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In this paper, we study the suspension performances with an adaptive inerter under the assumption that the inertance may be adjusted in real-time. A quarter-car model with an inerter installed in parallel with a spring and a damper is considered. First, for a given suspension system, a study of the effects of using a fixed inerter is provided. It shows the difficulty in choosing a fixed inerter to satisfy the vehicle performances at the sprung mass natural frequency without deteriorating significantly at the unsprung mass natural frequency. Then, a state-feedback H2 controller for an active suspension system is designed. The active force is approximated by an inerter with adjustable inertance. Ride quality, suspension deflection and tyre deflection performances are considered independently as the prime objective. Simulation results show that comparing with the passive suspension with fixed inerter, for ride quality and tyre deflection, the suspension with adaptive inerter can achieve improvement at the sprung mass natural frequency at the expense of a relatively small deterioration at the unsprung mass natural frequency. For suspension deflection, a better performance can be achieved at high frequencies with almost no loss of performance at other frequencies. This method is easier to implement in practice than the optimized passive suspension with a fixed inerter, which calls for complicated layout.

In this paper, the novel design of a side-slip angle observer and an observer-based stability controller for a vehicle with a non-linear tyre model and a varying forward speed is presented. The Takagi–Sugeno fuzzy modelling technique is first applied to represent the vehicle’s lateral dynamics with the non-linear Dugoff tyre model and varying speed. The observer and the observer-based controller are then constructed using the measured yaw rate and the estimated premise and state variables and are designed to be robust against the model and parameter uncertainties. The conditions for designing such an observer and an observer-based controller are derived in terms of linear matrix inequalities. Numerical simulations on a non-linear eight-degree-of-freedom vehicle dynamics model and experimental data are conducted to validate the effectiveness of the proposed approach. The comparison of results with those from other existing approaches shows that the designed observer can accurately estimate the vehicle’s side-slip angle and the controller can effectively control this side-slip angle regardless of the variation in the vehicle’s longitudinal velocity.

This paper presents a multi-objective control method with wheelbase preview for active vehicle suspension. A four-degree-of-freedom half-car model with active suspension is considered in this study. H ∞ norm and generalized H 2 norm are used to improve ride quality and ensure that hard constraints are satisfied. Disturbances at the front wheel are obtained as preview information for the rear wheel. Static output-feedback is utilized in designing controllers, the solution is derived by iterative linear matrix inequality (ILMI) and cone complementarity linearization (CCL) algorithms. Simulation results confirm that multi-objective control with wheelbase preview achieves a significant improvement of ride quality (a maximum 27 percent and 60 percent improvement on vertical and angular acceleration, respectively) comparing with that of control without preview, while suspension deflections, tyre deflections and actuator forces remaining within given bounds. The extent of the improvement on the ride quality for different amount of preview information used is also illustrated.

This paper presents a velocity-dependent multi-objective control method to solve the problem of preview control with velocity uncertainty. A half-car model is considered, the Pade approach is used to approximate the preview delay so as to rewrite it in a finite-dimensional description. It is assumed that the forward velocity of the vehicle resides in an interval and can be measured in real time. The controller, whose gain matrix depends on the information of the velocity, is designed by utilizing the polynomial parameter-dependent idea. The design procedure is developed based on homogeneous polynomial parameter-dependent matrices with arbitrary degree, with quadratic and linear parameter-dependent frameworks as special cases of this method. As the degree increases, the proposed controller can yield less conservative result. A linear parameter-dependent (LPV) controller has also been proposed for the system under time-varying velocity. Simulation results illustrate the usefulness and the advantages of the proposed methods.

This paper puts forward a novel optimization approach for Multiple Active Tuned Mass Dampers (MATMDs) system under seismic and wind-induced building vibration. A model of an n -storey building with MATMD system is established and a joint optimization method is used to obtain an optimal state-feedback controller gain and the parameters of the MATMD system. A mixed H 2 / H ∞ / GH 2 control is employed to attenuate the seismic and wind-induced vibration of the building with the constraints of the actuating forces and strokes of the masses. Genetic algorithm (GA) is used to search for the optimal parameters and obtain the corresponding controller gain. Two illustrative examples are presented in this paper. In the first comparison, the GA-based approach can obtain a better set of parameters and achieve better control performance. When comparing with an Active Tuned Mass Damper (ATMD), an MATMD system can achieve similar control effects with much smaller acting forces.

In the common practice of designing an attitude tracker for an aerospacecraft, one transforms the Newton-Euler rotation equations to obtain the dynamic equations of some chosen inertial frame based attitude metrics, such as Euler angles and unit quaternions. A Lyapunov approach is then used to design a controller which ensures asymptotic convergence of the attitude to the desired orientation. Although this design methodology is pretty standard, it usually involves singularity-prone coordinate transformations which complicates the analysis process and controller design. A new, singularity free error feedback method is proposed in the paper to provide simple and intuitive stability analysis and controller synthesis. This new body frame based method utilizes the concept of Euleraxis and angles to generate the smallest error angles from a body frame perspective, without coordinate transformations. Global tracking convergence is illustrated with the use of a feedback linearizing PD tracker, a sliding mode controller, and a model reference adaptive controller. Experimental results are also obtained on a quadrotor platform with unknown system parameters and disturbances, using a boundary layer approximated sliding mode controller, a PIDD controller, and a unit sliding mode controller. Significant tracking quality is attained.

It has always been difficult to accurately estimate the moment of inertia of an object, e.g. an unmanned aerial vehicle (UAV). Whilst various offline estimation methods exist to allow accurate parametric estimation by minimizing an error cost function, they require large memory consumption, high computational effort, and a long convergence time. The initial estimate's accuracy is also vital in attaining convergence. In this paper, a new real time solution to the model identification problem is provided with the use of a Joint Unscented Kalman Filter for dual estimation. The identification procedures can be easily implemented using a microcontroller, a gyroscope sensor, and a simple bifilar pendulum setup. Accuracy, robustness, and convergence speed are achieved.

The paper presents a study on the direct voltage control of a magnetorheological (MR) damper for application in vehicle suspensions. As MR damper dynamics is highly nonlinear, the direct control system design for an MR damper is difficult. Representing an MR damper by a Takagi-Sugeno (TS) fuzzy model enables the linear control theory to be directly applied to design the MR damper controller. In this paper, first the MR damper dynamics is represented by a TS fuzzy model, and then an H∞ controller that considers the suspension performance requirements and the constraint on the input voltage for the MR damper is designed. Furthermore, considering the case that not all the state variables are measurable in practice, the design of an H∞ observer with immeasurable premise variables and the design of a robust controller are proposed, respectively. Numerical simulations are used to validate the effectiveness of the proposed approaches. © 2013 IOP Publishing Ltd.

In the process industry, the key performance indicator (KPI)-related prediction and fault diagnosis are important steps to guarantee the product quality and improve economic benefits. A popular monitoring method as it has been, the partial least squares (PLS) algorithm is sensitive to outliers in training datasets, and cannot efficiently distinguish faults related to KPI from those unrelated to KPI due to its oblique projection to the input space. In this paper, a novel robust data-driven approach, named advanced partial least squares (APLS), is presented to handle process outliers under an improved framework of PLS. By means of a weighting strategy, APLS can remove the impact of outliers on process measurements and establish a more accurate model than PLS for fault diagnosis in the monitoring scheme, whose effectiveness has been verified through the Tennessee Eastman (TE) benchmark process. Simulation results demonstrate that the proposed approach is suitable not only for the KPI-related process prediction but also for the diagnosis of KPI-related faults.

A novel approach is presented for fully automated boundary extraction and rectification of bony tissue from planar CT data. The approach extracts and rectifies feature boundary in a hierarchical fashion. It consists of a fuzzy multilevel thresholding operation, followed by a small void cleanup procedure. Then a binary morphological boundary detector is applied to extract the boundary. However, defective boundaries and undesirable artifacts may still be present. Thus two innovative anatomical knowledge based algorithms are used to remove the undesired structures and refine the erroneous boundary. Results of applying the approach on lumbar CT images are presented, with a discussion of the potential for clinical application of the approach.