Simple Adaptive Scheme Research Articles

Adaptive Task-Space Manipulator Control with Parametric Uncertainties in Kinematics and Dynamics

This paper aims to deal with the problem of robot tracking control in the presence of parametric uncertainties in kinematics and dynamics. We propose a simple and effective adaptive control scheme that includes adaptation laws for unknown constant kinematic and dynamic parameters. In addition, instead of convolution-type filtered differentiation, we designed a new observer to estimate velocity in the task space, and the proposed adaptive control requires no acceleration measurement in the joint space. Using the Lyapunov stability and Barbalat’s lemma, we show that by appropriately choosing design parameters, the tracking errors and estimation errors in task space can asymptotically converge to zero. Through numerical simulation on a two-link robot with a fixed camera, we illustrate the design procedures and demonstrate the feasibility of the proposed adaptive control scheme for the trajectory tracking of robot manipulators.

Simple Adaptive Synergetic Control Scheme Based on The MIT Rule of the DC Motor

An adaptive synergetic control scheme of a DC motor via a DC-DC converter is presented in this article. The difficulty in implementing a synergetic control lies in the optimal choice of the control parameters. This operation becomes tedious when the number of closed loops in the control system is large. Several adaptive parameter methods are exposed in the literature, some of which are complex and others simple. A synergetic control parameter adaptation law is almost unexplored in the scientific literature. The proposed solution is an adaptation mechanism of the synergetic control parameter using the MIT (Massachusetts Institute of Technology) rule. A theoretical study carried out made it possible to highlight a law of adjustment of the current and the speed, the variation of which verifies the negative direction of the gradient of the defined cost function. Computer simulations in Matlab/Simulink show good controller performance in improving transient stability and settling time. In addition, synergetic control is an interesting tool for speed and current control of DC motor.

Adaptive active disturbance rejection control guaranteeing uniformly ultimate boundedness and simplicity

SummaryThe active disturbance rejection control (ADRC) scheme requires a high bandwidth in the extended state observer (ESO), causing heavy noise pollution in the control system. This article develops an effective and simple adaptive ADRC scheme to realize high performance control when ESO bandwidth is limited. The adaptative law is generated by the state tracking error such that all the feedback loops and feedforward channels are activated for active disturbance rejection. An “attached” and “quantified” adaptation is developed to guarantee the easy usage of the proposed scheme. The uniformly ultimate boundedness of all the signals in the closed‐loop system is proved in the sense of Lyapunov theory. Finally, two simulation examples are given to demonstrate the results.

Deterministic learning from neural control for uncertain nonlinear pure‐feedback systems by output feedback

SummaryThe essence of intelligence lies in the acquisition/learning and utilization of knowledge. However, how to implement learning in dynamical environments for nonlinear systems is a challenging issue. This article investigates the deterministic learning (DL) control problem for uncertain pure‐feedback systems by output feedback, which achieves the human‐like learning and control in a simple way. To reduce the complexity of control design and analysis, first, by combining an appropriate system transformation, the original pure‐feedback system is transformed into a simple normal nonaffine system. An observer is then introduced to estimate the transformed system states. Based on the backstepping and dynamic surface control techniques, a simple adaptive neural control scheme is first developed to guarantee the finite time convergence of the tracking error using only one neural network (NN) approximator. Second, through DL, the exponential convergence of the NN weights is obtained with the satisfaction of partial persistent excitation condition. Thus, locally accurate approximation/learning of the transformed unknown system dynamics is achieved and stored as constant NNs. Finally, by utilizing the stored knowledge, an experience‐based controller is constructed and a novel learning control scheme is further proposed to improve the control performance without any further adaptation online for the estimate neural weights. Simulation results have been given to illustrate that the proposed scheme not only can learn and memorize knowledge like humans but also can utilize experience to achieve superior control performance.

Simple adaptive trajectory tracking control of underactuated autonomous underwater vehicles under LOS range and angle constraints

In this study, the authors propose a simple adaptive trajectory tracking control scheme for underactuated autonomous underwater vehicles (AUVs) subject to unknown dynamic parameters and disturbances under asymmetric time-varying line-of-sight (LOS) range and angle constraints. A simple error mapping function is constructed to transform the LOS range and angle constraint problems of AUVs trajectory tracking into the problem of the boundedness of transform variable, such that any constraint boundaries of LOS range and angle can be handled. The compounded uncertain item caused by the unknown dynamic parameters and disturbances is transformed into a linear parametric form with only three unknown parameters called virtual parameters. Based on the above, a simple adaptive trajectory tracking control law is developed using dynamic surface control technique, where the adaptive laws online provide the estimations of virtual parameters. As a result, the proposed trajectory tracking control scheme is simple to compute and easy to implement in engineering applications. Strict stability analysis indicates that the designed control law makes AUVs track the desired trajectory within the LOS range and angle constraints and guarantees the boundedness of all signals of the closed-loop control system. Simulation results and comparison verify the effectiveness of the designed control scheme.

Fuel cell vehicle energy management strategy based on the cost of ownership

This study presents a novel energy management strategy (EMS) which outperforms the published strategies developed for an international technology challenge, IEEE Vehicular Technology Society (VTS) Motor Vehicles Challenge 2017. The objective of the strategy is to minimise the cost of ownership of a low-power (15 kW) fuel cell (FC)-battery electric vehicle. Both the fuel consumption cost and power sources degradation costs are combined to represent the total cost of ownership. The simple adaptive rule-based strategy optimises the FC operation during low-traction power operation and switches to battery charge-sustaining operation for high traction power operation. This minimises fuel consumption and increases the lifetimes of the FC and of the battery. The strategy is then compared with the EMS of the 2015 Toyota Mirai, and the challenge vehicle model is modified to capture the learnings from the Mirai. Finally, a cost-benefit analysis for a plug-in FC vehicle is considered in order to improve FC lifetimes and to reduce costs for short drive cycles.

Response attenuation of cable-stayed bridge subjected to central US earthquakes using neuro-fuzzy and simple adaptive control

Cable-stayed bridges generally present high flexibility, little redundancy, and complex dynamic behavior, which may lead to excessive dynamic responses. Structural control is an option to limit dynamic responses of these structures in order to avoid excessive damage and guarantee an acceptable level of comfort. Most studies in the field of control of cable-stayed bridges are performed considering their nominal parameters. However, bridges are subjected to extreme changes in temperature, cracking, corrosion, snow accumulation, extreme loading and fatigue. Additionally, engineering modeling simplifications, estimates, and assumptions also result in estimation of parameters that are different from the actual ones. Earthquake excitation prediction also involves a great amount of uncertainties. The development of a control scheme that presents satisfactory performance and presents enough robustness is fundamental for its successful operation. The main goal of this study is to find control solutions that are able to attenuate seismic induced responses, while providing enough predictability and robustness in face of parametric changes and the variability of earthquake loads. The implementation of a control approach that is cost effective, dependable, predictable and effective may lead to the possibility of accounting for this control solution in design, allowing for more flexible but safe structures. A control approach that is dependable and robust has the potential to guarantee performance limits and impact how structures are designed in the future. Adaptive control is presented in this research as a suitable and robust control alternative to deal with the many uncertainties related to the prediction of bridge structural parameters and earthquake loading. Adaptive control schemes based on the simple adaptive control and the neuro-fuzzy adaptive control theoretical basis are proposed to attenuate the seismic responses of the benchmark for cable-stayed bridges, considering different parametric scenarios, as well as site conditions and seismic characteristics of the central US region. The performances of the adaptive schemes are compared to non-adaptive control before and after two parametric variations are introduced to the bridge, considering earthquakes matched to the American Association of State Highways and Transportation Officials’ most recent design spectra. Probability density functions are developed in order to capture the controlled performance representative of the earthquake suite. The adaptive methods performances are compared to uncontrolled and passive schemes before and after two parametric variations are considered. The bridge controlled by the passive-on case presents a satisfactory performance for the nominal structure. However, once the parameters are changed, the performance of the control scheme deteriorates. The passive-off scheme sustains performance well; however, the control scheme does not reduce overall responses significantly. The neuro-fuzzy control displays improved performance in comparison to the passive cases. The simple adaptive control scheme gives an overall successful reduction in both peak and normed responses, sustaining performance for most criteria and shows improved performance and robustness compared to the other schemes.

Sparse grid approximation spaces for space–time boundary integral formulations of the heat equation

The aim of this paper is to develop and analyse stable and accurate numerical approximation schemes for boundary integral formulations of the heat equation with Dirichlet boundary conditions. The accuracy of Galerkin discretisations for the resulting boundary integral formulations strongly depends on the choice of discretisation space. We develop a-priori error analysis utilising a proof technique that involves norm bounds in hierarchical wavelet subspace decompositions. We apply this to full tensor product discretisations and anisotropic sparse grid discretisations and demonstrate improvements over existing results in both cases. Finally, a simple adaptive scheme is proposed to suggest an optimal shape for the sparse grid index sets.

Adaptive finite element methods for sparse PDE-constrained optimization

Abstract We propose and analyse reliable and efficient a posteriori error estimators for an optimal control problem that involves a nondifferentiable cost functional, the Poisson problem as state equation and control constraints. To approximate the solution to the state and adjoint equations we consider a piecewise linear finite element method, whereas three different strategies are used to approximate the control variable: piecewise constant discretization, piecewise linear discretization and the so-called variational discretization approach. For the first two aforementioned solution techniques we devise an error estimator that can be decomposed as the sum of four contributions: two contributions that account for the discretization of the control variable and the associated subgradient and two contributions related to the discretization of the state and adjoint equations. The error estimator for the variational discretization approach is decomposed only in two contributions that are related to the discretization of the state and adjoint equations. On the basis of the devised a posteriori error estimators, we design simple adaptive strategies that yield optimal rates of convergence for the numerical examples that we perform.

Significantly improved oxidation of bio-based furans into furan carboxylic acids using substrate-adapted whole cells

Abstract Furan carboxylic acids are important building blocks in polymer and fine chemical industries. In this work, a simple substrate adaptation strategy was applied to improve the catalytic performances of Comamonas testosteroni SC1588 cells for the synthesis of various furan carboxylic acids. It was found that biocatalytic synthesis of 5-hydroxymethyl-2-furancarboxylic acid (HMFCA) was substantially promoted by adding histidine and increasing cell concentrations. HMFCA was produced in a quantitative yield from 200 mM HMF in 24 h. Besides, the HMFCA yields of 71%–81% were achieved with the substrate concentrations up to 250–300 mM. It was firstly found that 4-tert-butylcatechol (TBC), as the stabilizer present in HMF, exerted a significantly detrimental effect on whole-cell catalytic synthesis of HMFCA at high substrate concentrations (more than 130 mM). In addition, a variety of furan carboxylic acids such as 2-furoic acid, 5-methyl-2-furancarboxylic acid and 5-methoxymethyl-2-furancarboxylic acid were synthesized with the yields up to 98%.

On simple adaptive control of plants not satisfying almost strict passivity and positivity conditions: an introduction to parallel feedforward configuration

Simple adaptive control systems were known to be robust against a class of disturbances and globally stable if the controlled plant is almost strictly positive real, that is, if there exists a positive definite static output feedback (unknown and not needed for implementation) such that the resulting closed-loop transfer function is strictly positive real. The present paper discusses the simple adaptive control scheme for a non-almost strictly positive real plants and gives a brief review of the parallel feedforward which makes the augmented plant satisfy the almost passivity or positivity conditions based on the stabilisability property of the system. The validity of the simplified adaptive algorithm under the positivity condition is examined through numerical simulation for both single-inputs single-outputs and multi-inputs multi-outputs systems.

On simple adaptive control of plants not satisfying almost strict passivity and positivity conditions: an introduction to parallel feedforward configuration

Simple adaptive control systems were known to be robust against a class of disturbances and globally stable if the controlled plant is almost strictly positive real, that is, if there exists a posi...

A posteriori error estimates for the Stokes problem with singular sources

We propose a posteriori error estimators for classical low-order inf–sup stable and stabilized finite element approximations of the Stokes problem with singular sources in two and three dimensional Lipschitz, but not necessarily convex, polytopal domains. The designed error estimators are proven to be reliable and locally efficient. On the basis of these estimators we design a simple adaptive strategy that yields optimal rates of convergence for the numerical examples that we perform.

Adaptive PID-tracking control of muscle-like actuated compliant robotic systems with input constraints

• Simple adaptive feedback strategies to dominate an uncertain system. • Bio-inspiration/biomechanics. • Controlling a human-upper-arm-like system: double pendulum with muscle forces. • Input constraints. In this paper, adaptive control strategies are analyzed to control a compliant robot arm in form of a double pendulum structure. Powered by antagonistic, muscle-like actuators, the controller shall enable the system to track prescribed trajectories for a desired movement of the arm, as quickly and precisely as possible. But, due to the natural muscle behavior, the control variables underly prescribed bounds: the limited control inputs are generated by muscle intensities. Therefore, feedback strategies are sought which have to be limited/saturated from the very beginning. For this, at first, simulation studies of adaptive λ -tracking controllers are performed to apply the best-rated ones to the compliant robotic system . After some model adjustments concerning the mechanics of a prototype (for experimental verification), an intensity control is determined, to divide the controller variables to the respective muscle pairs. Various numerical simulations of a system with DoF=2 show the effectiveness of the saturated controllers in set-point and tracking control. The efficiency of these controllers is verified and proven in experiments of a prototype.

Ana posteriorierror analysis for an optimal control problem with point sources

We propose and analyze a reliable and efficienta posteriorierror estimator for a control-constrained linear-quadratic optimal control problem involving Dirac measures; the control variable corresponds to the amplitude of forces modeled as point sources. The proposeda posteriorierror estimator is defined as the sum of two contributions, which are associated with the state and adjoint equations. The estimator associated with the state equation is based on Muckenhoupt weighted Sobolev spaces, while the one associated with the adjoint is in the maximum norm and allows for unbounded right hand sides. The analysis is valid for two and three-dimensional domains. On the basis of the deviseda posteriorierror estimator, we design a simple adaptive strategy that yields optimal rates of convergence for the numerical examples that we perform.

Adaptive quantised control of switched stochastic strict‐feedback non‐linear systems with asymmetric input saturation

The adaptive quantised tracking problem is addressed for a class of switched stochastic strict-feedback non-linear systems with asymmetrical input saturation in this study. With the aid of a Gaussian error function-based continuous differentiable switching model and some special techniques, the technical difficulties from dealing with the switching asymmetric saturation non-linearities and the sector-bounded quantisation errors are overcome. Then, by combining the common Lyapunov function method, backstepping technique and neural network approximation-based approach, a simple common adaptive tracking control scheme involving one adaptive parameter only is presented for such systems under arbitrary switching. The given quantised control scheme guarantees that all signals of the closed-loop system are semi-globally bounded in probability while the tracking error can converge to a small neighbourhood of the origin. Finally, simulation studies are provided to illustrate the effectiveness and applicability of the proposed control design.

Abstract 3892: Upfront DPD screening with adaptive dosing to secure 5-FU administration in head and neck cancer patients

Abstract Upfront screening for dihydropyrimidine dehydrogenase (DPD) deficiency in patients scheduled for 5-FU-based protocols should reduce the risk of treatment-related toxicities by preventive adaptive dosing. Our group has developed a simple, rapid and cheap functional testing that helps categorizing patients on their DPD status and detecting poor metabolizer (PM) patients, using UH2/U ratio measurement in plasma as a surrogate for DPD activity. 5-FU dosing can be next tailored using a geometric scale, according to the level of DPD deficiency. We report here the performances of this adaptive dosing strategy implemented in routine clinical practice in head and neck cancer patients. A total of 218 evaluable adult patients were scheduled for a 5-FU-regimen (mostly TPF protocol), with prospective search for DPD deficiency and, if required, tailored dosing. Overall, 20 patients (9%) were identified as PM and received subsequently an average 20-50% reduced dose of 5-FU as compared with extensive metabolizer (EM) patients (2102 ± 254 mg VS. 2577 ±353mg, p<0.001 t-test). Gender (Female) was associated with higher risk for being PM (p=0.01, Pearson's Chi-square test), whereas age was not. Early severe toxicities were seen only in 5% of patients, a figure markedly lower than the one usually reported with 5-FU-based regimen in head-and-neck cancer patients. Similarly, all severe toxicities (i.e; including now the delayed or cumulative ones) were observed in 12.8% of patients only. No statistical difference was evidenced in overall toxicities between PM patients with adapted dosing (10%) and EM patients with standard dosing (13%, p=0.7014, Chi-square test), suggesting that being DPD-deficient is not anymore a risk for severe or life-threatening toxicities with 5-FU, provided that corrective action is undertaken. As for early toxicities, incidence of global toxicities was lower than the 30-40% usually described with 5-FU in this setting. Finally, despite an average -20% reduction in 5-FU dosing between PM and EM patients, clinical efficacy was not statistically different between the two groups (CR+PR: 56% VS. 40%, SD: 11% VS. 5%, PD: 22% VS. 43%, p = 0.2774, chi-square test), thus demonstrating how cutting 5-FU dose in PM patients does not hinder treatment efficacy. In conclusion, this study shows that 5-FU-related toxicities can be avoided at low cost by the upfront detection of DPD deficient patients with simple adaptive dosing strategies. Of note, reducing 5-FU dosing in PM patients did not affect negatively treatment efficacy, while limiting the incidence of treatment-related toxicities, thus improving the efficacy/toxicity balance of this drug. Citation Format: Joseph Ciccolini, Manon Launay, Charlotte Dupuis, Florence Duffaud, Bruno Lacarelle, Sébastien Salas. Upfront DPD screening with adaptive dosing to secure 5-FU administration in head and neck cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3892.

Quadratic convergence of Levenberg-Marquardt method for elliptic and parabolic inverse robin problems

We study the Levenberg-Marquardt (L-M) method for solving the highly nonlinear and ill-posed inverse problem of identifying the Robin coefficients in elliptic and parabolic systems. The L-M method transforms the Tikhonov regularized nonlinear non-convex minimizations into convex minimizations. And the quadratic convergence of the L-M method is rigorously established for the nonlinear elliptic and parabolic inverse problems for the first time, under a simple novel adaptive strategy for selecting regularization parameters during the L-M iteration. Then the surrogate functional approach is adopted to solve the strongly ill-conditioned convex minimizations, resulting in an explicit solution of the minimisation at each L-M iteration for both the elliptic and parabolic cases. Numerical experiments are provided to demonstrate the accuracy, efficiency and quadratic convergence of the methods.

Programming Agent Deliberation Using Procedural Reflection

A key advantage of BDI-based approaches to agent programming, is that agents can deliberate about which course of action to adopt to achieve a goal or respond to an event. However, while state-of-the-art BDI-based agent programming languages allow the programmer to specify the context(s) in which a particular plan is applicable, they are typically limited to a single, hard-coded, deliberation strategy for all task environments. In this paper, we present an alternative approach, in which an agent’s deliberation strategy forms part of the agent program. We show how both conventional agent programs and the agent’s deliberation strategy can be encoded in the agent programming language meta-APL. Key steps in the deliberation cycle of meta-APL are reflected in the state of the agent and can be queried and updated by meta-APL rules, allowing application-specific BDI deliberation strategies to be programmed in a straightforward way. To illustrate the flexibility of meta-APL, we show how three typical BDI deliberation strategies can be programmed using meta-APL rules. We then show how meta-APL can used to program a simple adaptive deliberation strategy that avoids interference between intentions.

UPFRONT DPD DEFICIENCY DETECTION TO SECURE 5-FU ADMINISTRATION: PART 2- APPLICATION TO HEAD-AND-NECK CANCER PATIENTS.

Upfront screening for dihydropyrimidine dehydrogenase (DPD) deficiency in patients scheduled for 5-FU should help reduce the risk of toxicities by preventive adaptive dosing. Our group has developed a simple functional testing categorizing patients upon their DPD status, i.e. extensive metabolizer (EM) or poor metabolizer (PM) patients, using UH2/U ratio measurement in plasma as a surrogate for DPD activity. 5-FU dosing can then be tailored according to DPD deficiency status. We present here an observational study of this strategy implemented in routine clinical practice when treating head-and-neck cancer patients. A total of 218 evaluable adult patients were treated with a 5-FU-regimen, with DPD-based adaptive dosing. Among them, 20 (9%) were identified as PM and received subsequently a 20-50% reduced dosing of 5-FU as compared with EM patients (2102 ±254 mg VS. 2577 ±353mg, p<0.001 ttest). Gender (Female) was associated with higher risk for being PM (p=0.01, Pearson's Chi squared test). Overall, early severe toxicities were seen only in 5% of patients, all being EM with standard dosing. Similarly, overall severe toxicities were observed in 12.8% of patients only, both figures being markedly lower than usually reported with standard 5-FU. Despite the average -20% reduction in 5-FU dosing between PM and EM patients, clinical efficacy was not statistically different between the two groups (p = 0.2774, chi-square test). This study shows that 5-FU-related toxicities can be greatly reduced in routine clinical practice by the upfront detection of DPD deficient patients with simple adaptive dosing strategy.