Linear Quadratic Control Design Research Articles

A Linear Quadratic Regulator-Based Controller Design for Ensuring Attack Resilient Operations of Power Networks

Abstract This paper proposes a linear quadratic regulator-based control scheme to ensure the stability of power grids against cyber-attacks. The proposed control scheme is developed for the synchronous generator in a power system having a single generator. In this work, the nominal system represented by a linear state-space model is augmented by considering the impact of cyber-attacks and the Riccati equation for the augmented system is solved in such a way the attack is completely eliminated. The attack is considered on the actuator, i.e., within the dynamical model of the synchronous generator in a power grid. Simulation studies are conducted under different attacks on the actuator and results under all attack scenarios ensure attack resilient operations of power systems.

Gender-Based Eye Movement Differences in the Ilab-X Platform Using: An Eye-Tracking Study

Continuous fluidized bed spray granulation processes are known to be prone to non-linear oscillations, which requires a stabilizing controller design. In this article, a purely data-driven Koopman-based system linearization and control is proposed, which does not need any prior model knowledge and uses only systems input-output measurements. The linearization achieved by the Koopman embedding allows for linear-quadratic control design, which does not depend on the operating point. The proposed method was validated in a simulation study of a fluidized bed granulation process and was able to stabilize the particle size distribution in different operating points.

Koopman-based data-driven control for continuous fluidized bed spray granulation with screen-mill-cycle

Continuous fluidized bed spray granulation processes with external screen-mill cycle are known to be susceptible to nonlinear oscillations. As has been shown recently, mass control may induce periodical oscillation of the particle size distribution in fine grinding regimes, which requires the design of additional stabilizing control. In this contribution a data-driven control approach is investigated. It is based on the identification of the Koopman-based linear representation of the system and does not require any manual system modeling as it is based purely on input–output measurements. The resulting representation makes possible a linear–quadratic control design, that does not depend on a specific operational point. The proposed approach was successfully validated in a numerical study.

Two Phase Linear Quadratic Gaussian Controller Design for Launch Vehicle Autopilot

The achievable payload of a launch vehicle is sensitive to the mass of the terminal stage which injects the payload into orbit, as well as to tracking errors introduced by the autopilot. The system is usually characterized by lightly damped slosh modes with uncertain parameters, whose stabilization is critical, while maintaining the overall performance of the system. Passive slosh stabilization involves introduction of mechanical baffles into the fluid tanks, which impacts the achievable payload mass. Optimal tracking performance along with active slosh stabilization is hence an ideal combination of autopilot design objectives. While optimal control methods are attractive for such systems, the lack of physical insight in the design procedure often limits the usefulness of these methods for launch vehicle autopilot design. A major challenge in stabilizing lightly damped slosh is the necessity to cater to very large mode magnitudes and phase swings exhibited by the system over the range of parameter perturbations. The significant contribution of this paper is the development of a two phase Linear Quadratic Gaussian approach, utilizing the physical insight into the system performance for tuning the weights in the performance index, to satisfy the time domain as well as frequency domain objectives. A robust design which exhibits superior time domain as well as frequency domain performance as compared to the benchmark classical controller is obtained, with near perfect phase stabilization of the lightly damped slosh dynamics.

Koopman-based data-driven control for continuous fluidized bed spray granulation

Abstract Continuous fluidized bed spray granulation processes are known to be prone to non-linear oscillations, which requires a stabilizing controller design. In this article, a purely data-driven Koopman-based system linearization and control is proposed, which does not need any prior model knowledge and uses only systems input-output measurements. The linearization achieved by the Koopman embedding allows for linear-quadratic control design, which does not depend on the operating point. The proposed method was validated in a simulation study of a fluidized bed granulation process and was able to stabilize the particle size distribution in different operating points.

Linear quadratic gaussian control design with extended KALMAN filter

Linear quadratic gaussian control design with extended KALMAN filter

상태공간 모델링에 의한 오일쿨러의 Robust LQG 제어기 설계

상태공간 모델링에 의한 오일쿨러의 Robust LQG 제어기 설계

Adaptive Actuator Failure Compensation for Possibly Nonminimum-Phase Systems Using Control Separation Based LQ Design

This paper develops an adaptive actuator failure compensation control scheme for discrete-time and possibly nonminimum-phase systems with unknown actuator failures of characterizations that first, some unknown system inputs are stuck at some unknown fixed or varying values at unknown time instants, and second, the actuator failures as system disturbances are not matched by other inputs. Such a failure compensation control problem is solved by using a new control separation design based on a new linear quadratic (LQ) control formulation: One control component is used for disturbance rejection and actuator failure compensation, and one component is used for closed-loop stability and regulation, to achieve the desired control objective. The nominal LQ control solution uses a new cost function to derive the two-component control signal, which has improved performance over a traditional LQ control design. The adaptive actuator failure compensation control problem is solved using a stable adaptive law for parameter estimation, to guarantee the system stability and output regulation performances in the presence of plant and actuator failure uncertainties. Simulation results are presented to verify the satisfactory performance of the proposed adaptive LQ control scheme.

Modeling and Control of a Diesel Engine With Regenerative Hydraulic-Assisted Turbocharger

Diesel engines are of great challenges due to stringent emission and fuel economy requirements. Compared with the conventional turbocharger system, regenerative assisted system provides additional degrees-of-freedom for turbocharger speed control. Hence, it significantly improves control capability for exhaust-gas-recirculation (EGR) and boost pressure. This paper focuses on modeling and control of a diesel engine air-path system equipped with an EGR subsystem and a variable geometry turbocharger (VGT) coupled with a regenerative hydraulic-assisted turbocharger (RHAT). The challenges lie in the inherent coupling among EGR, turbocharger performance, and high nonlinearity of the engine air-path system. A control-oriented nonlinear RHAT system model is developed; and a linear quadratic (LQ) control design approach is proposed in this paper to regulate the EGR mass flow rate and boost pressure simultaneously and the resulting closed-loop system performance can be tuned by properly selecting the LQ control weighting matrices. Multiple LQ controllers with integral action are designed based on the linearized system models over a gridded engine operational map and the final gain-scheduling controller for a given engine operational condition is obtained by interpreting the neighboring LQ controllers. The gain-scheduling LQ controllers for both traditional VGT-EGR and VGT-EGR-RHAT systems are compared with the in-house baseline controller, consisting of two single-input and single-output (SISO) controllers, against the nonlinear plant. The simulation results show that the designed multi-input and multi-output LQ gain-scheduling controller is able to manage the performance trade-offs between EGR mass flow and boost pressure tracking. With the additional assisted and regenerative power available on the turbocharger shaft for the RHAT system, engine transient boost pressure performance can be significantly improved without compromising the EGR tracking performance, compared with the baseline control.

Reduced‐order simultaneous state and fault estimator based fault tolerant preview control for discrete‐time linear time‐invariant systems

In this study, an integrated fault tolerant preview tracking control framework is proposed based on reduced-order simultaneous state and fault estimation, robust preview control and fault signal compensation. In general, this work consists of three key design parts. Firstly, the analysis and synthesis conditions of a novel reduced-order simultaneous state and fault estimator are given based on the equivalent model reconstruction and optimisation of the parametric solution. Note that such a reduced-order design method can avoid traditional output equation reduction requirement, and has a wider application scope. Secondly, the robust preview tracking control policy is constructed by integrating state-feedback, preview action, and integral operation. Relying on augmentation modelling technique, a linear quadratic preview control design problem is transformed into an equivalent augmented state-feedback control design problem. Thirdly, the fault compensation is achieved by using estimated fault to accommodate fault influence. These involved designs are performed at the H ∞ optimisation level, and thus guarantee the robust tracking performance of closed-loop systems. The effectiveness of the above conclusions is finally verified via two case studies.

A Design of a Hybrid Non-Linear Control Algorithm

One of the high energy consuming devices in the buildings is the air-conditioning system. Designing a proper controller to consider the thermal comfort and simultaneously control the energy usage of the device will impact on the system energy efficiency and its performance. The aim of this study was to design a Multiple-Input and Multiple-Output (MIMO), non-linear, and intelligent controller on direct expansion air-conditioning system The control algorithm uses the Fuzzy Cognitive Map method as a main controller and the Generalized Predictive Control method is used for assigning the initial weights of the main controller. The results of the proposed controller shows that the controller was successfully designed and works in set point tracking and under disturbance rejection tests. The obtained results of the Generalized Predictive Control-Fuzzy Cognitive Map controller are compared with the previous MIMO Linear Quadratic Gaussian control design on the same direct expansion air-conditioning system under the same conditions. The comparative results indicate energy savings would be achieved with the proposed controller with long-term usage. Energy efficiency and thermal comfort conditions are achieved by the proposed controller.

An improved approach for H∞ design of linear quadratic tracking control for chemical processes with partial actuator failure

For chemical processes under partial actuator failure, a new H∞ design of linear quadratic (LQ) tracking control approach is proposed for robust tracking of set-point against actuator faults and unknown disturbance. In view of the disadvantages of traditional H∞ control that only take into account the output tracking error or the corresponding converted state variables into the controller design, a new state space model in which the process state variables and tracking error are combined and can be regulated separately is proposed first. Based on this model development, a new H∞ control strategy is proposed to yield improved tracking performance and disturbance rejection. The system performance robustness criterion against model/plant mismatch is further proposed to guarantee monotonicity conditions, which are sufficient for robust system stability. The case study on an injection velocity control further demonstrates the merits and effectiveness of the proposed method.

Optimal LQR Controller Design for Wing Rock Motion Control in Delta wing Aircraft

In this work, an Optimal Linear Quadratic (LQR) and optimal Minimax controller is proposed for Wing Rock Motion Control in Delta wing Aircraft model. The nonlinear Wing Rock Motion dynamics were considered. The LQR and Minimax controllers are designed in order to reduce the Wing Rock Motion. Simulations are performed in order to verify the ability of both controllers to reduce the wing rock motion in delta wing aircraft. Results were plotted together in order to show the difference of performance for both controllers for a comparative point of view.

New Minmax Linear Quadratic Fault-Tolerant Tracking Control for Batch Processes

A new design of linear quadratic control (LQ) is proposed for batch processes with partial actuator faults and unknown disturbance. The autoregressive model built through input-output process data is transformed into a new non-minimal state space (NMSS) model in which the process state variables and tracking error are combined, then a new LQ control scheme is derived based on minimax optimization. Unlike LQ based on traditional NMSS model (TNMSSLQ), the process state variables and tracking error can both be regulated in the proposed method. Monotonic situations of optimal performance cost are further derived to yield sufficient conditions for both nominal and robust system stability. Finally, an injection velocity control example is illustrated to show the effectiveness.

Decentralized Networked Control of Building Structures

AbstractThe article presents a new method for the design of decentralized networked switched controllers to mitigate the response of building structures under earthquakes. It consists of two phases. The first phase generates low‐order gain matrices based on the linear quadratic Gaussian (LQG) control design. It includes a substructural approach when the equations of motion of substructures are extracted from the equation of motion of the overall structures described by a finite element in‐plane (2‐D) model. Appropriate model reduction procedures, determination of damping as well as the selection of sensor and actuators locations and models are applied to each substructure. The sensors and actuators are implemented into the design model. Both resulting reduced‐order state space models of substructures are used for the proper LQG control design. The obtained local controllers are implemented into the overall structure to evaluate the performance of the closed‐loop system. Displacement, drift, acceleration, maximal actuator forces as well as dynamic responses on selected locations are checked. The computational originality is the method derived under the subsequent second phase, where the gain matrices computed in the first phase serve as a tool for the design of decentralized networked switched controller. The switching is realized periodically between two switched modes. Each mode corresponds with only one active local feedback loop for a certain period of time. The network parameter is the time interval of activity of each mode determined by a given protocol. Robustness of performance against packet dropouts and sensor faults is tested. A numerical example of the decentralized networked switched controller design applied on the 20‐story high‐fidelity building benchmark model is supplied. The simulation tests show the proposed method exhibits acceptable performance.

Improved infinite horizon LQ tracking control for injection molding process against partial actuator failures

Focusing on injection molding processes with partial actuator failures, a new design of infinite horizon linear quadratic control is introduced. A new state space process model is first derived through input–output process data. Furthermore, an improved infinite horizon linear quadratic control scheme, whereby the process state variables and tracking error can both be regulated separately, is proposed to show enhanced control performance against partial actuator failures and unknown disturbances. Under the circumstances of actuator faults, the closed-loop system is indeed a process with uncertain parameters. Hence, a sufficient condition is proposed to guarantee robust stability is presented using Lyapunov theory. The proposed concepts are illustrated in an injection velocity control case study to show the effectiveness.

Gain-scheduling control of a floating offshore wind turbine above rated wind speed

This paper presents an application of gain-scheduling (GS) control techniques to a floating offshore wind turbine on a barge platform for above rated wind speed cases. Special emphasis is placed on the dynamics variation of the wind turbine system caused by plant nonlinearity with respect to wind speed. The turbine system with the dynamics variation is represented by a linear parameter-varying (LPV) model, which is derived by interpolating linearized models at various operating wind speeds. To achieve control objectives of regulating power capture and minimizing platform motions, both linear quadratic regulator (LQR) GS and LPV GS controller design techniques are explored. The designed controllers are evaluated in simulations with the NREL 5MWwind turbine model, and compared with the baseline proportional-integral (PI) GS controller and non-GS controllers. The simulation results demonstrate the performance superiority of LQR GS and LPV GS controllers, as well as the performance trade-off between power regulation and platform movement reduction.

Artificial Bee Colony Algorithm Based Linear Quadratic Optimal Controller Design for a Nonlinear Inverted Pendulum

This paper presents a linear quadratic optimal controller design for a nonlinear inverted pendulum. Linear Quadratic Regulator (LQR), an optimal control method, is usually used for control of the dynamical systems. Main design parameters in LQR are the weighting matrices; however there is no relevant systematic techniques presented to choose these matrices. Generally, selecting weighting matrices is performed by trial and error method since there is no direct relation between weighting matrices and time domain specifications like overshoot percentage, settling time, and steady state error. Also it is time consuming and highly depends on designer’s experience. In this paper LQR is used to control an inverted pendulum as a nonlinear dynamical system and the Artificial Bee Colony (ABC) algorithm is used for selecting weighting matrices to overcome LQR design difficulties. The ABC algorithm is a swarm intelligence based optimization algorithm and it can be used for multivariable function optimization efficiently. The simulation results justify that the ABC algorithm is a very efficient way to determine LQR weighting matrices in comparison with trial and error method.

Design of state space linear quadratic tracking control using GA optimization for batch processes with partial actuator failure

This paper presents a new state space model design of linear quadratic (LQ) tracking control scheme using genetic algorithm (GA) optimization for batch processes under partial actuator faults. To develop the LQ control, the process model is first transformed into a new state space formulation that includes both the process state and the output tracking error dynamics. Then the subsequent LQ control is formulated. As analytical tuning rules of the elements in the weighting matrices are not known, a GA is introduced to optimize these elements to achieve the desired closed-loop process responses. Simulation on several processes demonstrates that the proposed LQ scheme obtains the design objective well with improved control performance.

Pre-Load Torque Responses for Flexibility in Single Link Manipulator

The Research article includes Model Torque Responses for flexibility of single link manipulators using Linearization Technique named as a LCQ. Manipulators are nothing but one type robotic arms commonly used in the industry .Here the controller design technique used for controlling manipulator was Linear Quadratic Controller Design. Generally the robotic arms are non-linear in nature. In order to control the non-linearity of manipulator and angular displacement, state space technique is used. The topology explains dynamic characteristics occurring in abnormal working condition torque responses are modified by using Linear Quadratic Controller Design Technique. The design method approach in state space control strategy. The dynamic torque responses of single link manipulator in abnormal condition and also using LCQ Technique are shown by M-File Controlling block of Simulink Library in Mat Lab Control tool box.