Run Pei

A new nonlinear model predictive control scheme for discrete-time system based on sliding mode control

Nonlinear model predictive control, which is conceptually similar to its linear counterpart except that nonlinear dynamic model is used for process prediction and optimization, is a developing control field. The proposed nonlinear model predictive control scheme, which is called nonlinear sliding mode model predictive control, integrates model predictive control and sliding mode variable structure control. A feasible dual-mode control scheme is presented in the paper, sliding mode predictive controller is implemented while the system state is outside the terminal region, besides sliding mode variable structure control designed off-line is used. The resulting control scheme has strong points of the two control methods. By predicting the pre-designed switching function, the predictive control sequence can be found by solving constrained open-loop optimal control problem, and the current control is implemented. At next sampling time the optimizing procedure is repeated. By constraining terminal sliding mode with an inequality, the state is steered into the predesigned sliding mode region, then the closed-loop stability is proved, and furthermore the system performance is analyzed. With some conditions, the method can be extended to a quasi-infinite horizon formulation. Simulation results show that less calculation is needed.

Estimation of vehicle speed and friction force using moving horizon strategy

A moving horizon estimation (MHE) approach to vehicle speed and tire-road friction force estimation is presented in this paper. The proposed method is an optimal strategy that explicitly allows for nonlinear and inequality constraints. The Kalman filter and moving horizon estimation are established to handle abrupt wheel skid and poor signal-to-noise sensor data. A distributed model of friction force is used to calculate the friction force. The simulation results show that the accurate estimate of the vehicle speed can be obtained even under heavy skid condition, and corrupted sensor noise. Additional, the MHE strategy proposed in this paper can handle the constraints effectively and avoid the fallacious estimate results.

Sliding mode model predictive control with terminal constraints

A new model predictive control scheme is proposed, which combines the receding horizon control and sliding mode variable structure control. By predicting the system states, the pre-designed switching function is predicted, then the control law can be found by solving the constrained open-loop optimal control problem based on the given cost function. The current control is implemented, and at the next sampling time the optimizing procedure is repeated. The proposed scheme has advantage of receding horizon control and sliding mode variable structure control, can deal with constraints online, and has strong robustness on the sliding surface. Furthermore, by constraining terminal sliding mode to be zero, the stability of closed-loop systems is guaranteed. Another scheme, which relaxes equality constraints to inequality constraints, is suggested for less calculation. In addition, the proposed method can be extended to nonlinear systems, and a nonlinear sliding mode predictive control scheme is acquired.

Safe approaching to non-cooperative spacecraft using potential function guidance based fuzzy logic system

A guidance control method is proposed for the mission of autonomous rendezvous and docking with non-cooperative target spacecraft. Firstly, the relative motion equations of state are described in a line of sight coordinate frame. Then artificial potential function guidance is used for chaser to approach non-cooperative target. Moreover, this guidance method is based on fuzzy logic system which satisfies safe approaching constraints in final approaching corridor scenario between the two spacecrafts. Stability of the proposed method is analyzed by using Lyapunov theory. Results obtained from numerical simulation studies demonstrate the validity of the method formulated in this paper.

The Design of RBF Neural Networks for Solving Overfitting Problem

One of the biggest problems in designing or training RBF neural networks are the overfitting problem. The traditional design of RBF neural networks may be pursued in a variety of ways. In this paper, we present a method for the design of RBF networks to solve overfitting problem. For a practical application, frequency information is usually available for designing RBF networks by frequency domain analysis, which has a sound mathematical basis. We try to include the frequency information into the design of RBF networks, which achieve the task of approximated a function in certain frequency range and have the property of structural risk minimization. After the structure of designed network is determined, the linear weights of the output layer are the only set of adjustable parameters. The approach of design is verified by approximation cases

A new algorithm of fuzzy on-line identification for nonlinear systems

A new fuzzy on-line identification algorithm for a nonlinear dynamic system is presented. This method combines the conventional on-line identification with fuzzy logic system. The nonlinear system is approximated by a set of fuzzy rules that describe the local linear dynamic in each subspace formed by fuzzifying the input and output space. The fuzzy space of input variables is partitioned by means of diagonal ANFIS method, the consequent parameters of the fuzzy rules are identified by means of weighted recursive least squares algorithm (WRLSA). The simulation results demonstrate that the proposed method is practical and efficient.

State Feedback Control Strategy Based on Ellipsoid for Piecewise Affine System with Time-Delay

This paper deals with a discrete-time piecewise affine (PWA) systems with time-delay. The PWA systems is described as ellipsoid which can be characterized by a set of vector inequalities, thereby the constraint of linear matrix inequalities (LMIs) is released. In terms of LMIs, the time-delay PWA system can be stabilized in Lyapunov theory. A convex optimization problem with LMIs constraints is formulated to design the controller. The simulation results verify the effectiveness of the proposed method.

Model Predictive Control of Piecewise Affine Systems with Constrained Input and Terminal Ellipsoid

In this paper, a model predictive control is proposed for the hybrid systems with constrained control input based on piecewise affine (PWA) model. The constrained control input of the considered systems is solved in terms of linear matrix inequalities (LMIs). An additional terminal ellipsoid is introduced to ensure the states converging to the equilibrium faster. The control law is obtained by convex optimization involving LMIs. The simulation results verify the effectiveness of the proposed method. It is shown that, based on LMI constraints, it is easy to get the model predictive control for the hybrid systems and is suitable for practical application

Input constrained discrete time-delay system robust H∞ control with invariant set

Based on the robust invariant set, Hinfin controller designing problem for uncertain discrete time-delay system with disturbance and input constraint is considered in this paper. At first, a state feedback controller was designed, when the disturbance belongs to a convex polytope, a control invariant set was derived based on the revised Lyapunov Krasovskii function, and inside this control invariant set, the state feedback controller ensure that the input constraint will not be violated and at the same time, systempsilas disturbance rejection performance will be bounded by minimization of the Hinfin norm bound. The LMI optimization approach is applied to solve the controller designing requirements. Numerical simulations results show that our proposed methods have desired control and performance requirements.

State feedback controller design for a class of hybrid systems with time-delay under input constraints

In this paper, we present an algorithm to design a state feedback controller for a class of hybrid systems with time-delay under input constraints to guarantee the asymptotic stability. First, the class of hybrid systems is modeled as a switched system with time-delay. Then, in succession algorithm is proposed which hinges on the use of common Lyapunov function and piecewise quadratic (PWQ) Lyapunov functions that can be computed as the solution of a set of linear matrix inequalities (LMIs). Furthermore, taking into account the constraints on the control action, the state feedback controller is synthesized. Finally, a numerical example is given to demonstrate th e effectiveness of the proposed approaches