Adaptive Admittance Control Scheme with Virtual Reality Interaction for Robot-Assisted Lower Limb Strength Training

In this paper we focus on the adaptive control of structural acoustics using intelligent structures with embedded piezoelectric (PZT) patches and low cost digital signal processor systems. After a discussion on the adaptive feedforward control scheme, a hybrid adaptive control scheme is proposed, which takes advantage of both feedback control and adaptive feedforward control. The two schemes are realized on a low-cost, small volume, convenient and universal digital signal processing (DSP) board. A carbon fiber reinforced polymer plate with two embedded PZT patches is developed and used in two experiments. The first experiment is adaptive interior noise control using the intelligent plate, in which the adaptive feedforward control scheme is employed. Obvious noise reduction is obtained for constant frequency, swept frequency and varying amplitude harmonic disturbances. The second experiment is adaptive control of sound-induced vibration of the plate, where two embedded PZT patches are used as an actuator and a sensor, respectively, and the hybrid adaptive controller is applied. The full vibration reduction for various harmonic excitations is obtained, verifying the advantage of the hybrid adaptive control. It is demonstrated that active control of structural acoustics can be efficiently achieved by employing intelligent structures, advanced adaptive control schemes and the low-cost DSP board.

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.

ABSTRACTThis paper investigates an adaptive optimal admittance control scheme for robot manipulators interacting with unknown environment. To resolve the optimized interaction performance considering tracking error and interaction force, an impedance adaptation approach is developed without the initial stabilizing policy. Based on the gradient‐based updating method, the online solution can exponentially converge to the optimal impedance gain without prior knowledge of environment dynamics. A nonlinear mapping method is integrated into the admittance control, transforming the constrained system into an equivalent system without state constraints. By eliminating feasibility conditions, the tracking controller can achieve the full‐state asymmetric time‐varying constraints under a broad range of initial conditions. Through the Lyapunov analysis, it is proven that the closed‐loop signals are bounded. Finally, simulation and experiment results demonstrate the effectiveness of the proposed methods.

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.

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.

<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

The integration of robotics into retinal microsurgery leads to a reduction in surgeon perception of tool-to-tissue interaction forces. This blunting of human tactile sensory input, which is due to the inflexible mass and large inertia of the robotic arm as compared to the milli-Newton scale of the interaction forces and fragile tissues during ophthalmic surgery, identifies a potential iatrogenic risk during robotic eye surgery. In this paper, we aim to evaluate two variants of an adaptive force control scheme implemented on the Steady-Hand Eye Robot (SHER) that are intended to mitigate the risk of unsafe scleral forces. The present study enrolled ten retina fellows and ophthalmology residents into a simulated procedure, which simply asked the trainees to follow retinal vessels in a model retina surgery environment. For this purpose, we have developed a force-sensing (equipped with Fiber Bragg Grating (FBG)) instrument to attach to the robot. A piezo-actuated linear stage for creating random lateral motions to the eyeball phantom has been provided to simulate disturbances during surgery. The SHER and all of its dependencies were set up in an operating room in the Wilmer Eye Institute at the Johns Hopkins Hospital. The clinicians conducted robot-assisted experiments with the adaptive controls incorporated as well as freehand manipulations. The results indicate that the Adaptive Norm Control (ANC) method, is able to maintain scleral forces at predetermined safe levels better than even freehand manipulations. Novice clinicians in robot training however, subjectively preferred freehand maneuvers over robotic manipulations. Clinician preferences once highly skilled with the robot is not assessed in this study.

A new adaptive control scheme for uncertain nonlinear systems with quantized input signal

Dead-zone is one of the most common hard nonlinearities ubiquitous in master–slave teleoperation systems, particularly in the slave robot joints. However, adaptive control techniques applied in teleoperation systems usually deal with dynamic uncertainty but ignore the presence of dead-zone. Dead-zone has the potential to remarkably deteriorate the transparency of a teleoperation system in the sense of position and force tracking performance or even destabilizing the system if not compensated for in the control scheme. In this paper, an adaptive bilateral control scheme is proposed for nonlinear teleoperation systems in the presence of both uncertain dynamics and dead-zone. An adaptive controller is designed for the master robot with dynamic uncertainties and the other is developed for the slave robot with both dynamic uncertainties and unknown dead-zone. The two controllers are incorporated into the four-channel bilateral teleoperation control framework to achieve transparency. The transparency and stability of the closed-loop teleoperation system is studied via a Lyapunov function analysis. Comparisons with the conventional adaptive control which merely deal with dynamic uncertainties in the simulations demonstrate the validity of the proposed approach.

Global adaptive neural network control of nonlinear time-varying systems with unknown control coefficients and model uncertainties

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.

As advanced integrated network architecture, ldquocognitive radio technologies,rdquo which aim to improve the spectrum efficiency, have been studied. In the cognitive radio networks, each node recognizes radio conditions, and according to them, optimizes their wireless communication routes with the integration of the heterogeneous wireless systems not only by switching over them but also aggregating and utilizing them simultaneously. The Adaptive control of switchover use and concurrent use of various wireless systems will offer a stable and flexible wireless communication. In this paper, we propose the adaptive traffic route control scheme that provides high quality of service (QoS) for cognitive radio technology, and examine the performance of the proposed scheme through the field trials and computer simulations. The experimental results of field trials show that the adaptive route control according to the radio conditions improves the user IP throughput by more than 20% and reduce the one-way delay to less than 1/6 with the concurrent use of IEEE802.16 and IEEE802.11 wireless media. Moreover, the simulation results assuming hundreds of mobile terminals reveal that the number of users that satisfy the required QoS of voice over IP (VoIP) service and the total network throughput of FTP users increase by more than twice at the same time with the proposed algorithm. The proposed adaptive traffic route control scheme can provide the cognitive radio technologies the appropriate communication qualities to meet various demands for application QoS.

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.