Linear Parameter-varying Gain-scheduled Controller Research Articles

A robust adaptive linear parameter-varying gain-scheduling controller for aeroengines

This paper presents an on-board model-based adaptive linear parameter-varying (LPV) gain-scheduling controller for aeroengines exhibiting strong nonlinearity, uncertainty, and component performance degradation. A novel hybrid on-board adaptive model is proposed using a dual-loop (fast-slow) structure, which consists of an adaptive LPV model as the fast loop to guarantee the on-board performance, and a component-level model as the slow loop to provide accurate model information at a slow update frequency, without violating the real-time requirement of the fast loop. An innovative mixed adaptive extended Kalman filter is designed to estimate and update the model, by introducing an extended-Kalman-filter-based covariance scaling mechanism and updating the system noise covariance matrix by employing full covariance estimation. The component-level adaptive nonlinear model and mixed adaptive extended Kalman filter increase the accuracy of the on-board model, while the adaptive LPV model enhances its real-time performance. Finally, a robust polytopic adaptive LPV gain-scheduling controller is designed based on the adaptive LPV model by introducing a new scheduling variable, i.e., health parameters estimated by the on-board adaptive model. The proposed adaptive model and controller are implemented to a high fidelity nonlinear aeroengine model. Simulation results demonstrate that the estimation error of health parameters and component degradation level of the proposed method is 0.0763%, which gives an order of magnitude higher health parameter and component degradation level estimation accuracy compared with the other commonly applied models. The proposed model-based adaptive LPV gain scheduling control scheme has a steady-state error of 0.0028%, gives one order of magnitude higher control accuracy than that of three state-of-the-art baseline controllers while demonstrating significantly reduced overshoot and adjustment time. This verifies the robustness and effectiveness of the proposed controller, and verifies that the model can meet the technical requirements of the engine control system and significantly improve the engine control performance.

Hard Constrained LPV Virtual Control with Application to Flutter Suppression of a Smart Airfoil

Hard constrained control problems are popular in practical applications due to physical and power limitations. For instance, the displacement of a linear actuator is finite. A lot of studies have been conducted in this area to deal with certain hard constrained control problems and some are computationally expensive. This paper introduces a novel LPV (linear parameter-varying) virtual control scheme to deal with a class of hard constrained control problems with an application to flutter suppression of a smart airfoil, leading to a state-feedback LPV gain scheduling controller with the guaranteed $${{\cal H}_\infty}$$ performance. The basic idea of LPV virtual control is to add virtual components (such as variable stiffness springs and dampers) near to the hard constraints to prevent actuators from reaching their limits. The LPV virtual controller will be designed based on the model with virtual components and in the implementation stage, these virtual dynamics becomes part of the gain-scheduling controller. The concept is validated by a smart airfoil example. In the smart airfoil example, the virtual varying springs and dampers are placed at both ends of groove to constrain the mass movement. Comparisons studies with conventional LPV hard constrained control, nonlinear control, and regular LPV control without considering hard constraints are conducted to assess the performance of the proposed method and showed advantage over the existing methods. For instance, the control mass $${{\cal L}_2}$$ norm is reduced by 77.5% over the nonlinear control and 35% over the conventional LPV control.

A Gain-Scheduled LPV Control for Oxygen Stoichiometry Regulation in PEM Fuel Cell Systems

This paper addresses the linear parameter varying (LPV) control of a polymer electrolyte membrane fuel cell (PEMFC). To optimize efficiency, PEMFCs require reliable control systems ensuring stability and performance, as well as robustness to model uncertainties and external perturbations. On the other hand, PEMFCs present a highly nonlinear behavior that demands nonlinear and/or adaptive control strategies to achieve high performance in the entire operating range. Here, an LPV gain scheduled control is proposed. The control is based on a piecewise affine LPV representation of the PEMFC, a model that can be available in practice. To deal with the saturation of the control action, an LPV antiwindup compensation is also proposed. The complete control strategy is applied to several experimental practical situations in a laboratory fuel cell system to evaluate its performance and the reliability of the proposed algorithms.

Discrete-time Switching MIMO LPV Gain-scheduling Control for the Reduction of Engine-induced Vibrations in Vehicles

The work of this paper is an extension of the previous work realized by the authors. The main objective of this work is the reduction of the engine-induced vibrations in vehicles using a switching strategy to augment the controller range of actuation. Two inertia mass actuators and two accelerometers are attached to the engine mounts. Multiple input multiple output (MIMO) linear parameter-varying (LPV) controllers are designed for the reduction of the engine-induced vibrations and validated experimentally with test drives in a Golf VI Variant. The stability of the controllers is guaranteed since LPV gain-scheduling techniques are used. The MIMO LPV controllers achieved excellent results for the reduction of nine frequency components of the engine-induced vibration using only two gain-scheduling parameters for a range of 750 rpm.

Interaction control of an industrial manipulator using LPV techniques

This paper presents the design and implementation of a hybrid force/motion control scheme on a six-degrees-of-freedom robotic manipulator employing a gain-scheduled linear parameter-varying (LPV) controller. A nonlinear dynamic model of the manipulator is obtained and the unknown parameters are estimated. The manipulator is decomposed into an inner and a wrist submodel, and a practical way is proposed to investigate the coupling between them. The motion control part of the hybrid controller which is the main focus of this paper is formed by a combination of an LPV controller and a model-based inverse dynamics controller for the inner submodel and the wrist joints, respectively. A quasi-LPV model with a reduced number of scheduling parameters is derived for the inner submodel, and a polytopic LPV gain-scheduled controller is synthesized in a two-degrees-of-freedom structure including feedback and feedforward parts, which is augmented by a friction compensation term. A PD controller with a feedforward path is designed to control the interaction force. The proposed hybrid force/motion scheme is implemented on the 6-DOF CRS A465 robotic manipulator to perform a writing task. Comparison of the results with those of a hybrid force/motion controller with a plain model-based inverse dynamics motion control and the same force control shows that the proposed controller improves the position tracking performance significantly.

Adaptive Reduced-Order-Model-Based Control-Law Design for Active Flutter Suppression

The design of classic active flutter controllers has often been based on low-fidelity and low-accuracy linear aerodynamic models. Most of these models were usually treated as a linear time-invariant system, without considering time-varying parameters, such as the Mach number, the angle of attack, the Reynolds numbers, etc. A high-fidelity reduced-order model based on the proper orthogonal decomposition adaptation algorithm is used to develop a new general linear parameter-varying aeroservoelastic model with aerodynamic nonlinearity. A robust gain-scheduling control-law design method for active flutter suppression based on the proposed linear parametervarying model is investigated. The proposed design method is demonstrated with the Goland wing aeroelastic model. The simulation results show that the linear parameter-varying gain-scheduled controller can effectively suppress flutter over a range of airspeeds, and the flutter boundary in the transonic regime is simultaneously increased by nearly 20% to 30%.

Low-complexity linear parameter-varying modeling and control of a robotic manipulator

In this paper, a practical procedure for linear parameter-varying (LPV) modeling and identification of a robotic manipulator is presented, which leads to a successful experimental implementation of an LPV gain-scheduled controller. A nonlinear dynamic model of a two-degrees-of-freedom manipulator containing all important terms is obtained and unknown parameters which are required to construct an LPV model are identified. An important tool for obtaining a model of complexity low enough to be suitable for controller synthesis is the principle-component-analysis-based technique of parameter set mapping. Since the resulting quasi-LPV model has a large number of affine scheduling parameters and a large overbounding, parameter set mapping is used to reduce conservatism and complexity in controller design by finding tighter parameter regions with fewer scheduling parameters. A sufficient a posteriori condition is derived to assess the stability of the resulting closed-loop system. To evaluate the applicability and efficiency of the approximated model, a polytopic LPV gain-scheduled controller is synthesized and implemented experimentally on an industrial robot for a trajectory tracking task. The experimental results illustrate that the designed LPV controller outperforms an independent joint PD controller in terms of tracking performance and achieves a slightly better accuracy than a model-based inverse dynamics controller, while having a simpler structure. Moreover, it is shown that the LPV controller is more robust against dynamic parameter uncertainty.

LPV gain-scheduled control of SCR aftertreatment systems

Hydrocarbons, carbon monoxide and some of other polluting emissions produced by diesel engines are usually lower than those produced by gasoline engines. While great strides have been made in the exhaust aftertreatment of vehicular pollutants, the elimination of nitrogen oxide (NO x ) from diesel vehicles is still a challenge. The primary reason is that diesel combustion is a fuel-lean process, and hence there is significant unreacted oxygen in the exhaust. Selective catalytic reduction (SCR) is a well-developed technology for power plants and has been recently employed for reducing NO x emissions from automotive sources and in particular, heavy-duty diesel engines. In this article, we develop a linear parameter-varying (LPV) feedforward/feedback control design method for the SCR aftertreatment system to decrease NO x emissions while keeping ammonia slippage to a desired low level downstream the catalyst. The performance of the closed-loop system obtained from the interconnection of the SCR system and the output feedback LPV control strategy is then compared with other control design methods including sliding mode, and observer-based static state-feedback parameter-varying control. To reduce the computational complexity involved in the control design process, the number of LPV parameters in the developed quasi-LPV (qLPV) model is reduced by applying the principal component analysis technique. An LPV feedback/feedforward controller is then designed for the qLPV model with reduced number of scheduling parameters. The designed full-order controller is further simplified to a first-order transfer function with a parameter-varying gain and pole. Finally, simulation results using both a low-order model and a high-fidelity and high-order model of SCR reactions in GT-POWER interfaced with MATLAB/SIMULINK illustrate the high NO x conversion efficiency of the closed-loop SCR system using the proposed parameter-varying control law.

LPV gain-scheduling controller design for a non-linear quarter-vehicle active suspension system

There always exists a conflict between ride comfort and suspension deflection performances during the vibration control of suspension systems. Active suspension control systems, which are designed by linear methods, can only serve as a trade-off between these conflicting performance criteria. Both performance objectives can only be accomplished at the same time by using a nonlinear controller. This paper addresses the non-linear induced L2 control of an active suspension system, which contains non-linear spring and damper elements. The design method is based on the linear parameter varying (LPV) model of the system. The proposed method utilizes the bilinear damping characteristic, stiffening spring characteristic when the suspension deflection approaches the structural limits, mass variations and parameter-dependent weighting filters. Simulation studies both in time and frequency domain demonstrate that the active suspension system controlled by the proposed method always guarantees an agreement between acceleration (comfort) and suspension deflection magnitudes together with a high ride performance.

Design, Flight Simulation, and Handling Qualities Evaluation of an LPV Gain-Scheduled Helicopter Flight Control System

This paper summarises a recent project involving the design, piloted simulation, and handling qualities evaluation of a linear parameter varying (LPV) gain-scheduled helicopter flight control law. The work was jointly carried out by the Control Systems Research group of Leicester University and the Advanced Engineering group of GKN Westland Helicopters, Yeovil, England. The controller design was based on a Lynx MK7 helicopter model and used the quadratic H ∞ performance methodology with a 2-degrees-of-freedom controller to achieve robustness and good handling qualities across a large region of operation. In an off-line computer analysis, the controller consistently achieved Levell Cooper–Harper handling qualities ratings. A series offlight simulations were performed by two test pilots on GKN Westland's Advanced Engineering Simulation Facility. Both pilots consistently awarded Level 1 handling qualities ratings for the LPV gain-scheduled controller. The paper is tutorial in nature, it presents a novel application, and it demonstrates the potential of LPV methods for designing practical gain-scheduled controllers.