Design of a linear motor-based shaker rig for testing driver's perceived ride comfort

In autonomous vehicles the driver will become a passenger. By ensuring a high level of ride comfort through active suspensions, the driver's ability to perform various tasks such as reading, drawing and texting can be enhanced. A high-performance shaker rig can conveniently be used to test the ride comfort improvement by means of various active suspensions under laboratory conditions. The paper deals with the design of such a rig, which employs a linear electric servomotor to impose accurately controlled vertical vibrations of driver seat. The reference time profiles of seat acceleration, velocity and displacement are generated off-line by using a half-car vehicle model and LQR control with a road preview option. The selection of linear motor comes from assessment of three characteristic shaker rig drive designs (hydraulic and two electric ones). To ensure high-precision seat motion, the proposed shaker control system includes: acceleration feedforward and feedback control loops, a state controller-like compensator of accelerometer offset to prevent drift of seat position and velocity, and feedforward compensation of linear motor cogging force mode. The designed shaker rig application is demonstrated through a case study related to ride comfort evaluation of various active suspension configurations and a drawing task. The drawing task results are employed to determine the root-mean-square vertical seat acceleration threshold, below which the active suspension drawing task performance remains similar to that obtained under standstill conditions.     

Model reference adaptive control of a soft bending actuator with input constraints and parametric uncertainties

In this article, model reference adaptive control of a pneumatically actuated soft robot has been studied in detail. To deal with the effects of system uncertainties, in the proposed control scheme, parametric uncertainties and input constraints are taken into account. To design such a controller, based on experimental analysis, the robot has been modeled as a second-order Linear Parameter Varying (LPV) system. Then, the dominant dynamics are presented as a Linear Time-Invariant (LTI) system, while uni-directional input constraint has been considered as a critical issue in the control scheme design. Furthermore, to compensate parametric uncertainties as well as unmodeled dynamics, adaptive laws are modified. Finally, the effectiveness is studied in different scenarios on an experimental platform to validate our claims. Moreover, to show the proposed approach capabilities and performance, the proposed controller has been compared with a PID and a recent sophisticated robust-adaptive controller, which presented a new formulation to achieve a better tracking performance with guaranteed stability in the presence of different constraints and unmodeled dynamics.     

Nonlinear adaptive torque control of electro-hydraulic load system with external active motion disturbance

This paper develops a high performance nonlinear adaptive control method for electro-hydraulic load simulator (EHLS). The tracking performance of EHLS is mainly affected by the following factors: actuator’s active motion disturbance, flow nonlinear and parametric uncertainties, etc. Most previous studies on EHLS pay too much attention on actuator’s active motion disturbance, while deemphasize the other two factors. This paper concerns EHLS as a motion loading system. Besides actuator’s motion disturbance, both the nonlinear characteristics and parametric uncertainties of the loading system are addressed by the present controller. First, the nonlinear model of EHLS is developed, and then a Lyapunov-based control algorithm augmented with parameters update law is developed using back-stepping design method. The stability of the developed control algorithm is proven via Lyapunov analysis. Both the co-simulation and experiment are performed to validate the effectiveness of the developed algorithm.     

Nonlinear tracking control based on extended state observer for vehicle active suspensions with performance constraints

In this paper, a nonlinear tracking control strategy with extended state observer (ESO) is presented for vehicle active suspensions to improve the ride comfort, where suspension spaces, dynamic tire loads are considered as time-domain constraints to be guaranteed. The unique characteristic of the proposed approach lies in the independence on accurate mathematical model. More exactly, the unknown dynamics and external disturbances of the vehicle suspension are regarded as an augmented state of the system and are estimated using the designed ESO. The stability analysis shows that both the estimation error and the tracking error of the control output are bounded and that the upper bounds of the errors monotonously decrease with the increase of the observer bandwidth. Finally, a competitive experiment on a quarter-car suspension prototype is given to demonstrate the effectiveness of the proposed control schemes.     

Discrete-time frequency-shaping parametric LQ control withapplication to active seat suspension control

The theory of a discrete-time parametric linear quadratic (PLQ) control is extended to a class of frequency-shaped performance measures. The incorporation of frequency-dependent weighting matrices allows the emphasis or de-emphasis of the importance of the system variables being penalized over specific bands of frequencies. Results are presented for constant-gain and dynamic output feedback configurations of frequency-shaping optimal control. The resultant control is applied to the design of active seat suspension control. The active suspension maximizes ride comfort by discriminatory minimization of average whole-body absorbed power over a band of frequencies that causes the most discomfort to a human being     

Adaptive sliding controller with self-tuning fuzzy compensation for vehicle suspension control

Since the hydraulic actuating suspension system has nonlinear and time-varying behavior, it is difficult to establish an accurate dynamic model for a model-based sliding mode control design. Here, a novel model-free adaptive sliding controller is proposed to suppress the position oscillation of the sprung mass in response to road surface variation. This control strategy employs the functional approximation technique to establish the unknown function for releasing the model-based requirement. In addition, a fuzzy scheme with online learning ability is introduced to compensate the functional approximation error for improving the control performance and reducing the implementation difficulty. The important advantages of this approach are to achieve the sliding mode controller design without the system dynamic model requirement and release the trial-and-error work of selecting approximation function. The update laws for the coefficients of the Fourier series functions and the fuzzy tuning parameters are derived from a Lyapunov function to guarantee the control system stability. The experimental results show that the proposed control scheme effectively suppresses the oscillation amplitude of the vehicle sprung mass corresponding to the road surface variation and external uncertainties, and the control performance is better than that of a traditional model-based sliding mode controller.     

H<Subscript>∞</Subscript> control of multiple model subject to actuator saturation: application to quarter-car suspension system

This paper deals with the H_∞ control of nonlinear systems in multiple model representation subject to actuator saturation. An application to Quarter-Car suspension system under actuator saturation is then given using the multiple model approach. The concept of so-called parallel distributed compensation (PDC) is employed for designing control system. The idea of this controller consists in designing a linear feedback control for each local linear model. To address the input saturation problem in this paper, both constrained and saturated controls input cases are proposed. In the two cases, H_∞ stabilization conditions in the sense of Lyapunov method are derived. Moreover, a controller design with larger attraction domain is formulated and solved as a linear matrix inequality (LMI) optimization problem. Our simulation results show that both the saturated and constrained controls can stabilize the resulting closed-loop suspension system and eliminate the effect of external disturbances. Indeed, the main roles of car suspension systems, which consist on improving ride comfort of passengers and the road holding capacity of the vehicle, are achieved.     

Steering control of automated vehicles using absolute positioning GPS and magnetic markers

Steering control for passenger cars on automated highways is analyzed. The feasibility of an automatic steering system based on absolute positioning global positioning system (GPS) and a magnetic marker guidance system has been evaluated using computer simulations. State estimation and control algorithm issues are examined for such a control system. By use of GPS and a magnetic marker sensor, an accurate and real-time estimation of the vehicle's lateral displacements with respect to the road can be accomplished. A steering control algorithm based on road curvature preview for achieving good road tracking and providing ride comfort is also presented. The proposed estimation and control system are validated by simulation results.     

Semiactive Suspension Control of a Light Commercial Vehicle

This paper presents the design and implementation of a semi-active suspension control system for a light commercial vehicle using continuously varying dampers, accelerometers, and an onboard processor. The control algorithms such as sky-hook, ground-hook, and hybrid are designed based on the vertical velocities of each quarter of the car. These velocities are estimated from Kalman filter using quarter car vehicle model. The controllers are implemented in an actual vehicle equipped with the developed semiactive suspensions system and their performance are compared. Sky-hook control improved ride comfort by reducing body accelerations in the 1-3 Hz range, ground-hook control improved road holding by reducing wheel accelerations in the 10-15 Hz range, and hybrid control results were in between the sky-hook and ground-hook results. The main contribution of this paper is the successful implementation of the semiactive suspension control strategies on an actual vehicle with accompanying experimental results.     

Robust Stabilization of Complex Switched Networks With Parametric Uncertainties and Delays Via Impulsive Control

In this paper, a general complex switched network (CSN) model is presented. The model is more general than those in the literatures in which it contains switching behaviors on both its nodes and topology configuration. Robust stabilization of directed time-varying CSN with parametric uncertainties and two types of delays is investigated. The two types of delays consist of the system delay at each node and the coupling delay between nodes. Based on the Lyapunov stability theory, sufficient robust stabilization conditions are proposed for CSNs via impulsive control. In addition, four special stabilization cases: CSNs with both system and coupling delays, CSNs with parametric uncertainties and either the system delay or the coupling delay, and complex networks with parametric uncertainties and both type of delays, are discussed. A systematic design procedure for stabilizing impulsive control is presented. A numerical example is provided for illustration. A comparative study of the stability ranges of the impulsive intervals corresponding to the general case of the directed time-varying CSN and the four special cases is carried out by simulation.     

Robust fuzzy neural network sliding mode control scheme for IPMSM drives

This article proposes a robust fuzzy neural network sliding mode control (FNNSMC) law for interior permanent magnet synchronous motor (IPMSM) drives. The proposed control strategy not only guarantees accurate and fast command speed tracking but also it ensures the robustness to system uncertainties and sudden speed and load changes. The proposed speed controller encompasses three control terms: a decoupling control term which compensates for nonlinear coupling factors using nominal parameters, a fuzzy neural network (FNN) control term which approximates the ideal control components and a sliding mode control (SMC) term which is proposed to compensate for the errors of that approximation. Next, an online FNN training methodology, which is developed using the Lyapunov stability theorem and the gradient descent method, is proposed to enhance the learning capability of the FNN. Moreover, the maximum torque per ampere (MTPA) control is incorporated to maximise the torque generation in the constant torque region and increase the efficiency of the IPMSM drives. To verify the effectiveness of the proposed robust FNNSMC, simulations and experiments are performed by using MATLAB/Simulink platform and a TI TMS320F28335 DSP on a prototype IPMSM drive setup, respectively. Finally, the simulated and experimental results indicate that the proposed design scheme can achieve much better control performances (e.g. more rapid transient response and smaller steady-state error) when compared to the conventional SMC method, especially in the case that there exist system uncertainties.     

GA-based fuzzy PI/PD controller for automotive active suspensionsystem

A genetic-algorithm (GA)-based fuzzy proportional-plus-integral-proportional-plus-derivative (PI/PD) controller is proposed for an automotive active suspension system (AASS). The fuzzy PI- and PD-type controllers are combined to cope with the different road conditions. By using the merit of GAs, the optimal decision-making rules for both types of controllers are constructed. The real-time simulation results demonstrate that the fusion of GAs and fuzzy controller for an AASS can provide passengers much more ride comfort     

PID-type sliding mode-based adaptive motion control of a 2-DOF piezoelectric ultrasonic motor driven stage

This paper presents a new scheme of adaptive sliding mode control (ASMC) for a piezoelectric ultrasonic motor driven X–Y stage to meet the demand of precision motion tracking while addressing the problems of unknown nonlinear friction and model uncertainties. The system model with Coulomb friction and unilateral coupling effect is first investigated. Then the controller is designed with adaptive laws synthesized to obtain the unknown model parameters for handling parametric uncertainties and offsetting friction force. The robust control term acts as a high gain feedback control to make the output track the desired trajectory fast for guaranteed robust performance. Based on a PID-type sliding mode, the control scheme has a simple structure to be implemented and the control parameters can be easily tuned. Theoretical stability analysis of the proposed novel ASMC is accomplished using a Lyapunov framework. Furthermore, the proposed control scheme is applied to an X–Y stage and the results prove that the proposed control method is effective in achieving excellent tracking performance.     

履带车辆行驶平顺性仿真及试验

基于虚拟样机技术,结合三维造型软件、有限元分析和多体动力学软件,对履带车辆行驶平顺性仿真技术进行了研究.建立履带车辆的刚柔混合虚拟样机模型及随机不平路面模型,生成平顺性仿真模型系统,通过在F等级随机路面行驶仿真,得到车辆行驶时驾驶员座椅位置振动加速度曲线,根据评价标准对车辆进行平顺性评价,并与实车试验测试数据的计算结果进行对比分析.结果表明,仿真模型系统及评价方法合理,可以为提高和改善履带车辆的机动性能提供有效的方法和手段.     

Robust Stability of Discrete Bilinear Uncertain Time-Delay Systems

This paper addresses the problem of robust stability for discrete homogeneous bilinear time-delay systems subjected to uncertainties. Two kinds of uncertainties are treated: (1) nonlinear uncertainties and (2) parametric uncertainties. For parametric uncertainties, we also discuss both unstructured uncertainties and interval matrices. By using the Lyapunov stability theorem associated with some linear algebraic techniques, several delay-independent criteria are developed to guarantee the robust stability of the overall system. One of the features of the newly developed criteria is its independence from the Lyapunov equation, although the Lyapunov approach is adopted. Furthermore, the transient response and the decay rate of the resulting systems are also estimated. In particular, the transient responses for the aforementioned systems with parametric uncertainties also do not involve any Lyapunov equation which remains unsolved. All the results obtained are also applied to solve the stability analysis of uncertain time-delay systems.     

Adaptive robust pressure control of variable displacement axial piston pumps with a modified reduced-order dynamic model

Variable displacement axial piston pumps (VDAPPs) are wildly used in mobile working machines and they play a key role in machine’s energy-saving load sensing (LS) systems. Typically, electric load sensing (ELS) systems utilize traditional linear control methods, which only can realize limited control flexibility and performance. This study proposes and experimentally verifies an adaptive robust pressure control strategy for a VDAPP system. To facilitate the model-based controller design, a modified reduced-order dynamic modeling of VDAPPs is proposed. Furthermore, an adaptive robust backstepping control strategy is designed to deal with the dynamic nonlinearities and parametric uncertainties of the VDAPP system for achieving accurate pressure tracking. The controller design consists of two steps, processing the pump pressure tracking and the axial angle control, respectively. Comparative experiments and simulations with different working conditions were performed to validate the advantages of the proposed control strategy. The proposed controller achieved higher pressure tracking accuracy and it showed great capability in dealing with dynamic nonlinearities, uncertainties, and time-varying disturbances.     

A Robust Hybrid Intelligent Position/Force Control Scheme for Cooperative Manipulators

We examine in this paper the complex problem of simultaneous position and internal force control in multiple cooperative manipulator systems. This is done in the presence of unwanted parametric and modeling uncertainties as well as external disturbances. A decentralized adaptive hybrid intelligent control scheme is proposed here. The controller makes use of a multi-input multi-output fuzzy logic engine and a systematic online adaptation mechanism. Unlike conventional adaptive controllers, the proposed controller does not require a precise dynamical model of the system's dynamics. As a matter of fact, the controller can achieve its control objectives starting from partial or no a priori knowledge of the system's dynamics. The ability to incorporate the already acquired knowledge about the system's dynamics is among what distinguishes the proposed controller from its predecessor adaptive fuzzy controllers. Using a Lyapunov stability approach, the controller is proven to be robust in the face of varying intensity levels of the aforementioned uncertainties. The position and the internal force errors are also shown to asymptotically converge to zero under such conditions     

Adaptive Fuzzy Control of Nonlinear in Parameters Uncertain Chaotic Systems Using Improved Speed Gradient Method

This paper presents an adaptive fuzzy controller for Nonlinear in Parameters (NLP) chaotic systems with parametric uncertainties. In the proposed controller, the unknown parameters are estimated by the novel Improved Speed Gradient (ISG) method, which is a modification of Speed Gradient (SG) algorithm. ISG employs the Lagrangian of two suitable objective functionals for on-line estimation of system parameters. The most significant advantage of ISG is that it is applicable to NLP systems and it results in a faster rate of convergence for the estimated parameters than the SG method. Estimated parameters are used to design the fuzzy controller and to calculate the Lyapunov exponents of the chaotic system adaptively. Furthermore, established on the well-known Takagi–Sugeno (T-S) fuzzy model, a LMI (Linear Matrix Inequality)-based fuzzy controller is designed and is tuned using estimated parameters and Lyapunov exponents. Throughout the controller design procedure, several important issues in fuzzy control theory including relaxed stability analysis, control input performance specifications, and optimality are taken into account. Combination of ISG parameter estimation method and T-S-based fuzzy controller yields an adaptive fuzzy controller capable to suppress uncertainties in parameters and initial states of NLP chaotic systems. Finally, simulation results are provided to show the effectiveness of the ISG and adaptive fuzzy controller on chaotic Lorenz system and Duffing oscillator.     

A Globally Stable High-Performance Adaptive Robust Control Algorithm With Input Saturation for Precision Motion Control of Linear Motor Drive Systems

This paper focuses on the synthesis of nonlinear adaptive robust controller with saturated actuator authority for a linear motor drive system, which is subject to parametric uncertainties and uncertain nonlinearities such as input disturbances as well. Global stability with limited control efforts is achieved by breaking down the overall uncertainties to state-linearly-dependent uncertainties (such as viscous friction) and bounded nonlinearities (such as Coulomb friction, cogging force, etc.), and dealing with them via different strategies. Furthermore, a guaranteed transient performance and final tracking accuracy can be obtained by incorporating the well-developed adaptive robust control strategy and effective parameter identifier. Asymptotic output tracking is also achieved in the presence of parametric uncertainties only. Meanwhile, in contrast to the existing saturated control structures that are designed based on a set of transformed coordinates, the proposed saturated controller is carried out in the actual system states, which have clear physical meanings. This makes it much easier and less conservative to select the design parameters to meet the dual objective of achieving global stability with limited control efforts for rare emergency cases and the local high-bandwidth control for high performance under normal running conditions. Real-time experimental results are obtained to illustrate the effectiveness of the proposed saturated adaptive robust control strategy     

Investigation of Sliding-Surface Design on the Performance of Sliding Mode Controller in Antilock Braking Systems

Sliding mode control (SMC) has widely been employed in the development of a wheel-slip controller because of its effectiveness in applications for nonlinear systems as well as its performance robustness on parametric and modeling uncertainties. The design of a sliding surface strongly influences the overall behavior of the SMC system due to the discontinuous switching of control force in the vicinity of a sliding surface that produces chattering. This paper investigates the effects of sliding-surface design on the performance of an SMC-based antilock braking system (ABS), including a brake-torque limitation, an actuator time delay, and a tire-force buildup. Different sliding-surface designs commonly used in ABS were compared, and an alternative sliding-surface design that improves convergence speed and oscillation damping around the target slip has been proposed. An 8-degree-of-freedom (dof) nonlinear vehicle model was developed for this paper, and the effects of brake-system parameter variations, such as a brake actuator time constant, target slip ratios, an abrupt road friction change, and road friction noises, were also assessed.