汽车转向/防抱死制动系统的无模型协同控制

汽车制动系统和转向系统相互之间存在着复杂的耦合关系,会对汽车行驶安全性和操纵稳定性造成极大的影响。为了动态补偿这种干扰影响,以无模型控制方法设计汽车整车防抱死制动控制器、整车前轮主动转向控制器及转向系统和制动系统的协同控制器,从理论上证明了设计的无模型控制系统的稳定性。最后,在MATLAB/Simulink平台上搭建了车辆模型和控制器,进行汽车转向制动控制的动态性能仿真。仿真结果表明其解决了汽车两个系统的耦合干扰,提高了汽车制动效能和转向稳定性。     

Estimation of road frictional force and wheel slip for effective antilock braking system (ABS) control

The introduction of electric braking via brake‐by‐wire systems in electric vehicles) has reduced the high transportation delays usually involved in conventional friction braking systems. This has facilitated the design of more efficient and advanced control schemes for antilock braking systems (ABSs). However, accurate estimation of the tire‐road friction coefficient, which cannot be measured directly, is required. This paper presents a review of existing estimation methods, focusing on sliding‐mode techniques, followed by the development of a novel friction estimation technique, which is used to design an efficient ABS control system. This is a novel slip‐based estimation method, which accommodates the coupling between the vehicle dynamics, wheel dynamics, and suspension dynamics in a cascaded structure. A higher‐order sliding‐mode observer–based scheme is designed, considering the nonlinear relationship between friction and slip. A first‐order sliding‐mode observer is also designed based on a purely linear relationship. A key feature of the proposed estimation schemes is the inclusion of road slope and the effective radius of the tire as an estimated state. These parameters impact significantly on the accuracy of slip and friction estimation. The performance of the proposed estimation schemes are validated and benchmarked against a Kalman filter (KF) by a series of simulation tests. It is demonstrated that the sliding‐mode observer paradigm is an important tool in developing the next generation ABS systems for electric vehicles.     

Adaptive neuro-fuzzy wheel slip control

Due to complex and nonlinear dynamics of a braking process and complexity in the tire–road interaction, the control of automotive braking systems performance simultaneously with the wheel slip represents a challenging problem. The non-optimal wheel slip level during braking, causing inability to achieve the desired tire–road friction force strongly influences the braking distance. In addition, steerability and maneuverability of the vehicle could be disturbed. In this paper, an active neuro-fuzzy approach has been developed for improving the wheel slip control in the longitudinal direction of the commercial vehicle. The dynamic neural network has been used for prediction and an adaptive control of the brake actuation pressure, during each braking cycle, according to the identified maximum adhesion coefficient between the wheel and road surface. The brake actuation pressure was dynamically adjusted on the level that provides the optimal level of the longitudinal wheel slip vs. the brake pressure selected by driver, the current vehicle speed, the brake interface temperature, vehicle load conditions, and the current value of longitudinal wheel slip. Thus the dynamic neural network model operates (learn, generalize and predict) on-line during each braking cycle, fuzzy logic has been integrated with the neural model as a support to the neural controller control actions in the case when prediction error of the dynamic neural model reached the predefined value. The hybrid control approach presented here provided intelligent dynamic model – based control of the brake actuation pressure in order to keep the longitudinal wheel slip on the optimum level during a braking cycle.     

Existence and stability of limit cycles in control of anti-lock braking systems with two boundaries via perturbation theory

This paper presents a two-phase control logic for anti-lock braking systems (ABS). ABS are by now a standard component in every modern car, preventing the wheels from going into a lock situation where the wheels are fixed by the brake and the stopping distances are greatly prolonged. There are different approaches to such control logics. An ABS design proposed in recent literature controls the wheel's slip by creating stable limit cycles in the corresponding phase space. This design is modified via an analytical approach that is derived from perturbation theory. Simulation results document shorter braking distance compared to available tests in the literature.     

Existence,stability and robustness analysis of limit cycles in hybrid anti-lock braking systems

We investigate the stability and robustness properties of anti-lock braking systems (ABS) based on actuators with on/off dynamics. Namely, we propose a hybrid ABS controller which gives rise to an asymptotically stable limit cycle on the wheel slip. The proposed approach allows to derive exact information on the maximum allowable uncertainty in the measured variables which guarantee the cycle stability. Moreover, a structural stability analysis is performed with respect to different road conditions and to the actuator rate limit.     

纯电动智能汽车制动防抱死系统控制逻辑研究

线控制动技术(Brake-by-Wire)是纯电动智能汽车的关键技术之一.为了提高智能驾驶汽车制动的安全稳定性,需要针对一款纯电动智能汽车的制动防抱死系统控制逻辑进行研究.在该方案实施前,设置合理的逻辑门限值,通过Stateflow进行仿真实验.基于Simulink搭建了8自由度的车辆动力学模型,对弯道制动工况进行仿真...     

Optimal control using an algebraic method for control-affine non-linear systems

A deterministic optimal control problem is solved for a control-affine non-linear system with a non-quadratic cost function. We algebraically solve the Hamilton–Jacobi equation for the gradient of the value function. This eliminates the need to explicitly solve the solution of a Hamilton–Jacobi partial differential equation. We interpret the value function in terms of the control Lyapunov function. Then we provide the stabilizing controller and the stability margins. Furthermore, we derive an optimal controller for a control-affine non-linear system using the state dependent Riccati equation (SDRE) method; this method gives a similar optimal controller as the controller from the algebraic method. We also find the optimal controller when the cost function is the exponential-of-integral case, which is known as risk-sensitive (RS) control. Finally, we show that SDRE and RS methods give equivalent optimal controllers for non-linear deterministic systems. Examples demonstrate the proposed methods.     

Newton method based iterative learning control for discrete non-linear systems

Significant progress has been achieved in terms of both theory and industrial applications of iterative learning control (ILC) in the past decade. However, the techniques of solving non-linear ILC problems are still under development. The main result of this paper is a novel non-linear ILC algorithm that utilizes the capability of the Newton method. By setting up links between non-linear ILC problems and non-linear multivariable equations, the Newton method is introduced into the ILC framework. The implementation of the new algorithm allows one to decompose a nonlinear ILC problem into a sequence of linear time-varying ILC problems. Simulations on a discrete non-linear system and a manipulator model display its advantages. Conditions for its semi-local convergence are analysed. Links of ILC with existing non-linear topics are pointed out as ways to construct new non-linear ILC schemes. Potential improvements are discussed for future work.     

Neural-network hybrid control for antilock braking systems

The antilock braking systems are designed to maximize wheel traction by preventing the wheels from locking during braking, while also maintaining adequate vehicle steerability; however, the performance is often degraded under harsh road conditions. In this paper, a hybrid control system with a recurrent neural network (RNN) observer is developed for antilock braking systems. This hybrid control system is comprised of an ideal controller and a compensation controller. The ideal controller, containing an RNN uncertainty observer, is the principal controller; and the compensation controller is a compensator for the difference between the system uncertainty and the estimated uncertainty. Since for dynamic response the RNN has capabilities superior to the feedforward NN, it is utilized for the uncertainty observer. The Taylor linearization technique is employed to increase the learning ability of the RNN. In addition, the on-line parameter adaptation laws are derived based on a Lyapunov function, so the stability of the system can be guaranteed. Simulations are performed to demonstrate the effectiveness of the proposed NN hybrid control system for antilock braking control under various road conditions.     

State feedback for non-linear control systems

A theory of static state feedback for non-linear discrete-time systems is developed. The theory applies to non-linear systems possessing a recursive representation of the form x _k+1 =?(x _k , u_k ), where ? is a continuous function, and it deals with the construction of continuous state feedback functions that internally stabilize a given system. The theory yields an explicit method for the computation of stabilizing feedback functions, and several examples of the computation of such functions are provided.     

Feedback control for non-linear singular systems

An invertible non-linear map is developed, which transforms a non-linear singular system into a regular one and preserves local properties under the application of specific feedback control laws. The presented algorithm is easily implementable on a digital computer, since it is of closed form. An application of the above algorithm is presented on an autonomous vehicle.     

A parametric frequency response method for non-linear time-varying systems

A new parametric frequency response algorithm is introduced to investigate linear and non-linear dynamic systems with time-varying parameters. In the new algorithm the time-varying parameters are regarded as additional inputs of the systems and the non-linear generalised frequency response functions for multi-input-single-output systems are then employed to obtain Zadeh's system functions from a differential equation representation. The parametric frequency response method reveals how the time-varying parameters affect the behaviour of the systems through a time-varying term. The new method can be applied to both linear and non-linear time-varying systems.     

A synthesis of Lyapunov functions for non-linear time-varying control systems

The formulation of a class of Lyapunov functions for time-varying nonlinear control systems which occur in the stability analysis of control systems is considered. A new approach is presented, which is an extension of a generation technique for time-invariant non-linear systems. This approach, which uses by analogy the classical theory of Hamilton, permits stability and transient information to be obtained from system equations with time-varying damping as well as time-varying gain. A second and third-order example is used to illustrate the application of this new result.     

Convergence analysis of a computational method for optimal control of non-linear differential-algebraic systems

The convergence analysis of a computational method for optimal control problems of non-linear differential-algebraic systems is considered. The class of admissible controls is taken to be the class of piecewise smooth functions. A control parametrizution technique is used to approximate the optimal control problem into a sequence of optimal parameter selection problems. The solution of each of these approximate problems gives rise to a suboptimal solution to the original optimal control problem in an obvious way. The gradients of the cost functional with respect to parameters are derived. Furthermore, the error bounds between the suboptimal costs and the true optimal cost are derived.     

A fault tolerant control framework for periodic switched non-linear systems

An observer-based fault tolerant control (FTC) framework is proposed for a class of periodic switched non-linear systems (PSNS) without full state measurements. Two kinds of faults are considered: continuous faults that affect each mode during its dwell period; and discrete faults that affect the switching sequence. Under the average dwell time scheme, the proposed FTC framework can maintain the stability of overall PSNS in spite of these two kinds of fault. A switched reluctance motor example is taken to illustrate the efficiency of the proposed method.     

Relationships between several novel control schemes proposed for a class of non-linear stochastic systems

Several different novel controllers are proposed to stabilize a class of discrete-time non-linear stochaslic systems where the non-linearity involves the state and input of the system as well as a white-noise sequence vector. It is shown that the conditions of stabilizability by these controllers are closely related.     

Approximate model matching for non-linear control systems

Approximate model matching refers to the problem of controlling a non-linear system so as to achieve a response resembling that of a desirable model. The paper presents a family of recursive output feedback controllers that can achieve approximate model matching in all cases where it is possible. The design of these controllers depends on the solution of a set of algebraic inequalities.     

Partially observed non-linear risk-sensitive optimal stopping control for non-linear discrete-time systems

In this paper we introduce and solve the partially observed optimal stopping non-linear risk-sensitive stochastic control problem for discrete-time non-linear systems. The presented results are closely related to previous results for finite horizon partially observed risk-sensitive stochastic control problem. An information state approach is used and a new (three-way) separation principle established that leads to a forward dynamic programming equation and a backward dynamic programming inequality equation (both infinite dimensional). A verification theorem is given that establishes the optimal control and optimal stopping time. The risk-neutral optimal stopping stochastic control problem is also discussed.     

Design and experimental validation of a nonlinear wheel slip control algorithm

We present a new wheel slip controller and validate it both experimentally and in simulation. The control strategy is based on both wheel slip and wheel acceleration regulation, and ensures global asymptotic stability in closed loop. The stability analysis is established using tools for cascaded systems. This approach has the advantage of setting conditions on the control gains that are considerably milder, if compared with those obtained from Lyapunov’s direct method. Simulations are provided to illustrate the robustness against vertical load variations and uncertainties in tyre–road friction. Furthermore, tests on a tyre-in-the-loop facility show that with our control law the wheel slip converges precisely to the assigned reference.     

Optimal feedback control of non-linear systems

A method is proposed for computing optimal nonlinear feedback control laws. It is shown that the feedback loop satisfies a system of quasi-linear partial differential equations. This system is of first order when the dimensions of the state and the control vector are the same. Hereby, it degenerates into algebraic equations when the performance index does not depend on the control. These important results offer new ways for determining optimal feedback laws. Connections with the Volterra series and the Hamilton-Jacobi-Bellman equation are treated. Some examples are given to illustrate the advantages of this method.