Optimal Parameter Selection for Constraint-Following Control for Mechanical Systems Based on Stackelberg Game

2021 ◽  
Author(s):  
Qinqin Sun ◽  
Xiuye Wang ◽  
Guolai Yang ◽  
Ye-Hwa Chen ◽  
Fai Ma
Keyword(s):  
Stackelberg Game ◽  
Optimal Parameter ◽  
Mechanical Systems ◽  
Parameter Selection ◽  
Design Parameters ◽  
Fast Time ◽  
Control Cost ◽  
Stackelberg Strategy ◽  
Control Scheme ◽  
Unique Parameter

Abstract This paper proposes an optimal parameter design of control scheme for mechanical systems by adopting the Stackelberg game theory. The goal of the control is to drive the mechanical system to follow the prescribed constraints. The system uncertainty is (possibly fast) time-varying and bounded. A β-measure is defined to gauge the performance. A robust control is proposed to render the β-measure uniformly ultimately bounded. This control scheme is based on feasible design parameters (i.e., parameters within prescribed range), whose choice may not be unique. For optimal (unique) parameter selection, a Stackelberg game is formulated. By taking the control design parameters as the players, for each player, a cost function is built with the consideration of the performance cost, the time cost and the control cost. To follow, the Stackelberg strategy is then carried out via backward induction, which results in the choice of the optimal parameters.

Robust Maneuvering Control for Marine Vehicles Under Uncertain Ocean Currents

2013 ◽  
Author(s):  
Qingmin Huang ◽  
Ye-Hwa Chen ◽  
Xin Nie
Keyword(s):  
Ocean Currents ◽  
Design Parameters ◽  
Fast Time ◽  
State Transformation ◽  
Marine Vehicles ◽  
Control Scheme ◽  
Uniform Ultimate Boundedness ◽  
Stability Ball ◽  
Marine Vehicle ◽  
Structure Properties

We consider the problem of tracking a desired trajectory with a desired velocity for a marine vehicle. There are possibly fast time-varying uncertainties which may exist in the model, the inputs, the ocean currents, as well as environment disturbances. Based on the possible bound of the uncertainties and some structure properties the marine vehicle are met, a state transformation is made to the dynamics of error of the marine vehicle. A robust control scheme is proposed which renders the transformed system practically stable. A proof shows that the original uncertain marine vehicle under this control will also be practically stable. Furthermore, the uniform ultimate boundedness ball and uniform stability ball of the marine vehicle can be made arbitrarily small by suitable choice of the design parameters.

scholarly journals Observer-based robust integral of the sign of the error control of class I of underactuated mechanical systems: Theory and real-time experiments

2021 ◽  
pp. 014233122110313
Author(s):  
Afef Hfaiedh ◽  
Ahmed Chemori ◽  
Afef Abdelkrim
Keyword(s):  
Real Time ◽  
Error Control ◽  
Degrees Of Freedom ◽  
Sliding Mode ◽  
Mechanical Systems ◽  
Class I ◽  
Control Input ◽  
Underactuated Mechanical Systems ◽  
Control Scheme ◽  
And Performance

In this paper, the control problem of a class I of underactuated mechanical systems (UMSs) is addressed. The considered class includes nonlinear UMSs with two degrees of freedom and one control input. Firstly, we propose the design of a robust integral of the sign of the error (RISE) control law, adequate for this special class. Based on a change of coordinates, the dynamics is transformed into a strict-feedback (SF) form. A Lyapunov-based technique is then employed to prove the asymptotic stability of the resulting closed-loop system. Numerical simulation results show the robustness and performance of the original RISE toward parametric uncertainties and disturbance rejection. A comparative study with a conventional sliding mode control reveals a significant robustness improvement with the proposed original RISE controller. However, in real-time experiments, the amplification of the measurement noise is a major problem. It has an impact on the behaviour of the motor and reduces the performance of the system. To deal with this issue, we propose to estimate the velocity using the robust Levant differentiator instead of the numerical derivative. Real-time experiments were performed on the testbed of the inertia wheel inverted pendulum to demonstrate the relevance of the proposed observer-based RISE control scheme. The obtained real-time experimental results and the obtained evaluation indices show clearly a better performance of the proposed observer-based RISE approach compared to the sliding mode and the original RISE controllers.

scholarly journals Selecting single cell clustering parameter values using subsampling-based robustness metrics

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ryan B. Patterson-Cross ◽  
Ariel J. Levine ◽  
Vilas Menon
Keyword(s):  
Single Cell ◽  
Optimal Parameter ◽  
Clustering Algorithms ◽  
Cell Types ◽  
Parameter Selection ◽  
Data Set ◽  
Biologically Relevant ◽  
Cell Clustering ◽  
Parameter Values ◽  
Robustness Metrics

Abstract Background Generating and analysing single-cell data has become a widespread approach to examine tissue heterogeneity, and numerous algorithms exist for clustering these datasets to identify putative cell types with shared transcriptomic signatures. However, many of these clustering workflows rely on user-tuned parameter values, tailored to each dataset, to identify a set of biologically relevant clusters. Whereas users often develop their own intuition as to the optimal range of parameters for clustering on each data set, the lack of systematic approaches to identify this range can be daunting to new users of any given workflow. In addition, an optimal parameter set does not guarantee that all clusters are equally well-resolved, given the heterogeneity in transcriptomic signatures in most biological systems. Results Here, we illustrate a subsampling-based approach (chooseR) that simultaneously guides parameter selection and characterizes cluster robustness. Through bootstrapped iterative clustering across a range of parameters, chooseR was used to select parameter values for two distinct clustering workflows (Seurat and scVI). In each case, chooseR identified parameters that produced biologically relevant clusters from both well-characterized (human PBMC) and complex (mouse spinal cord) datasets. Moreover, it provided a simple “robustness score” for each of these clusters, facilitating the assessment of cluster quality. Conclusion chooseR is a simple, conceptually understandable tool that can be used flexibly across clustering algorithms, workflows, and datasets to guide clustering parameter selection and characterize cluster robustness.

scholarly journals Experimental on the protective performance of column bridge pier anti-scouring device

2021 ◽  
Vol 248 ◽  
pp. 01015
Author(s):  
Zhejiang Chen ◽  
Liang Liu ◽  
Yonglin Hu ◽  
Nan Ye ◽  
Xiaoli Shen ◽  
...  
Keyword(s):  
Optimal Parameter ◽  
Reduction Rate ◽  
Local Scour ◽  
Bridge Pier ◽  
Design Parameters ◽  
Water Tank ◽  
Protective Device ◽  
Measuring Point ◽  
Test Range ◽  
The Impact

Because of the problem of local scour caused by the change of the flow structure caused by the water-resistance of the column bridge pier, the theoretical analysis, and indoor water tank test were used to study the effect of installing a new anti-scouring device in front of the bridge pier on the local scour reduction effect; the influence of the main design parameters such as the height of the protective device, the angle of the protective device and the distance from the protective device to the bridge pier on the local scour of the bridge pier was selected, and the optimal parameter design combination was selected. The test results show that: under the same water flow conditions, the maximum scour depth reduction rate of the measuring point under the protection of the protective device is 48.4% to 74.2% compared with the unprotected scour; the reduction rate of the bridge pier is relative to the relative height of the device and the device equivalent. The angle and the distance between the device and the bridge pier are related, and the shock reduction rate decreases with the increase of the flow intensity. In the test range, the ratio of the device height to the water depth is 2/3, the device angle is 60 °, and the distance from the bridge pier is 3. When the diameter of the pier is doubled, the effect of reducing the impact on the pier is the best.

scholarly journals Adaptive Cluster Synchronization for Nondelayed and Delayed Coupling Complex Networks with Nonidentical Nodes

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Ze Tang ◽  
Jianwen Feng
Keyword(s):  
Sufficient Conditions ◽  
Pinning Control ◽  
Cluster Synchronization ◽  
Linear Matrix ◽  
Control Cost ◽  
Synchronization Problem ◽  
Delayed Coupling ◽  
Control Scheme ◽  
Successful Control ◽  
Synchronization Pattern

We focus on the cluster synchronization problem for a kind of general networks with nondelayed and delayed coupling. Based on the pinning control scheme, a small fraction of the nodes in each cluster are pinned for successful control, and the states of the whole dynamical networks can be globally forced to the objective cluster states. Sufficient conditions are derived to guarantee the realization of the cluster synchronization pattern for all initial values by means of the Lyapunov stability theorem and linear matrix inequalities (LMIs). By using the adaptive update law, relative smaller control gains are obtained, and hence the control cost can be substantially lower. Numerical simulations are also exploited to demonstrate the effectiveness and validity of the main result.

Recurrent Higher Order Neural Network Control for Output Trajectory Tracking with Neural Observers and Constrained Inputs

2010 ◽  
pp. 286-311 ◽  
Author(s):  
Luis J. Ricalde ◽  
Edgar N. Sanchez ◽  
Alma Y. Alanis
Keyword(s):  
Neural Network ◽  
Tracking Error ◽  
Network Control ◽  
Design Parameters ◽  
The Neural Network ◽  
Control Scheme ◽  
Output Trajectory ◽  
Error Dynamics ◽  
Adaptation Law ◽  
Constrained Inputs

This Chapter presents the design of an adaptive recurrent neural observer-controller scheme for nonlinear systems whose model is assumed to be unknown and with constrained inputs. The control scheme is composed of a neural observer based on Recurrent High Order Neural Networks which builds the state vector of the unknown plant dynamics and a learning adaptation law for the neural network weights for both the observer and identifier. These laws are obtained via control Lyapunov functions. Then, a control law, which stabilizes the tracking error dynamics is developed using the Lyapunov and the inverse optimal control methodologies . Tracking error boundedness is established as a function of design parameters.

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