Lateral robust iterative learning control for unmanned driving robot vehicle

In this paper, a robust adaptive iterative learning control (ILC) scheme is developed for the high-precision attitude tracking control of spacecraft in the presence of parametric uncertainties and external disturbances. The proposed robust adaptive ILC law consists of three parts, i.e., the classic proportional-derivative (PD) feedback control term, the PD-type feedforward learning term, and the robust term. The adaptive updating laws are designed for the gain matrices of both the classic PD feedback control term and the PD-type feedforward learning term. The asymptotic stability of the whole closed-loop system is proved through the Lyapunov function–based convergence analysis. The proposed robust adaptive ILC scheme can not only compensate for the parametric uncertainties and repetitive disturbance, but also handle the nonrepetitive disturbance owing to the robust control concept. Moreover, the proposed robust adaptive ILC scheme can achieve the fast convergence speed benefiting from the adaptive technique. Numerical simulations illustrate the effectiveness and superiority of the proposed ILC scheme.

Here the authors concerned with robust iterative learning control (ILC) for uncertain time-delay systems. They demonstrate that the design of a robust ILC is straightforward based on a performance index for the error system. The ILC algorithms under consideration are rather simple. They show that after the two-dimensional analysis of ILC, a Lyapunov-like approach can be used to directly obtain a stable algorithm that achieves monotonic convergence of the control input error. Sufficient stability conditions are provided in terms of linear matrix inequalities, which can determine learning gains as well. They also show that the Lyapunov-like approach can be applied to design robust ILC for uncertain systems with time-varying delay or multiple time delays. Numerical simulation results are presented to illustrate the effectiveness of the proposed ILC approach.

This paper addresses robust performance analysis and synthesis of lifted system iterative learning control (ILC). By applying the full block S-procedure, sufficient conditions for the robust performance of ILC with both unstructured and structured uncertainty are derived. In the synthesis problem, we consider the design of the learning filter Q for modelinversion based ILC. The problem is reformulated as a convex optimization problem such that the converged tracking error is minimized subject to the monotonic convergence condition. This synthesis approach enables full automation of the ILC design since neither an uncertainty model has to be identified nor a robustness filter has to be chosen. The advantages of this novel robust performance ILC technique are demonstrated on an experimental linear motor test setup.

In order to improve the steering performance of vehicles on low adhesion roads, this paper proposed a drift control method by coordinating the steering angle and the rear wheel slip ratio. Because lateral force of rear wheels is saturated with high longitudinal slip ratio under the drift condition, a vehicle dynamic model considering the coupling of the rear wheels’ longitudinal and lateral force is established. In view of the fact that the lateral tire force is affected by the nonlinearity of sideslip angle and slip ratio, the vehicle dynamic model is complex and makes it difficult for controller design. Due to this, a description method of the nonlinear relationship is presented for simple which introduces a virtual control quantity representing the influence of slip ratio on lateral tire forces based on the function of tire force with sideslip angle and slip ratio. Then, a sliding mode control method is proposed to ensure the stability of the closed-loop system and an off-line two-dimensional map is constructed to convert the virtual control quantity into the actual control quantity. The simulation results in the high-precision vehicle dynamics simulation software show that even if the initial state is far from the equilibrium point of drift, the drift controller also can ensure the stability of the closed-loop system and achieve a good drift performance.

An iterative learning control (ILC) framework based on two-degree-of-freedom control is presented for a linear plant with perturbations. A sufficient condition in the frequency domain is obtained for robust convergence with the bounded asymptotic error in the existence of input disturbance, output disturbance and errors in initial states. Based on the sufficient condition, the problem of designing ILC can be converted to a robust performance problem and a robust ILC against disturbances and initialization errors is derived by structured singular value and mu synthesis

Nonlinear dynamic modeling and model reduction strategy are studied in this paper for a rotating thin cylindrical shell. The nonlinear dynamic model is first established in terms of ordinary differential equations, in which the effects of Coriolis and centrifugal forces are considered, as well as the initial hoop tension due to rotation. This model describes both the in‐plane vibrations and the flexural vibration and reflects the coupling effects of those deformations. Based on this original model, a novel model reduction strategy is proposed to reduce the degrees of freedom by neglecting vibration modes predominated by in‐plane vibrations. Meanwhile, for the reduced‐order model, the in‐plane vibrations’ contributions to the rotating shell’s response are still preserved. To validate the dynamic model and the model reduction strategy, comparisons and simulations are carried out. Subsequently, nonlinear dynamic behaviors are investigated preliminarily by analyzing the rotating cylindrical shell’s amplitude‐frequency responses under different excitation levels.

This study first investigates robust iterative learning control (ILC) issue for a class of two‐dimensional linear discrete singular Fornasini–Marchesini systems (2‐D LDSFM) under iteration‐varying boundary states. Initially, using the singular value decomposition theory, an equivalent dynamical decomposition form of 2‐D LDSFM is derived. A simple P‐type ILC law is proposed such that the ILC tracking error can be driven into a residual range, the bound of which is relevant to the bound parameters of boundary states. Specially, while the boundary states of 2‐D LDSFM satisfy iteration‐invariant boundary states, accurate tracking on 2‐D desired surface trajectory can be accomplished by using 2‐D linear inequality theory. In addition, extension to 2‐D LDSFM without direct transmission from inputs to outputs is presented. A numerical example is used to illustrate the effectiveness and feasibility of the designed ILC law.

An iterative learning-based adaptive disturbance rejection control (ADRC) method is proposed to tackle control issues for three-dimensional (3D) overhead crane. The tracking differentiator (TD) and extended state observer (ESO) are employed to extract desired displacement and estimate system disturbances. A robust iterative learning control (ILC) law is constructed by integrating sliding mode control (SMC) with ILC. The stability of the closed-loop system is proved through Lyapunov stability theory, and simulations are conducted to verify the effectiveness and robustness of the proposed method.

In this paper, an integrated tube robust iterative learning model predictive control (Tube‐RILMPC) strategy based on the dynamic partial least squares (DyPLS) identified algorithm is proposed. The problems of large amount of online data calculation and input and output variable dimensions disaster in the original variable space are solved. This integrated Tube‐RILMPC strategy enhances the tracking performance and robustness of two‐dimensional (2D) control system. The output trajectories of system are located in the tube the nominal trajectory, which reduces the modelling error caused by the uncertain model. Based on the worst‐case performance index of ellipsoidal uncertainty and polytopic uncertainty, a robust iterative learning control (ILC) strategy is designed. Finally, the superiority of the proposed control algorithm is verified by comparative simulation.

In this paper is proposed a robust higher-order iterative learning control (ILC) algorithm for discrete-time systems. In contrast to conventional discrete-time learning methods, the proposed learning algorithm is constructed based on both time-domain performance and iteration-domain performance. Also, the proposed learning algorithm use more than one past error in the iteration-domain. It is proved that the proposed method has robustness in the presence of external disturbances and, in absence of all disturbances, the convergence of the proposed learning algorithm is guaranteed. A numerical example is given to show the robustness in the presence of state disturbance and convergence property according to parameters change.

A Robust Iterative Learning Control with Neural Networks for Robot

SummaryA new robust iterative learning control scheme is presented for state tracking control of nonlinear MIMO systems. The main characteristic of the proposed controller lies in its ability to deal with unstructured uncertainties that are norm‐bounded but not globally or locally Lipschitz continuous as usual. The classical resetting condition of iterative learning control is removed and replaced with more practical alignment condition. The class of systems to be considered is further extended to more general scenarios, in which input distribution uncertainties are included. In the end, an illustrative example is presented to demonstrate the efficacy of the proposed control scheme. Copyright © 2015 John Wiley & Sons, Ltd.

In practical applications, the durations of operations in uncertain batch processes may fluctuate due to security considerations, physical constraints, and environmental changes. Regarding this issue, this paper addresses iterative learning control (ILC) for a class of uncertain single‐input single‐output (SISO) batch processes with nonuniform batch lengths. The aim is to develop an optimization‐based ILC scheme that guarantees robustness while meeting input constraints requirements. Two robust optimal ILC algorithms resting upon the modified error update model are proposed for the nonuniform batch length problem, where the linear matrix inequality (LMI) optimization techniques are employed to deal with polytopic and ellipsoidal model uncertainties. Hence, the ILC design requirements are reduced to a convex optimization method involving LMIs, which results in iterative input update signals at the end of each batch. Also, the theoretical analyses of the proposed algorithms are presented. Finally, two numerical cases for batch processes are simulated to testify the feasibility and performance of the proposed methods.

This paper considers the robust convergence problem of iterative learning control (ILC) for discrete-time systems in the presence of iteration-varying uncertain plant parameters. If the uncertain parameters all converge along the iteration axis, then the perfect tracking property of ILC can be achieved under certain conditions. Otherwise, the tracking objective of ILC can be achieved within a bound if some of the uncertain parameters are bounded, rather than convergent, along the iteration axis.

Because of the complexity of vehicle steering motion, the classical linear two degrees of freedom lateral dynamics model of vehicle has been unable to meet well the research needs of the active steering control system. In order to make better use of the chassis control technology to improve the safety when vehicle turning and the vehicle-handling stability at high speeds, it is necessary to establish a more complex new dynamic model which not only fully reflect the steering characteristics of the vehicle, but also meet the needs of the control system design. In this paper, the active four-wheel steering vehicle is regarded as the research object; the modeling method of a eight degrees of freedom nonlinear dynamics model of this active four-wheel steering vehicle is discussed based on the comprehensive analysis of kinetic and kinematic; the differential equations for each degree of freedom of motion are derived step by step; and the modeling and solving methods are also illustrated by means of the Simulink software. In addition, the model accuracy is also verified effectively by contrast with ADAMS vehicle multi-body dynamics model. Verification results show that the established model has a relatively high accuracy and can be applied to the research and analysis for different active chassis control systems of the vehicle.