Composite Learning Control With Application to Inverted Pendulums

Composite adaptive control (CAC) that integrates direct and indirect adaptive control techniques can achieve smaller tracking errors and faster parameter convergence compared with direct and indirect adaptive control techniques. However, the condition of persistent excitation (PE) still has to be satisfied to guarantee parameter convergence in CAC. This paper proposes a novel model reference composite learning control (MRCLC) strategy for a class of affine nonlinear systems with parametric uncertainties to guarantee parameter convergence without the PE condition. In the composite learning, an integral during a moving-time window is utilized to construct a prediction error, a linear filter is applied to alleviate the derivation of plant states, and both the tracking error and the prediction error are applied to update parametric estimates. It is proven that the closed-loop system achieves global exponential-like stability under interval excitation rather than PE of regression functions. The effectiveness of the proposed MRCLC has been verified by the application to an inverted pendulum control problem.Comment: 5 pages, 6 figures, conference submissio

Oedometric study of dredged marine soils admixed with sand for settlement reduction

Dredged marine soils (DMS) can be reused as fill materials for land reclamation project other than dump back to the open sea. However, in Malaysia, dredged marine soils were considered as a geowaste because of it has poor engineering properties. In the present study, dredged marine soils were excavated from the dredging works near the jetty of Kuala Perlis, Malaysia. To investigate the settlement reduction of DMS, a sand-mixed was used in this study and these results were compared with natural DMS (without sand). Oedometer test were conducted to calculate the consolidation properties of DMS and k-value can be obtained from the test. The test results showed that the dissipation of water from soils occurs faster in the sand-mixed compare to the control sample (without sand) due to the drainage path that have been reduced (two-way drainage)

Oedometric study of dredged marine soils admixed with sand for settlement reduction

Dredged marine soils (DMS) can be reused as fill materials for land reclamation project other than dump back to the open sea. However, in Malaysia, dredged marine soils were considered as a geowaste because of it has poor engineering properties. In the present study, dredged marine soils were excavated from the dredging works near the jetty of Kuala Perlis, Malaysia. To investigate the settlement reduction of DMS, a sand-mixed was used in this study and these results were compared with natural DMS (without sand). Oedometer test were conducted to calculate the consolidation properties of DMS and k-value can be obtained from the test. The test results showed that the dissipation of water from soils occurs faster in the sand-mixed compare to the control sample (without sand) due to the drainage path that have been reduced (two-way drainage)

On Reduced Input-Output Dynamic Mode Decomposition

The identification of reduced-order models from high-dimensional data is a challenging task, and even more so if the identified system should not only be suitable for a certain data set, but generally approximate the input-output behavior of the data source. In this work, we consider the input-output dynamic mode decomposition method for system identification. We compare excitation approaches for the data-driven identification process and describe an optimization-based stabilization strategy for the identified systems

Concurrent Learning Adaptive Model Predictive Control with Pseudospectral Implementation

This paper presents a control architecture in which a direct adaptive control technique is used within the model predictive control framework, using the concurrent learning based approach, to compensate for model uncertainties. At each time step, the control sequences and the parameter estimates are both used as the optimization arguments, thereby undermining the need for switching between the learning phase and the control phase, as is the case with hybrid-direct-indirect control architectures. The state derivatives are approximated using pseudospectral methods, which are vastly used for numerical optimal control problems. Theoretical results and numerical simulation examples are used to establish the effectiveness of the architecture.Comment: 21 pages, 13 figure

Data-driven adaptive model-based predictive control with application in wastewater systems

This study is concerned with the development of a new data-driven adaptive model-based predictive controller (MBPC) with input constraints. The proposed methods employ subspace identification technique and a singular value decomposition (SVD)-based optimisation strategy to formulate the control algorithm and incorporate the input constraints. Both direct adaptive model-based predictive controller (DAMBPC) and indirect adaptive model-based predictive controller (IAMBPC) are considered. In DAMBPC, the direct identification of controller parameters is desired to reduce the design effort and computational load while the IAMBPC involves a two-stage process of model identification and controller design. The former method only requires a single QR decomposition for obtaining the controller parameters and uses a receding horizon approach to process input/output data for the identification. A suboptimal SVD-based optimisation technique is proposed to incorporate the input constraints. The proposed techniques are implemented and tested on a fourth order non-linear model of a wastewater system. Simulation results are presented to compare the direct and indirect adaptive methods and to demonstrate the performance of the proposed algorithms