Search Results (11)

This paper addresses the observer-based controller design for adaptive cruise control (ACC) systems using a linear matrix inequality (LMI) framework, considering both input saturation and disturbance attenuation performance. To formulate the controller design problem as LMIs, the nonlinear input saturation is represented as a convex combination of linear state feedback controllers. Unlike conventional approaches that only reformulate input saturation, this work further incorporates the estimated state and the decay rate of a Lyapunov function to establish an invariant level set condition, leading to a novel LMI-based design criterion. The proposed method incorporates level set conditions to handle input constraints and employs an ${\mathcal{H}}_{\infty }$ criterion to ensure disturbance attenuation. Since the resulting design conditions are non-convex due to bilinear matrix terms, a two-step approach is applied to derive the controller design conditions in the form of LMIs. Finally, simulation results are presented to demonstrate the effectiveness of the proposed method. Full article

Nonlinear control theory applied to unmanned aeronautical vehicles is an engineering topic that has received higher and higher popularity during the last decade. Model-based control approaches have shown increased performance in flight control accuracy and robustness compared to model-free proposals based on parameter adaptation and estimation. However, model-based structures need more computational efforts in terms of spatial and temporal variables. To avoid these constraints, the latest drone flight controls are based on quaternion models, ensuring more advanced computational performances. To this aim, this paper deals with a flight control algorithm of a quadrotor, in which the mathematics model of the plant is defined in terms of quaternions. Additionally, when aerial vehicles are used in specific applications such as slung load transportation and agriculture fields, among others, the variation of the mass receives high importance since it could make the entire system unstable. In the same line of ideas, this paper presents a ${\mathcal{H}}_{\infty }$ strategy, combined with a Super-Twisting Sliding-Mode Control, ensuring the control objective of the mass variations identification, and trajectory tracking, to be solved. The stability analysis of the proposed control approach is also discussed, and the quality and performances of the presented control strategy are tested by simulations, in an interesting case in which mass variations and external perturbations cannot be negligible. Full article

The paper concerns the technology of the design, realization, and testing of a flexible smart wing in a wind tunnel equipped with a turbulence generator. The system of smart wing, described in detail, consists mainly of: a physical model of the wing with an aileron; an electric servomotor of broadband with a connecting rod-crank mechanism for converting the rectilinear motion of the servoactuator into the aileron deflection; two transducers: an encoder for measuring the deflection of the control aileron and an accelerometer mounted on the wing to measure its bending and torsional vibrations; a procedure for determining the mathematical model of the wing by experimental identification; a turbulence generator in the wind tunnel; implemented ${\mathscr{H}}_{\infty }$ and LQG algorithms for active control of vibrations. The attenuation experimentally obtained for the aeroelastic vibrations of the wing, but also for those accentuated by the turbulence, reaches values of up to 50%. Full article

This paper addresses a mode-dependent state-feedback ${\mathcal{H}}_{\infty }$ control for stochastic descriptor hybrid systems, considering both the absence and presence of actuator saturation. Firstly, the necessary and sufficient conditions for the stochastic admissibility criterion with ${\mathcal{H}}_{\infty }$ performance $\gamma$ of the closed-loop system are proposed. Given the proposed non-convex condition, the author reformulates it into linear matrix inequalities (LMIs). Then, to extend the result to the systems with actuator saturation, the actuator-saturated control input is expressed as a linear combination of a given state-feedback control input and a virtual control input that always remains under the saturation level. To verify this expression, the set invariant condition is also suggested by using the singular mode-dependent Lyapunov function candidate. Therefore, the conditions for the existence of both the mode-dependent state-feedback ${\mathcal{H}}_{\infty }$ control and the ellipsoidal shape invariant sets are successfully derived in terms of LMIs. Two numerical examples demonstrate the effectiveness of the proposed method by solving optimization problems subject to the proposed LMIs that minimize ${\mathcal{H}}_{\infty }$ performance $\gamma$ and maximize the invariant set, respectively. Full article

This paper presents a practical study on how to improve the ${\mathscr{H}}_{\infty }$ performance and meet the input–output constraints of the two-degrees-of-freedom (DOF) flexible-joint manipulator system (FJMS) with parameter uncertainties and external disturbances. For this reason, a robust constrained moving-horizon ${\mathscr{H}}_{\infty }$ controller is designed to improve the system ${\mathscr{H}}_{\infty }$ performance while still satisfying the input–output constraints of the uncertain system. First, the uncertain controlled system model of the two-DOF FJMS is established via the Lagrange equation method, Spong’s assumption, and the linear fractional transformation (LFT) technique. Then, the control requirements and input–output constraints of the uncertain system are transformed into the linear matrix inequality (LMI) via the theory of ${\mathscr{H}}_{\infty }$ control and the full-block multiplier technique. Next, the LMI optimization problem refreshed by the current state is addressed at each sample moment with the idea of the moving-horizon control of the model predictive control (MPC), and the calculated gain is implemented to the nonlinear closed-loop system under the state feedback structure. The validity and feasibility of the designed control scheme is finally verified via the results of simulation experiments. Full article

Applications of a quadrotor with payload, particularly for chemical spraying, have increased in recent times. The variation in payload mass over time causes a change in the moments of inertia (MOI). Moreover, large tilt angles are required for fast reference tracking and external disturbance rejection. These variations in plant parameters (i.e., mass and inertia) and large tilt angles can degrade the control scheme’s performance and stability. This article proposes a linear matrix inequalities (LMIs)-based linear parameter varying (LPV) control scheme for a quadrotor subject to time-varying mass, time-varying inertia, mass flow rate, and large tilt angles. The control strategy is designed by solving LMIs derived from quadratic ${\mathcal{H}}_{\infty }$ performance and D-stability. The robust stability and quadratic ${\mathcal{H}}_{\infty }$ performance are assessed by LMIs. The efficacy of the proposed methodology is established using numerical simulations, and its performance is compared to the linear time-invariant (LTI) ${\mathcal{H}}_{\infty }$ design with pole placement constraints. The results obtained show that the LPV control scheme gives better tracking performance in the presence of time-varying parameters, noise, and external disturbances without actuator saturation. In comparison to the LTI design technique, the proposed LPV scheme improves the rise time ($t_r$), settling time ($t_s$), and mean squared error (MSE) by up to $14\%$, $15\%$, and $30\%$, respectively. Moreover, smooth transitions are observed in the tilt angles and control signals with the LPV scheme, contrary to the LTI controller, which exhibits significant oscillations. Full article

Green manufacturing (especially the right production scale) and optimization of logistics transportation (especially logistics transportation to ensure economic benefits) have become challenging issues. This paper focuses on production planning and scheduling of dynamic supply chain via ${\mathcal{H}}^{\infty }$ control scheme. This paper considers the production scale control and resource scheduling optimization of complex dynamic supply chain under the impact of the COVID-19 pandemic.Firstly, the information of green manufacturing production is collected, and a new mathematical model of complex dynamic supply chain including multi-level distributor/retailer, multi-delay inventory and multi-lead time is established. Secondly, the optimal resource scheduling scheme is designed based on the principle of ${\mathcal{H}}^{\infty }$ cybernetics, which is called the hierarchical scheduling of complex dynamic supply chain in this paper. Finally, a numerical example shows the effectiveness and feasibility of the proposed scheme. Through this model, the production of manufacturers, the inventory of manufacturers and distributors, and the order quantity of distributors can be arranged in advance, which improves the robustness of the closed-loop dynamic supply chain. Full article

The current article presents the design, implementation, validation, and use of a Computer-Aided Control System Design (CACSD) toolbox for nonlinear and hybrid system uncertainty modeling, simulation, and control using $\mu$ synthesis. Remarkable features include generalization of classical system interconnection operations to nonlinear and hybrid systems, automatic computation of equilibrium points for nonlinear systems, and optimization of least conservative uncertainty bounds, with direct applicability for $\mu$ synthesis. A unified approach is presented for the step-down (buck), step-up (boost), and single-ended primary-inductor (SEPIC) converters to showcase the use and flexibility of the toolbox. Robust controllers were computed by minimization of the ${\mathcal{H}}_{\infty }$ norm of the augmented performance systems, encompassing a wide range of uncertainty types, and have been designed using the well-known mixed-sensitivity closed loop shaping $\mu$ synthesis method. Full article

Motion control involving DC motors requires a closed-loop system with a suitable compensator if tracking performance with high precision is desired. In the case where structural model errors of the motors are more dominating than the effects from noise disturbances, accurate system modelling will be a considerable aid in synthesizing the compensator. The focus of this paper is on enhancing the tracking performance of a wheeled mobile robot (WMR), which is driven by two DC motors that are subject to model parametric uncertainties and uncertain deadzones. For the system at hand, the uncertain nonlinear perturbations are greatly induced by the time-varying power supply, followed by behaviour of motion and speed. In this work, the system is firstly modelled, where correlations between the model parameters and different input datasets as well as voltage supply are obtained via polynomial regressions. A robust ${\mathcal{H}}_{\infty }$ -fuzzy logic approach is then proposed to treat the issues due to the aforementioned perturbations. Via the proposed strategy, the ${\mathcal{H}}_{\infty }$ controller and the fuzzy logic (FL) compensator work in tandem to ensure the control law is robust against the model uncertainties. The proposed technique was validated via several real-time experiments, which showed that the speed and path tracking performance can be considerably enhanced when compared with the results via the ${\mathcal{H}}_{\infty }$ controller alone, and the ${\mathcal{H}}_{\infty }$ with the FL compensator, but without the presence of the robust control law. Full article

Marine Satellite Tracking Antenna (MSTA) is an important shipboard device for ships to bidirectionally communicate with the outside. The attitude stabilization system is an important part of MSTA for keeping the antenna dish tracking the geostationary satellite in the presence of severe ship dynamics. In this paper, the designed high-performance attitude stabilization system is introduced, including hardware system, model identification and controller design. The detailed procedure for identifying the stepper motor model is stated, which is different from conventional procedure. The design procedure of the robust controller based on ${\mathcal{H}}_{\infty }$ loop shaping is given. The designed attitude stabilization system was tested on a Stewart platform that was used as the ship simulator. From the testing results, it can be seen that the performance of designed attitude stabilization system is very high and the tracking angle error can be limited to within $\pm 0.15$ deg, which satisfies the performance requirements of Ka-band MSTA product. Full article

Recent advances in the resolution of multi-model and multi-objective control problems via non-smooth optimization are exploited to provide a novel methodology in the challenging context of autoland design. Based on the structured ${\mathcal{H}}_{\infty }$ control framework, this paper focuses on the demanding flare phase under strong wind conditions and parametric uncertainties. More precisely, the objective is to control the vertical speed of the aircraft before touchdown while minimizing the impact of windshear, ground effects, and airspeed variations. The latter is indeed no longer controlled accurately during flare and strongly affected by wind. In addition, parametric uncertainties are to be considered when designing the control laws. To this purpose, extending previous results published by the authors in a conference paper, a specific multi-model strategy taking into account variations of mass and center-of-gravity location is considered. The methodology is illustrated on a realistic aircraft benchmark proposed by the authors, which is fully described in this paper and freely available from the SMAC (Systems Modeling Analysis & Control) toolbox website (http://w3.onera.fr/smac). Full article