Discrete-Time Robust Control With an Anticipative Action for Preview Systems

Robust Anticipative Controller Design with Application to A High Dynamic Engine Testbed

Spacecraft equipped with liquid propulsion systems are susceptible to propellant sloshing during orbit correction maneuvers. During a maneuver, the liquid propellant is pushed towards the bottom of the tanks due to the effective gravity of the thrusters, resulting in lateral liquid motion and a sloshing phenomenon. The sloshing effect, if not mitigated by the attitude control system, may cause dynamic instabilities and reduce maneuver performance. It is difficult to synthesize fluid mechanics models in a control design process, and spacecraft dynamics under sloshing remains a great challenge in terms of attitude control design. Mechanical analogies, such as pendulums or spring-mass systems, have been found to capture the main effects of liquid sloshing and can serve as a control design model; however, such mechanical analogies are inherently uncertain and a robust control methodology is required for assuring the spacecraft stability and performance during a maneuver. In this work, the quantitative frequency design approach is applied for the first time to provide robust controllers for spacecraft with multi propellant tanks and an attitude dynamics model incorporating an uncertain sloshing mechanical analogy. This methodology has been used to design the attitude control of SpaceIL's lunar lander Beresheet and was proved successful in 10 orbit correction maneuvers. A case study of one of these maneuvers is presented as well as telemetry results from the mission.

A systematic and complete method to design robust predictive controllers for unconstrained linear systems is proposed. The synthesis procedure is based on rigorous theoretical foundations, without resorting to approximations or ad hoc design guidelines, yet it remains a viable tool for practical application. A significant feature is that the robust predictive controller retains the servo performance of a nominal predictive controller designed using conventional methods. In addition, the robust predictive controller can be designed to guarantee perfect steady‐state rejection of asymptotically constant disturbances. The robust design method is developed for systems affected by unmodeled dynamics and is based on solving a discrete‐time model‐matching problem. It is shown that the robust controller can be classified legitimately as a predictive controller because it minimizes the same performance functional as the nominal predictive controller. An illustrative design example is given.

The field of active noise control is currently very popular due to the fact that it has many practical applications. Some of these applications include decreasing cabin noise in aircraft and automobiles, reducing noise in rocket payload bays, and minimizing machinery noise in factories. Many researchers have developed active control algorithms for vibrating plates radiating sound into free space or enclosures. Most of the controllers employed have been adaptive like the Filtered-x LMS techniques. Very little previous work has been focused on feedback control techniques. In an earlier paper we have described techniques for controlling sound transmission through rectangular plates radiating into a closed cavity. Robust controllers, based on Linear Quadratic Gaussian with Loop Transfer Recovery (LQG/LTR) methods were designed and implemented on simple test articles. In comparison with LMS algorithm, this robust controller did not perform well. The time delay present in the structural system was not considered in the LQG/LTR design method. Hence, a poor performance was the result. In this paper, we have designed and implemented a modified LQG/LTR controller with time delay on an acoustic chamber test article. This controller estimates the previous states from the present output and predicts states for feedback purposes. The time delay of the structure is compensated in the configuration of the estimator of the controller. We have obtained the simulation and experimental results with the modified controller. The performance of the modified LQG/LTR controller is much better than the LMS controller. The performance of the LMS and LQG/LTR controllers is investigated with respect to change in noise frequency and plant parameter variations. The experimental results are included in this paper.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Robust tracking control of uncertain nonlinear systems with adaptive dynamic programming

This paper reviews robust feedback control applications in power system. Robust control concept in power system control area evolves by the way of uncertainty modeling. Uncertainties can be caused by variation of loading, load parameters, network topology and machine parameters. One direct way to take into consideration of uncertainties is to enumerate various system operating conditions and use a set of linear time invariant systems to model the power systems. Enumeration method is used in robust control signal selection and robust controller design. Another way is to model uncertainties as structured or unstructured uncertainties. Controllers are designed based on nominal system condition to tolerate uncertainty as much as possible. The controller synthesis problem is solved by either Algebraic Ricatti Equation (ARE) or Linear Matrix Inequality (LMI). LMI is shown to be more flexible in controller design since it provides a way to specify system dynamic performance. Most recently, linear parameter varying (LPV) concept expands the use of enumeration method to express a set of linear time invariant (LTI) systems with state space dependent on linear parameters. LPV based control methods extend gain scheduling to design linear control for each operating conditions and overall a nonlinear controller for the varying parameter system.

Robust quantum control is crucial for realizing practical quantum technologies. Energy landscape shaping offers an alternative to conventional dynamic control, providing theoretically enhanced robustness and simplifying implementation for certain applications. This work demonstrates the feasibility of robust energy landscape control in a practical implementation with ultracold atoms. We leverage a digital mirror device (DMD) to shape optical potentials, creating complex energy landscapes. To achieve a desired objective, such as efficient quantum state transfer, we formulate a novel hybrid optimization approach that effectively handles both continuous (laser power) and discrete (DMD pixel activation) control parameters. This approach combines constrained quasi-Newton methods with surrogate models for efficient exploration of the vast parameter space. Furthermore, we introduce a framework for analyzing the robustness of the resulting control schemes against experimental uncertainties. By modeling uncertainties as structured perturbations, we systematically assess controller performance and identify robust solutions. We apply these techniques to maximize spin transfer in a chain of trapped atoms, achieving high-fidelity control while maintaining robustness. Our findings provide insights into the experimental viability of controlled spin transfer in cold atom systems. More broadly, the presented optimization and robustness analysis methods apply to a wide range of quantum control problems, offering a toolkit for designing and evaluating robust controllers in complex experimental settings.

Robust controllers ensure stability in feedback loops designed under uncertainty but at the cost of performance. Model uncertainty in time-invariant systems can be reduced by recently proposed learning-based methods, which improve the performance of robust controllers using data. However, in practice, many systems also exhibit uncertainty in the form of changes over time, e.g., due to weight shifts or wear and tear, leading to decreased performance or instability of the learning-based controller. We propose an event-triggered learning algorithm that decides when to learn in the face of uncertainty in the LQR problem with rare or slow changes. Our key idea is to switch between robust and learned controllers. For learning, we first approximate the optimal length of the learning phase via Monte-Carlo estimations using a probabilistic model. We then design a statistical test for uncertain systems based on the moment-generating function of the LQR cost. The test detects changes in the system under control and triggers re-learning when control performance deteriorates due to system changes. We demonstrate improved performance over a robust controller baseline in a numerical example.

We propose a robust control system which has robustness for disturbance and can deal with a nonlinear system with partially unknown structure by fusing reinforcement learning and robust control theory. First, we solved an optimal control problem without using unknown part of functions of the system, using neural network and the repetition learning of reinforcement learning algorithm. Second, we built the robust reinforcement learning control system which permits uncertainty and has robustness for disturbance by fusing the idea of H infinity control theory with above system.

Some robust control methods have been developed in the past in order to increase tracking performance in the presence of parametric uncertainties. In the presence of parametric uncertainty, unmodelled dynamics and other sources of uncertainties, robust control laws are used. Corless-Leitmann [1] approach is a popular approach used for designing robust controllers for robot manipulators. In early application of Corless-Leitmann [1] approach to robot manipulators [2, 3], it is difficult to compute uncertainty bound precisely. Because, uncertainty bound on parameters depends on the inertia parameters, the reference trajectory and manipulator state vector. Spong [4] proposed a new robust controller for robot manipulators using the Lyapunov theory that guaranties stability of uncertain systems. In this approach, Leithmann [5] or Corless-Leithman [1] approach is used for designing the robust controller. One of the advantage of Spong’s approach [4] is that uncertainty on parameter is needed to derive robust controller and uncertainty bound parameters depends only on the inertia parameters of the robots. Yaz [6] proposed a robust control law based on Spong’s study [4] and global exponential stability of uncertain system is guaranteed. However, disturbance and unmodelled dynamics are not considered in algorithm of [4, 6]. Danesh at al [7] develop Spong’s approach [4] in such a manner that control scheme is made robust not only to uncertain inertia parameters but also to robust unmodelled dynamics and disturbances. Koo and Kim [8] introduce adaptive scheme of uncertainty bound on parameters for robust control of robot manipulators. In [8], upper uncertainty bound is not known as would be in robust controller [4] and uncertainty bound is estimated with estimation law in order to control the uncertain system. A new robust control approach is proposed by Liu and Goldenerg [9] for robot manipulators based on a decomposition of model uncertainty. Parameterized uncertainty is distinguished from unparameterized uncertainty and a compensator is designed for parameterized and unparameterized uncertainty. A decomposition-based control design framework for mechanical systems with model uncertainties is proposed by Liu [10]. In order to increases tracking performance of uncertain systems, design of uncertainty bound estimation functions are considered. For this purpose, some uncertainty bound estimation functions are developed [11-15] based on a Lyapunov function, thus, stability of

Robust observer-based OMF vibration control of flexible linkage mechanisms using piezoelectric films

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Robust adaptive control for rotational deployment of an underactuated tethered satellite system

Robust motion tracking control of piezo-driven flexure-based four-bar mechanism for micro/nano manipulation

This article deals with the reduction of structural vibrations. Two approaches are possible to tackle this problem, either a passive method with dampers, or an active method with actuators that are controlled in order to decrease or even cancel the vibrations in the structure. The second method is used here. The objective is to control the damping of plants that are not only lightly damped but also whose models are uncertain. The proposed methodology is based on iso-damping contours and CRONE control. The iso-damping contour is defined thanks to fractional order integration in the Nichols plane and is graduated with the value of the damping factor of the closed-loop response. The CRONE control is a robust control method that uses fractional order integration. The methodology is here applied to a multivariable plant that is an aircraft wing model made with a beam and a tank whose different levels of fillings are considered as uncertainties.