Recursive Least-squares Identification Method Research Articles

Research on Direct Lift Carrier-Based Unmanned Aerial Vehicle Landing Control Based on Performance Index Intelligent Optimization/Dynamic Optimal Allocation

High-precision control problems for carrier-based UAVs are challenging due to the requirements for safety, high-performance operation and uncertain ocean environments. To address such problems, this paper proposes a direct lift landing control method that can ensure the safe operation of UAVs using intelligent optimization of control performance indicators and dynamic optimal allocation methods. The direct lift control (DLC) scheme is adopted to improve the operability of the control by introducing flap channels to achieve the fast correction of vertical disturbances. To improve the landing control performance, an intelligent optimization method for DLC gain is proposed, and a neural network relationship between parameter uncertainty and optimal DLC gain is established. In addition, a recursive least-squares identification method is used to estimate the uncertain parameters in real time, so that the established neural network can be used to generate the optimal DLC commands online, and the generated control commands can be assigned to multiple actuators of the carrier-based UAV through a dynamic optimal assignment algorithm. Finally, the superiority of the method is verified using simulation comparison.

Two‐stage parameter estimation algorithms for Box–Jenkins systems

A two-stage recursive least-squares identification method and a two-stage multi-innovation stochastic gradient method are derived for Box-Jenkins (BJ) systems. The key is to decompose a BJ system into two subsystems, one containing the parameters of the system model and the other containing the parameters of the noise model, and then to estimate the parameters of the system model and the noise model, respectively. The simulation examples indicate that the proposed algorithms can generate highly accurate parameter estimates and require small computational burden.

Parameters Recursive Identification of Minimum Variance Control

we discuss the problem of parameters recursive identification and designing of optimal input signal for minimum variance control from the point of system identification. we propose multi-innovation recursive least-squares identification method and separable iterative recursive least-squares identification method to identify and estimate it on line. Finally, the efficiency and possibility of the proposed strategy can be confirmed by the simulation example results.

Numerical and experimental identifications of a motor-toggle mechanism

In this paper, the punching machine is made up by a toggle mechanism driven by a permanent magnet (PM) synchronous servomotor. First, Hamilton’s principle, Lagrange multiplier, geometric constraints and partitioning method are employed to derive its dynamic equations. The system parameters are difficult to obtain if the mechanism’s components cannot be taken apart. In this paper, the recursive least-squares (RLS) method is adopted to identify these parameters of the motor-toggle mechanism, which cannot be disassembled and their real values cannot be measured. From the comparison between the numerical simulations and experimental results, it is shown that the RLS identification method is feasible, and the parameters of links’ masses and friction coefficient are identified accurately.

Multivariate time-series model for suspended sediment concentration

Multivariate time-series models of suspended sediment concentration are presented. The suspended sediment dynamics are considered as an unknown system to be identified. The suspended sediment concentration profile and its relationship with current velocity profile are investigated. The model unknown parameter matrices of the models are identified based on an on-line recursive least-squares identification method. The simulation results based on the real data collected from the Rufiji Delta, Tanzania, show that this kind of model is well suited to predicting suspended sediment concentration and transportation of sediment.

Adaptive predictive control of dissolved oxygen concentration in a laboratory-scale bioreactor

We present an algorithm for the adaptive control of dissolved oxygen concentration in a bioreactor, based on the agitation rate. The dynamics are represented by an incremental first-order model with variable dead-time and parameters. These are estimated on-line by a recursive least-squares identification method with a forgetting factor and a constant sensitivity. The model is employed to predict the behaviour of the dissolved oxygen concentration over a finite horizon, using an original method which requires little computation. Then, a Generalized Predictive Control optimisation strategy computes the agitation rate from the predictions and the desired set point, while gradually updating the controller smoothness. This algorithm, which requires little preliminary knowledge, has been implemented on a laboratory-scale fed-batch bioreactor for which the use of conventional controllers showed limited performance, due to the unpredictable and evolutive nature of the dynamics. The new controller proved to be robust and effective over a wide range of operating conditions, while requiring no operator adjustments.