Active Control Law Research Articles

Vibration suppression of a pneumatic drive flexible manipulator using adaptive phase adjusting controller

Generally, a low damping system has a small phase margin. One should deal with the problems of long time delay and unmodeled dynamics for active vibration control of a low damping system sometimes. For a pneumatic drive flexible manipulator system, time delay or phase lag and unmodeled nonlinear dynamics problems have raised the level of concern. It is vital but difficult to regulate the phase of vibration controller, to compensate for the time-varying phase lag or time delay due to the gas compression and parametric uncertainty of the pneumatic control system. The objective of this investigation is to formulate an active vibration control law with practical approach of phase adjusting for a pneumatic drive flexible manipulator system. A control strategy named adaptive phase adjusting control is designed and applied. The adaptive phase adjusting controller is implemented by using the phase angles as explicit parameters. These parameters are tuned online, depending on the corresponding control performance index. To evaluate the control performance of the designed adaptive phase adjusting technique, experiments are conducted on a pneumatic drive flexible manipulator experimental setup. The experimental results demonstrate the adaptive phase adjusting controller has the ability to optimize the phase angle accordingly. Vibration suppression is accomplished by using the designed adaptive phase adjusting controller, even with time delay and unmodeled dynamics in the system.

A Novel Concise Adaptive Neural Control for a Class of Nonlinear MIMO Systems with Unknown Time Delays

This note makes effort at the problem of robust adaptive control for a class of nonlinear MIMO systems with unknown time-delays. A concise adaptive neural control scheme is developed by using the Backstepping method, the Lyapunov-Krasovskii functional and a novel “MLN” technique. Unlike the existing literatures, the actual control laws are only composed of the state variables, the reference signals and their derivatives, independent of the designed virtual controls and the other intermediate variables. In addition, only n (the number of system outputs) neural networks are introduced to compensate the nonlinear uncertainties of the whole system. Thus, the outstanding advantage of the proposed scheme is that the control law with a concise structure is model-independent and easy to implement in practical engineering, due to less computational burden. The corresponding scheme guarantees uniform ultimate boundedness of all the signals in the closed-loop system, and the tracking errors can converge to a arbitrary small neighborhood of zero. Finally, a simulation example illustrates the effectiveness of the proposed scheme.

Baseline vibration attenuation in helicopters: robust MIMO-HHC control

Active control techniques aimed at reducing helicopter vibrations have been extensively studied in the last few decades. The most studied control law is the so-called T-matrix algorithm; its implementation requires knowledge of the frequency response relating the control input to the output measurements at the disturbance frequency, which is very hard to characterise analytically. Adaptive schemes have been employed in literature to handle this problem. Surprisingly, however, very little effort has been devoted to the analysis of the T-matrix algorithm and in particular to the trade-off between robustness and adaptation in its deployment. In this paper an H∞ approach to the design of a robust T-matrix algorithm is proposed, with the aim of developing a systematic approach to the design of active vibration control laws for helicopters.

Control system design of mechanical systems with time varying mechanical and control parameters

A new control framework of motion and/or vibration control system design for mechanical systems with a time varying mechanical and control parameters is studied. As examples of such time varying mechanical and control parameters, we can assume a variable damping or stiffness parameter of semi-active control devices and a time varying weighting function in a generalized plant respectively. The mechanical system in the present study is assumed to have also an actuator for active motion and/or vibration control. The active control law to drive the actuator is obtained by a gain-scheduling controller based on linear matrix inequalities (LMIs) so that the closed-loop system is stable for all assumed values of the time varying mechanical and control parameters in the generalized plant. We use the adjustability of the time varying mechanical and control parameters in the closed-loop system to realize given control specifications. As the control law of the time varying parameters in the closed-loop system, a multi-layered feedforward artificial neural network (ANN) is designed as a dynamic map from available signals in the control system to time varying mechanical and control parameters. Design parameters of the ANN are optimized with a genetic algorithm (GA). With a design example of an active positioning and vibration control of a mechanical system with variable damping coefficient, the proposed design approach is shown to be capable of achieving highly sophisticated control specifications that are hard to be satisfied by conventional control methods.

Adaptive Output Feedback Control Using Fault Compensation and Fault Estimation for Linear System with Actuator Failure

The problem of linear systems subject to actuator faults (outage, loss of effectiveness and stuck), parameter uncertainties and external disturbances is considered. An active fault compensation control law is designed which utilizes compensation in such a way that uncertainties, disturbances and the occurrence of actuator faults are account for. The main idea is designing a robust adaptive output feedback controller by automatically compensating the fault dynamics to render the close-loop stability. According to the information from the adaptive mechanism, the updating control law is derived such that all the parameters of the unknown input signal are bounded. Furthermore, a disturbance decoupled fault reconstruction scheme is presented to evaluate the severity of the fault and to indicate how fault accommodation should be implemented. The advantage of fault compensation is that the dynamics caused by faults can be accommodated online. The proposed design method is illustrated on a rocket fairing structural-acoustic model.

Fault‐tolerant control design for uncertain Takagi–Sugeno systems by trajectory tracking: a descriptor approach

This study considers the problem of fault-tolerant control (FTC) by trajectory tracking for uncertain non-linear system described by Takagi–Sugeno models. The considered faults are constant, exponential or polynomial. The provided results are easily formulated in terms of linear matrix inequalities by employing the descriptor redundancy property. This latter introduces ‘virtual’ dynamics both in the active FTC control law scheme and in the output error allowing to decouple the gains of the active FTC controller, the observer gain matrices and the system ones. Numerical examples are given to illustrate the efficiency of the proposed approach.

Development of robust control law for active buffeting load alleviation of smart fin structures

Aerodynamic buffeting load can lead to premature fatigue damage of aircraft vertical fin structures. This article presents a robust control law development strategy for active buffeting load alleviation of a smart fin structure. The impact of aerodynamic loads on the modeling uncertainties of the smart fin was investigated through extensive wind tunnel tests. Test results revealed that the airflow introduced higher damping ratio and caused frequency shift to the vibration modes. These aerodynamic effects may adversely affect the performance and robustness of active control laws. Based on the observations, the structured singular value synthesis technique was used to develop a robust control law for the smart fin using a truncated baseline dynamic model. A parametric uncertainty block was introduced to account for the changes in the modal parameters of the baseline dynamic model due to the aerodynamic effects. An additive uncertainty block was included to account for the unmodeled higher-order vibration modes as well as the modeling errors in the low frequency range. The robust performance of the control law was demonstrated through simulations as well as extensive closed-loop control experiments in the wind tunnel using various free airstreams and vortical airflows. This provided a verified control law design strategy for active buffeting alleviation applications.

Output‐feedback stabilisation control for a class of under‐actuated mechanical systems

Output-feedback control of general underactuated mechanical systems is currently considered a major open problem. This study is focused on the output-feedback stabilisation control problems for a special class of underactuated mechanical systems, which appear in robotics and aerospace applications. For the synthesis of controller, first, the considered underactuated mechanical system is explicitly transformed into two cascade connected subsystems, and then an auxiliary filter-based virtual stabilisation controller is developed to locally asymptotically stabilise the first subsystem. Further, the designed virtual controller is again involved into the second subsystem using backstepping procedure to construct the actual control law, in which a series of auxiliary time-varying first-order low-pass filters are also implemented to avoid using the derivative of the system non-linear functions. Moreover, in the second step, finite-time observer technique is utilised to precisely reconstruct the immeasurable states to achieve the finite-time stabilisation control in the sense of output feedback. Lyapunov analysis shows the local asymptotic stability of the closed-loop system through the cascade system stability criteria. Simulation results are presented by using two benchmark non-linear underactuated mechanical systems to demonstrate the feasibility and the effectiveness of the proposed controller.

Fuzzy Logic Control of Double-Fed Induction Generator Wind Turbine

This paper presents fuzzy logic control of Doubly Fed Induction Generator (DFIG) wind turbine in a sample power system. First, a mathematical model of the doubly fed induction generator written in an appropriate d-q reference frame is established to investigate simulations. In order to control the rotor currents of DFIG, a power active and reactive control law is synthesized using PI controllers. Then, the performances of fuzzy logic controller (FLC) are investigated and compared to those obtained from the PI controller. Results obtained in Matlab/Simulink environment show that the FLC is more robust, prove excellent performance for the control unit by improving power quality and stability of wind turbine.

Mixed H2/H∞ robust model predictive control with saturated inputs

In this paper, we investigate the mixed H2/H∞ robust model predictive control (RMPC) for polytopic uncertain systems, which refers to the infinite horizon optimal guaranteed cost control (OGCC). To fully use the capability of actuators, we adopt a saturating feedback control law as the control strategy of RMPC. As the saturating feedback control law can be effectively represented by the convex hull of a group of auxiliary linear feedback laws, the auxiliary feedback laws allow us to design the actual feedback control law without consideration of the input constraints directly to achieve the improved performance. Moreover, we suggest the relative weights on the actual and auxiliary feedback laws to the RMPC, which in turn improves the closed-loop system performance. Furthermore, an off-line design of the proposed RMPC is also developed to make it more practical. Numerical studies demonstrate the effectiveness of the proposed algorithm.

Scalable Stabilization of Large Scale Discrete-time Linear Systems via the 1-norm

This article reconsiders the stabilizing controller synthesis problem for discrete–time linear systems with a focus on systems of large scale. In this case, existing solutions are either not scalable, and thus, not tractable, or conservative. This motivates us to exploit finite–time control Lyapunov functions (CLFs), i.e., a relaxation of the standard CLF concept, to obtain a nonconservative and scalable synthesis method. The main idea is to employ Minkowski functions of a particular family of polytopic sets, which includes the hyper–rhombus induced by the 1–norm, as candidate finite–time CLFs. This choice results in explicit periodic vertex–interpolation control laws, which are globally stabilizing. The vertex–control laws can be computed offline using distributed optimization, in a scalable fashion, while the actual control law comes in an explicit, distributed form. Large scale illustrative examples demonstrate the effectiveness of the proposed approach.

Stable Direct Adaptive Neural Control of an Electrically Driven Dual-Axis Motion Platform Using Backstepping Technique

Abstract In this paper, the adaptive backstepping neural control (ABNC) is applied to an electrically driven dual-axis motion platform. The Dynamic model of the electrically driven dual-axis motion is obtained by coupling the dynamics of the dual-axis motion platform with the actuators (DC motors) dynamics. Thus, it is more realistic to select the actuators input voltages to be the control inputs instead of input torques, unlike the case when the actuators dynamics are not included. Unlike the existing ABNC techniques, single hidden layer feedforward neural networks with additive hidden nodes (SLFNN) are used to approximate the unknown nonlinear functions in the actual and virtual control laws where the networks parameters are adjusted based on extreme learning machine (ELM) algorithm. In ELM-based SLFNN, the hidden layer parameters are randomly selected and only the output layer weights linking the hidden layer with the output layer are needed to be updated. The adaptive update laws for the output layer weights are derived based on Lyapounov stability theory for guaranteeing semi-global boundedness of all signals in the closed-loop system. The simulation study illustrates the effectiveness of the proposed controller and shows that the system outputs track the desired trajectories with small tracking errors.

Design and Implementation of a Dual-Redundant Flight Control System for a Miniature Autonomous Helicopter

This paper present the design and implementation of a dual-redundant flight control system for a miniature autonomous helicopter, named SRD-Heli. The systematic design procedure includes hardware integration, flight control law, exchange scheme, as well as experimental evaluation. A dual-redundant control architecture is adopted to construct the flight control system for the helicopter. The control architecture is composed by (1) an active flight control law designed for the active computer, which works in normal condition, (2) a standby flight control law to effectively control the helicopter by standby computer in fault condition. The result of actual flight tests shows that the flight control system is capable of achieving the desired performance.

D19 目標制御出力に着目したセミアクティブ振動制御(M-OS2-3 セミアクティブ制御,M-OS2 建築・建設構造物の耐震・免震・制振,「海を越え,国を越え,世代を超えて!」)

As a method for semi-active control of structural systems, the active-control-based method that emulates the control force of a targeted active control law by semi-active control devices has been studied. In the active-control-based method, the semi-active control devices are not necessarily able to generate the targeted active control force because of the dissipative nature of those devices. In such a situation, the meaning of the targeted active control law becomes unclear in the sense of the control performance achieved by the resulting semi-active control system. In this study, a new semi-active control strategy that approximates the control output (not the control force) of the targeted active control is proposed. The variable parameter of the semi-active control device is selected at every time instant so that the predicted control output of the semi-active control system becomes close to the corresponding predicted control output of the targeted active control as much as possible. Parameters of the targeted active control law are optimized in the premise of the above "output emulation" strategy so that the control performance of the semi-active control becomes good and the "error" of the achieved control performance between the targeted active control and the semi-active control becomes small.

Single neural network approximation based adaptive control for a class of uncertain strict-feedback nonlinear systems

A new adaptive control design approach is presented for a class of uncertain strict-feedback nonlinear systems. In the controller design process, all unknown functions at intermediate steps are passed down, and only one neural network is used to approximate the lumped unknown function of the system at the last step. By this approach, the designed controller contains only one actual control law and one adaptive law, and can be given directly. Compared with existing methods, the structure of the designed controller is simpler and the computational burden is lighter. Stability analysis shows that all the closed-loop system signals are uniformly ultimately bounded, and the steady state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach.

Adaptive control based on single neural network approximation for non-linear pure-feedback systems

In this study, a single neural network (SNN)-based adaptive control design method is developed for a class of uncertain non-affine pure-feedback non-linear systems. Different from existing methods, all unknown parts at intermediate steps are passed down, and only an SNN is used to approximate the lumped unknown function of the system at the last step of controller design. By this approach, the designed controller consisting of an actual control law and an adaptive law can be given directly, and the complexity growing problem inherent in conventional methods can be completely eliminated. Stability analysis shows that all the closed-loop system signals are uniformly ultimately bounded, and the steady-state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation results demonstrate the effectiveness of the proposed approach.

Active flutter suppression control law design method based on balanced proper orthogonal decomposition reduced order model

Active control for nonlinear aeroelastic structures is an attractive innovative technology. The design of classic active flutter controllers has often been based on low-fidelity and low-accuracy linear aerodynamic models. Multi-physics high-fidelity reduced order model (ROM) was used to design active control laws. In order to provide a lower-order model for controllers design, a balanced proper orthogonal decomposition ROM (POD-BT/ROM) was investigated. A state-space aeroservoelastic model and the active flutter suppression control law design method based on POD-BT/ROM were proposed. The effectiveness of the proposed method was then demonstrated by NACA 0012 airfoil, AGARD 445.6 wing and the Goland wing+ aeroelastic model.

Active control of sound transmission through a stiffened panel using a hybrid control strategy

This article presents active control laws for sound transmission through a stiffened panel in the low-frequency range. A stiffened panel mounted on a metallic box is used to simulate aircraft fuselage and cabin system. A loudspeaker located outside the box is driven by band-limited white noise to exert acoustic excitation. Dynamic properties of the stiffened panel are characterized through a series of modal testing. Sound isolation performance is evaluated both analytically and experimentally for further efficient control algorithms design. Based on the analysis of these results, a hybrid control strategy combining both feedback and feedforward control is proposed, which utilizes a high-authority/low-authority control architecture. The low-authority control loop is positive position feedback control and high-authority control loop is filtered-X control. When positive position feedback is implemented, active damping is added on the secondary path, which makes filtered-X least mean square more robust, accelerates convergence rates, and reduces residue errors. In the end, the effectiveness of the hybrid control is verified through a serious of closed-loop control experiments. These results demonstrate that sound pressure level could be lowered by a maximum of 15.9 dB at the first resonance frequency. In addition, the overall sound reduction in the cabin is more than 7 dB.

Active Collision Avoidance Control for Fractionated Satellite Clusters in Ellipse Orbit

The active collision avoidance maneuver of fractionated satellite cluster is studied. Especially, two clusters with different structure configuration in ellipse orbit and the collision description method between them are discussed. Due to the limitation of the collision probability based on the minimum distance, a new method is developed which including the effect of relative velocity on collision. Only J2 perturbation is considered in the relative dynamics and a PD controller based on BP neural network using automatic differentiation method is adopted when a collision is imminent. The simulation results validate that the presented active collision avoidance control law is effective.

Nonlinear Fault-Tolerant Control of a Quadrotor UAV Based on Sliding Mode Control Technique

In this paper, a sliding mode based fault-tolerant control (SM-FTC) is designed, implemented and tested in a quadrotor unmanned small helicopter under the propeller damage and actuator fault conditions. Based on the concept of sliding mode control, both passive and active fault-tolerant control laws have been designed and experimentally testbed on a quadrotor UAV (unmanned aerial vehicle) testbed available at Concordia University. These two types of controllers are carried out and compared through both theoretical analyses and experimental tests on the quadrotor UAV system.