Adaptive Control of Structural Acoustics using Intelligent Structures with Embedded Piezoelectric Patches

This paper investigates the feasibility of adaptive control scheme for vibration suppression of flexible beam. The dynamic model of the flexible cantilever beam using finite element modeling is derived. Adaptive vibration controllers are good candidates for problems where parameters of a plant are unknown or there are uncertainties in a system. Parameters of adaptive controllers are adjusted on-line to achieve the best performance. Two adaptive vibration control schemes: adaptive feedforward control scheme using least mean square algorithm and model reference adaptive control algorithm are developed to suppress the vibration. Computer simulations are performed to verify the effectiveness of the proposed adaptive vibration control schemes and demonstrate their applications in vibration control.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Simultaneous adaptive positioning and vibration control of a flexible active composite manipulator with two piezoelectric patches and an active strut are investigated in this paper. First, the configuration of the manipulator is described, and then its dynamics is analyzed. An adaptive fuzzy logic control (AFLC) strategy and an independent modal space adaptive control (IMSAC) scheme are proposed for the active strut motion control and for the manipulator vibration control, respectively. In the AFLC, an adaptive input scaling concept is introduced to adaptively adjust the input of a traditional fuzzy logic controller. As a result, an inverse dead zone is gained to compensate the existing strut dead zone and overcome the dead zone effects. In the IMSAC, a novel adaptive feedback control scheme is developed using the adaptive filtered-x algorithm that is commonly used for adaptive feedforward control. Simulations and experiments demonstrate that the AFLC can provide very accurate positioning control with a relative steady-state positioning error of 0.06%, and that the IMSAC raises the damping ratios of the first two modes of the flexible manipulator 10 times and substantially suppresses the vibration induced by the manipulator motion. </para>

Data-driven model based adaptive feedback-feed forward control schemes for open cycle - OTEC process

An effort was made to design an anti-G suit valve controller based on ideal pressure profiles. A computer simulation of the cardiovascular model developed at Wright State University was used to obtain the optimal pressure profiles. The model developed by Jaron and Chu (1984) was used for this simulation. The simulation results show that an optimal duration for the pressure profiles that provides the maximum Gz tolerance exists. The results clearly contradict the belief that the faster the suit filling, the better the design and higher the G-tolerance. An anti-G suit valve controller using an adaptive feedforward control scheme was designed to produce the ideal pressure profiles. >

Recently, a class of adaptive control schemes called L1 Adaptive Control (L1-AC) has been proposed and widely advertised in aerospace control for achieving fast and robust adaptation and better performance than the existing Model Reference Adaptive Control (MRAC) Schemes. The L1-AC scheme is designed mainly for plants with full state measurement even though the name L1-AC has been used as an umbrella name for more general classes of plants. In this paper, we show that the L1-AC for plants with measured states is simply a standard MRAC with a low pass filter inserted in front of the control input. The analysis of the scheme is almost identical to that of MRAC as the same Lyapunov function is used to establish stability. The motivation for using the filter is the fact that for this class of adaptive schemes i.e. MRAC for plants with full state measurement the tracking error can be made arbitrarily small during transient by increasing the adaptive gain. A high adaptive gain however makes the differential equation of the adaptive law or estimator very stiff and leads to numerical problems that cause high oscillations in the estimated parameters leading to loss of adaptivity and deviations from what the theoretical properties dictate. The L1-AC approach mistook these numerical oscillations as properties of the adaptive scheme and inserted an input low pass filter in order to filter them out. While the filter helps reduce the frequency of these oscillations in the control law the price paid is high. First the numerical instability does not go away and the estimated parameters continue to oscillate without converging to the true parameters even in the presence of sufficiently rich signals. Second, due to the filter the tracking error is no longer guaranteed to converge to zero and the transient bounds for the tracking error also depend on the filter. As a result, the tracking properties of the L1-AC scheme are worse than what a simple MRAC scheme can generate with adaptive gains that could be high but away from the region of numerical instability. In addition, the presence of the filter reduces the robust stability margins in the presence of unmodeled dynamics and provides literally no advantage as simple robust MRAC techniques can solve the same problem achieving much better properties. The authors of L1-AC often compare the properties of a numerically unstable MRAC due to extremely high adaptive gains something that is prohibited by robust adaptive control with those of the filtered MRAC aka L1-AC to show that L1-AC performs better. Such comparisons are not only misleading but do not reveal what causes what giving the reader a false impression of a new theory that results to new performance and robustness improvements. The use of filters in adaptive control is not new and they are used to improve the performance and robustness of certain adaptive control schemes without destroying their ideal tracking properties. In this paper we present such a scheme that guarantees good transient performance and robustness and reveals the trade off between transient performance and robust stability.

This paper focuses on a frequency-weighted hybrid adaptive control with application tosimultaneous precision positioning and vibration suppression of smart composite structures.Following the introduction of the structure and materials system of an active compositepanel (ACP) with a surface-mounted and two embedded piezoelectric ceramic patches, thesensor selection for the purpose of precision positioning or vibration control is discussed,and the function assignment of a laser displacement sensor as well as the embeddedpiezoelectric sensor is determined for the ACP. The frequency-weighted hybrid adaptivecontrol approach is then developed. The approach consists of an adaptive feedforwardposition controller for precision positioning, an adaptive feedforward vibrationcontroller for vibration suppression as well as an adaptive feedback controller for bothprecision positioning and vibration suppression. The frequency-weighted filtersintroduced in the approach realize the signal fusion of the two sensors. Finally,experiments are also performed for harmonic disturbances at the first two naturalfrequencies of the ACP and a random disturbance for both cases of with/without theadaptive feedforward vibration controller, demonstrating that the frequency-weightedhybrid adaptive control approach can provide satisfactory precision positioningand vibration suppression in both cases, and that better position accuracy canbe achieved if the adaptive feedforward vibration controller is also employed.

In general, the power systems with high penetration of renewable energies result in a system with a relatively low moment of inertia, and can be susceptible to significant frequency variations. However, the virtual inertia of wind power which imitates the kinetic inertia of synchronous generator can be used to improve the system dynamic behavior. This paper presents a novel hybrid adaptive fuzzy control scheme that utilizes both the rotating mass of the DFIG and a super-capacitor bank as the virtual inertia sources for DFIG-Based wind energy applications to provide short-term frequency regulation support. First, in order to compensate the intermittent nature of the wind, a super-capacitor is connected to the DC link of the back-to-back converters and an additional adaptive fuzzy controller is added to the supper-capacitor system controller to realize the short-term frequency support. In a ddition, to reduce the cost of the supper-capacitor, by utilizing the rotating mass of the DFIG, an adaptive fuzzy controller is also designed according to the deviation and derivative of the grid frequency for the active power regulation of the rotor-side converter to provide the optimal virtual inertia. Furthermore, a coordination optimizer is designed to obtain an optimal trade-off between the energy output from the DFIG rotating mass and the energy from the super-capacitor. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed control strategy.

In this paper, a novel observer-based adaptive backstepping control scheme is proposed for time-varying formation tracking of multiple unmanned aerial vehicle (multi-UAV) system consisting of multiple nonlinear UAVs with unknown parameters and immeasurable states. The communication network among the UAVs is represented by a directed graph and a part of the UAVs cannot obtain the reference information directly. A state observer is designed to estimate the immeasurable states. An adaptive control scheme is developed by employing backstepping control strategy and the designed state observer. It is proved that the proposed scheme can guarantee that the UAVs maintain the formation shape while tracking the reference trajectory. Besides, all the signals of the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB). Simulation example is provided to show the effectiveness of the proposed scheme.

An adaptive control scheme is developed for the optimization of a fed-batch ethanol production process. The fermentation process is modeled by an hybrid neural model combining mass balance equations and neural networks, used to represent the kinetic rates. The networks used, the functional link networks (FLN), allow the linear estimation of their parameters; this enables the re-estimation of the parameters at each sampling time, and thus the development of an adaptive optimal control scheme.

Muscle weakness is the primary impairment causing mobility difficulty among stroke survivors. Millions of people are unable to live normally because of mobility difficulty every year. Strength training is an effective method to improve lower extremity ability but is limited by the shortage of medical staff. Thus, this paper proposes a robot-assisted active training (RAAT) by an adaptive admittance control scheme with virtual reality interaction (AACVRI). AACVRI consists of a stiffness variable admittance controller, an adaptive controller, and virtual reality (VR) interactions. In order to provide human-robot reality interactions corresponding to virtual scenes, an admittance control law with variable stiffness term was developed to define the mechanics property of the end effector. The adaptive controller improves tracking performances by compensating interaction forces and dynamics model deviations. A virtual training environment including action following, event feedback, and competition mechanism is utilized for improving boring training experience and engaging users to maintain active state in cycling training. To verify controller performances and the feasibility of RAAT, experiments were conducted with eight subjects. Admittance control provides desired variable interactions along the trajectory. The robot responds to different virtual events by changing admittance parameters according to trigger feedbacks. Adaptive control ensures tracking errors at a low level. Subjects were maintained in active state during this strength training. Their physiological signals significantly increased, and interaction forces were at a high level. RAAT is a feasible approach for lower limb strength training, and users can independently complete high-quality active strength training under RAAT.

The purpose of this chapter is to redesign the standard adaptive control schemes by using hybrid structure composed by Model Reference Adaptive Control (MRAC) or Adaptive Pole Placement Control (APPC) strategies, associated to Variable Structure (VS) schemes for achieving non-standard robust adaptive control strategies. The both control strategies is now on named VS-MRAC and VS-APPC. We start with the theoretical base of standard control strategies APPC and MRAC, discussing their structures, as how their parameters are identified by adaptive observers and their robustness properties for guaranteeing their stability. After that, we introduce the sliding mode control (variable structure) in each control scheme for simplifying their design procedure. These design procedure are based on stability analysis of each hybrid robust control scheme. With the definition of both hybrid control strategies, it is analyzed their behavior when controlling system plants with unmodeled disturbances and parameter variation. It is established how the adaptive laws compensates these unmodeled dynamics. Furthermore, by using simple systems examples it is realized a comparison study between the hybrid structures VS-APPC and VSMRAC and the standard schemes APPC and MRAC. As the hybrid structures use switching laws due to the sliding mode scheme, the effect of chattering is analyzed on the implementation and consequently effects on the digital control hardware where sampling times are limiting factor. For reducing these drawbacks it is also discussed possibilities which kind of modifications can employ. Finally, some practical considerations are discussed on an implementation on motor drive systems.

In this paper, a T–S (Takagi–Sugeno) adaptive tracking algorithm control based on small gain theorem is proposed for an uncertain robot system with $$n$$-link manipulators. A nonzero time-varying parameter is introduced in the common T–S fuzzy logic system, the T–S type fuzzy logic system with updated parameters laws is build, then the new and original universal approximation with parameter is introduced. The approximation accuracy can be updated on-line by the parameters, which is not limited by the number of fuzzy rules. With the novel property of universal approximation, the proposed adaptive control can be synthetized to overcome the limitations such as on-line learning computation burden in conventional fuzzy logic systems. The originality T–S fuzzy logic system is used to compensate the unknown model of robot manipulators and the adaptive tracking control algorithm is designed with the new property of universal approximation. Based on the analysis of small gain theorem and ISS theory (input-to-state stability), all signals in closed-loop system can be guaranteed to be bounded, and the system can be extended from semi-global stability to global stability by employing the proposed adaptive control scheme. Finally, simulation results are shown to demonstrate the effectiveness of the adaptive control scheme.

To enhance the real-time performance of multi-frequency line spectrum vibration control within the floating raft system and minimize the vibration of the floating raft, a hybrid adaptive active vibration control algorithm based on the FxLMS algorithm is introduced. Focusing on the bottom degree of freedom in the floating raft system, a horizontal two-degree of freedom system is established. Initially, the dynamic model of this system is analyzed; subsequently, the hybrid adaptive control algorithm is analyzed theoretically, and its stability is verified by simulation under normal condition, shock disturbance condition, and random signal disturbance condition. The results reveal that the average control effects of feedforward control across each line spectrum under these conditions are −9.55 dB, −10.88 dB, and −9.91 dB, respectively. In contrast, hybrid adaptive control demonstrates stronger performance with average control effects of −23.19 dB, −21.65 dB, and −21.48 dB. Lastly, an experimental platform was established to assess the performance of the proposed control algorithm. The experimental results demonstrate that the hybrid adaptive control successfully mitigates vibrations arising from multiple frequency spectra. The hybrid adaptive control achieves average control effects of −14.77 dB, −15.13 dB, and −14.63 dB across the three conditions, demonstrating superior control efficacy.

The structure and performance requirements for active vibration isolation control systems motivate the use of adaptive control. This is especially true for spacecraft platforms subject to uncertainties inherent in on-orbit operation. This paper is an initial investigation into adaptive control strategies and algorithms that may have application to isolation on spacecraft platforms. Analysis of the algorithms, numerical simulation, and laboratory test data are used to evaluate adaptive feedforward control. Of particular interest are the performance characteristics and limitations of the filtered-x LMS (FXLMS) algorithm. The augmented error algorithm and combination feedback/feedforward control are two means investigated to extend the capabilities of FXLMS by desensitizing the algorithm to the specific dynamics of the plant. Several experiments were conducted on a laboratory testbed which serves as the prototype for a planned active vibration isolation flight demonstration.

In this paper, two novel adaptive finite-time control schemes are proposed for position tracking of nonlinear teleoperation system, which dynamic uncertainties, actuator saturation, and time-varying communication delays are considered. First, a novel auxiliary variable is designed to provide more stable performance. The radial basis function (RBF) neural network is introduced to estimate dynamic uncertainties. Second, two adaptive finite-time control schemes are investigated. In control scheme I, the RBF neural network and the gain switching strategy are applied to compensate the actuator saturation. In control scheme II, an auxiliary compensation filter and the compensation adaptive update laws, which contain the finite-time structure, are developed for dealing with saturation. Third, the finite-time adaptive controller is designed in each of these two control schemes. Based on the multiple Lyapunov function method, the closed-loop teleoperation system with these two control methods is proved to be bounded and finite-time stability. Finally, the simulation experiments are performed and the comparisons with other control methods are shown. The effectiveness of the proposed control schemes is demonstrated.