems in the presence of disturbances and sensor noise. The observer is based on an H∞ observer that can estimate both the system states and unknown parameters by minimising a cost function consisting of the sum of the square integrals of the estimation errors in the states and unknown parameters. The paper presents necessary and sufficient conditions for the existence of the observer, and the equations for determining observer gains are formulated as linear matrix inequalities (LMIs) that can be solved offline using commercially available LMI solvers. The observer design has also been extended to the case of time-varying unknown parameters. The use of the observer is demonstrated through illustrative examples and the performance is compared with extended Kalman filtering. Compared to previous results on nonlinear observers, the proposed observer is more computationally efficient, and guarantees state and parameter estimation for two very broad classes of nonlinear systems (Lipschitz and dissipative nonlinear systems) in the presence of input disturbances and sensor noise. In addition, the proposed observer does not require online computation of the observer gain.

Stability of adaptive control in the presence of input disturbances and H∞. performance

Adaptive control in the presence of input disturbances is considered. It is shown that standard adaptive control algorithms can stabilize the system in the presence of input disturbances and guarantee an ℒ2-gain bound. This method allows one to guarantee the performance of the adaptive control even before the adaptive parameters converge to their optimal values. The results are obtained for linear systems with polytopic type parameter uncertainties. Static output-feedback as well as state feedback are considered. The computational efficiency of the results is also discussed. An illustrative example is given.

Multirate Observers for Nonlinear Sampled-Data Systems Using Input-to-State Stability and Discrete-Time Approximation

This paper is devoted to the problem of nonlinear state estimation under multirate sampling in presence of disturbance inputs. Considering a general description of a nonlinear sampled-data system, we establish a prescriptive framework for multirate observer design via an approximate discrete-time model of the plant. This framework is shown to be input-to-state stable in a semiglobal practical sense with respect to the estimation error for the unknown exact discrete-time model. A numerical example of an aerospace vehicle with input and output channels of various sampling rates demonstrates how the multirate observer can drastically improve performance compared with the single-rate observer.

Self-Scheduled Control of a Gyroscope

A classical mixed sensitivity minimization approach and a model matching formulation are compared with the goal to design a linear parameter-varying augmented state feedback control law for a laboratory-scale control moment gyroscope. Dynamic weighting filters are used to impose integral action and roll-off on the controller. Consequently, measurement noise is effectively suppressed and steady state accuracy is guaranteed even in the presence of input disturbances. Both designs are validated in real-time experiments and compared to a previous design that uses static weights. With the new designs, control effort is reduced while transient performance is maintained and tracking accuracy, as well as disturbance attenuation, is improved.

Motion Planning and Stabilization Control of a Multipropeller Multifunction Aerial Robot

A multipropeller multifunction aerial robot capable of flight and wall climbing is presented in this paper. This novel robot consists of four propellers and two leg-wheel mechanisms. The propellers providing thrust for the vehicle are devoted to the attitude control. Two leg-wheel mechanisms are used for the wall climbing. The dynamic modeling in flight mode is derived in terms of the coupling between the main body and the legs. The wall-climbing mode of the robot falls into wheel-wall-climbing mode and leg-wall-climbing mode, while the latter is the focus of this paper. The kinematic and dynamic modeling, as well as the constraints in leg-wall-climbing mode are investigated. Based on the model, the leg-wall-climbing motion planning is proposed in terms of the constraints. The paper also presents a stabilization control strategy to maintain the attitude stability when the aerial robot is in leg-wall-climbing mode. Simulations of the robot in leg-wall-climbing mode are accomplished to show the effectiveness of the designed stabilization controller at the presence of input disturbances, sensor noise, sensor delays, and parametric modeling errors. A quadrotor subsystem experimental platform is built, and the experimental results support the theoretical analysis.

Noncertainty-equivalent multi-variable adaptive control of submersibles using filtered signals

Based on the attractive manifold design approach, a noncertainty-equivalent adaptive autopilot for the dive-plane control of submersibles, equipped with bow and stern hydroplanes, is developed. The parameters of the model are unknown and disturbance inputs are assumed to be present. The objective is to control the depth and pitch angle of the vehicle. For the vehicle in the presence of disturbance input and parameter uncertainties, it is shown that in the closed-loop system with σ-modification, the pitch angle and depth tracking errors are uniformly ultimately bounded. Also it is proven that for the disturbance-free submarine model with unknown parameters, precise asymptotic tracking of the reference trajectories is accomplished. Furthermore the control system accomplishes regulation of the system trajectories to a manifold, and asymptotically achieves the performance of a deterministic controller. Simulation results are presented which show that the submarine performs the specified dive-plane maneuvers in spite of large parameter uncertainties and random disturbance forces.

Discrete Time Variable Structure Control for the Dynamic Positioning of an Offshore Supply Vessel

This paper presents a Discrete-Time Variable-Structure Control (DTVSC) for the dynamic positioning system of a marine supply vessel. The DTVSC guarantees robustness with respect to disturbances and parametric variations. Two wave-filtering approaches are employed: the Extended Kalman filter (EKF) and the multi-rate Kalman filter (MREKF). The proposed solution is compared with a PID-based control and a passive non-linear wave filter. The reported simulations show that the proposed solution produces better performances and it is robust in the presence of input disturbances and model uncertainties.

Robust output tracking control of a class of non-minimum phase systems and application to VTOL aircraft

In this article, we study the output tracking control of a class of MIMO nonlinear non-minimum phase systems in the presence of input disturbances. In order to attenuate the effects of disturbances, the method of uncertainty and disturbance estimator (UDE) is extended to the controller design for non-minimum phase systems. Due to the fact that the accumulated disturbances is composed of internal states and external disturbances, a different stability analysis is given, and the overall closed-loop system is proved to be semi-globally stable. The proposed state-feedback controller not only forces system outputs to asymptotically track desired trajectories, but also drives the unstable internal dynamics to follow bounded and causal ideal internal dynamics (IID) solved via stable system centre (SSC) method. Simulation results demonstrate that the proposed controller achieves excellent tracking and disturbance rejection performance via the example of VTOL aircraft which has been the benchmark of nonlinear non-minimum phase systems.

Enthalpy predictive functional control of a pasteurisation plant based on a plate heat exchanger

Model predictive control (MPC) has been proven its efficiency in several industrial applications for over three decades. In this paper, predictive functional control (PFC) in its first order as well as its higher order forms is applied to control the pasteurisation temperature in a plate heat exchanger. The originality of the paper lies in the investigation of four control approaches using a combination of manipulated variables (medium temperature and product flow) tested in the presence of input disturbances and setpoint changes, leading to the so called 'enthalpy gearbox'. The paper describes the broad lines of first principles modelling of the studied pasteurisation plant sections, as well as giving an idea of the model accuracy when compared to real plant behaviour. A reminder of the first order PFC formulation as well as the full derivation of the analytical higher PFC control laws and the adequacy of a given control law over another are given. The PFC enthalpy approach (enthalpy gearbox) is finally compared in terms of performances to the simple PFC control scheme as well as the revisited performances given by classical control (PID) in terms of mean absolute error (MAE), overshooting and constraints violation.

Event-driven intermittent control

An intermittent controller with fixed sampling interval is recast as an event-driven controller. The key aspect of intermittent control that makes this possible is the use of basis functions, or, equivalently, a generalised hold, to generate the intersample open-loop control signal. The controller incorporates both feedforward events in response to known signals and feedback events in response to detected disturbances. The latter feature makes use of an extended basis-function generator to generate open-loop predictions of states to be compared with measured or observed states. Intermittent control is based on an underlying continuous-time controller; it is emphasised that the design of this continuous-time controller is important, particularly in the presence of input disturbances. Illustrative simulation examples are given.

Dynamic inversion with zero-dynamics stabilisation for quadrotor control

For a quadrotor, one can identify the two well-known inherent rotorcraft characteristics: underactuation and strong coupling in pitch-yaw-roll. To confront these problems and design a station-keeping and tracking controller, dynamic inversion is used. Typical applications of dynamic inversion require the selection of the output control variables to render the internal dynamics stable. This means that in many cases, perfect tracking cannot be guaranteed for the actual desired outputs. Instead, the internal dynamics of the feedback linearised system is stabilised using a robust control term. Unlike standard dynamic inversion, the linear controller gains are chosen uniquely to satisfy the tracking performance. Stability and tracking performance are guaranteed using a Lyapunov-type proof. Simulation with a typical nonlinear quadrotor dynamic model is performed to show the effectiveness of the designed control law in the presence of input disturbances.