Liqian Dou

Parameters selection for SVR based on PSO

Support vector machine (SVM) has recently emerged as a powerful technique for solving problems in pattern classification and regression, but its performance mainly depends on the parameters selection of it. Parameters selection for SVM is very complex in nature and quite hard to solve by conventional optimization techniques, which constrains its application to some degree. PSO, as an evolutionary computing technology, has been applied successfully to various optimization problems, but has some disadvantage. So in this paper PSO is modified by added certain particles at each iterative to broaden search area, which makes particles free of local optimization. A new methodology for parameters selection of support vector regression is proposed, based on the modified PSO tuning algorithm. The methodology is used to model nonlinear dynamical system in simulation, and the simulation result assures the validity of it, not only on time but also on model accuracy

Elevator Group Control Scheduling Approach Based on Multi-Agent Coordination *

Elevator group control scheduling problem is considered using the approach of multi-agent coordination. Unlike traditional elevator group control scheduling approaches that were implemented by a central agent, this scheduling proposed in this paper was implemented by many elevator agents, and every agent is able to calculate, reason and decide. Based on this, the structure of single elevator agent and the structure of system are designed, and coordination mechanism is then defined. Finally, in computer, the algorithm is simulated and compared with other scheduling algorithms. The results show the approach proposed in the paper can reduce effectively the average waiting time of passengers and rate of waiting long time of passengers, and it indicates an excellent applicability at most of traffic flow pattern

Aeroservoelastic modeling and analysis of a six-DOF hypersonic flight vehicle:

By using light-weighted material in hypersonic flight vehicle, the vehicle body can be easily deformed. The mutual couplings in aerodynamics, flexible structure and control system will result in aeroservoelasticity problem. Based on the six-DOF geometry of a hypersonic flight vehicle, this paper uses assumed modes method, Shock-Expansion theory and Piston theory to develop the structural elastic model and the aerodynamic model, with actuator incorporated, the open-loop aeroservoelastic model of a six-DOF HFV is established. In open-loop analysis, the response of the elastic coordinates to the tail rudder deflection and the frequency characteristic of the transfer function of the tail rudder deflection to the first elastic coordinate are studied. A high gain feedback controller is designed for the aeroservoelastic model, and closed-loop analysis is conducted based on this model. The simulations show the influence of the elastic motion on the aerodynamic forces and moments.

Self-adaptive multi-objective optimization method design based on agent reinforcement learning for elevator group control systems

This paper study the multi-objective optimization problem of elevator group control systems by using the Markov Decision Process model. Define the Agent to be the leaner and decision-maker of the MDP model. And then using reinforcement learning Algorithm combined with generic method defines the elements of this model. Moreover we use SARSA(λ ) value iteration algorithm which was selected to iterative estimation the utility function combined with tile coding function approximation to design an optimization algorithm, and then prove that the solution of this algorithm will converges to a bounded domain which is given in this paper. The effect for dynamic optimization objective function of proposed approach was validated by virtual simulation environment of elevator group control systems.

Multi-model switching control of hypersonic vehicle with variable scramjet inlet based on adaptive neural network

The variable-geometry scramjet inlet can improve the aerodynamic performance of hypersonic vehicle by moving the movable lip to capture more airflow, which causes the uncertainties of model structures and parameters in the meanwhile. This paper presents a multi-model switching control system to tackle the strong nonlinearity and interaction characteristics. According to the working conditions, we build several nonlinear aerodynamic models with different lengths of the movable lip. For each model, a specific neural network controller has been adopted. By constructing the common Lyapunov function, it is proved that all signals of the closed-looped system are uniformly ultimatedly bounded by the continuous controller. Numerical simulations are presented to verify the feasibility of the proposed control approach.

A mixed robust optimization and multi-agent coordination method for elevator group control scheduling

An elevator group control scheduling (EGCS) method, which is combined with robust optimization (RO) and multi-agents, is proposed to deal with the uncertainty with the elevator traffic flow and the computation complexity. Considering the uncertainty of elevator traffic flow, the elevator schedule model based on RO is developed. An integrated method combined with multi-agent coordination (MAC) and RO schedule is designed to compensate for the disadvantage of RO for the EGCS problem. Simulation results show that the proposed method can improve the performance and adaptability of the EGCS.

Excitation signal design for closed-loop system identification

An iterative optimal excitation signal design method is presented to select an optimal signal for closed-loop system identification. The control performance criteria is used as the criteria for optimizing excitation signal, and the robust stability of closed-loop identification and power spectrum constraints for excitation signal, control signal and output signal are considered. Based on these, the paper formulize optimal excitation signal design problem and solve power spectrum of excitation signal. Finally, an iterative method is used to solve optimal excitation signal and identify system model. Simulation result shows that model quality by closed-loop identification is improved by using excitation signal optimal method proposed in this paper.

Ascent phase trajectory optimization for aircraft based on Nonlinear Programming

Ascent phase minimum time-to-climb trajectory optimization problem(TOP) is studied in the paper. To avoid the drawback of indirect methods and traditional direct methods, a new optimal control method-Gauss Pseudospectral Method(GPM) was used to transform the trajectory optimization problem into Nonlinear Program(NLP) problem. A initial guess strategy was proposed to improve the convergence rate of NLP problem, based on the guess, which can be solved using a sparse nonlinear optimization software named SNOPT. The optimality of the solution was verified via checking up the Hamiltonian function. Finally, simulation results demonstrate the efficiency of the method.

An iterative identification and control design method based on ν-gap metric

An iterative identification and control design method based on nu-gap is proposed for ensuring the stability of closed-loop system and control performance improvement. The method use the last designed controller to control the real plant, and to identify an uncertain model set that contains the real system. And then, redesign controller stabilizes the model set. At the same time, iterative closed-loop performance improvement condition and controller design method are proposed. Simulation result shows that the proposed method is available.

Fault Detection of Distributed Networked Control Systems with Access Constraints

Abstract This paper considers the fault detection problem of distributed networked control systems (DNCS) with limited data transmission rate. In order to deal with the limited bandwidth of the network, two steps are taken. The first one is the periodic communication sequence which is introduced to the two-level DNCS to ensure that only some specified subsystems rather than all of them are connected to the central fault diagnosis unit at a certain time; the second one is that the signals which are transmitted to the central unit from each subsystem are not the inputs and outputs but the residuals which are smaller. Because periodic communication sequences introduction changes the observability of the system, a theorem is provided to discuss the observability of DNCS as well as to give a new system model under observable condition. On the basis of residuals transmitted from subsystems to the central unit, some steps are taken to get the inputs and outputs which are necessary for fault detection. Then, according to the new system model and obtained inputs and outputs, the central fault diagnosis unit based on the periodic system theory is designed under this communication pattern. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method.