Steve C. Southward

An Adaptive Semiactive Control Algorithm for Magnetorheological Suspension Systems

In this paper, we will present a nonlinear-model-based adaptive semiactive control algorithm developed for magnetorheological (MR) suspension systems exposed to broadband nonstationary random vibration sources that are assumed to be unknown or not measurable. If there exist unknown and/or varying parameters of the dynamic system such as mass and stiffness, then the adaptive algorithm can include on-line system identification such as a recursive least-squares method. Based on a nonparametric MR damper model, the adaptive system stability is proved by converting the hysteresis inherent with MR dampers to a memoryless nonlinearity with sector conditions. The convergence of the adaptive system, however, is investigated through a linearization approach including further numerical illustration of specific cases. Finally the simulation results for a magnetorheological seat suspension system with the suggested adaptive control are presented. The results are compared with low-damping and high-damping cases, and such comparison further shows the effectiveness of the proposed nonlinear model-based adaptive control algorithm for damping tuning.

No-Jerk Skyhook Control Methods for Semiactive Suspensions

This paper presents two alternative implementations of skyhook control, named ‘‘skyhook function’’ and ‘‘no-jerk skyhook,’’ for reducing the dynamic jerk that is often experienced with conventional skyhook control in semiactive suspension systems. An analysis of the relationship between the absolute velocity of the sprung mass and the relative velocity across the suspension are used to show the damping-force discontinuities that result from the conventional implementation of skyhook control. This analysis shows that at zero crossings of the relative velocity, conventional skyhook introduces a sharp increase (jump) in damping force, which, in turn, causes a jump in sprung-mass acceleration. This acceleration jump, or jerk, causes a significant reduction in isolation benefits that can be offered by skyhook suspensions. The alternative implementations of skyhook control included in this study offer modifications to the formulation of conventional skyhook control such that the damping force jumps are eliminated. The alternative policies are compared to the conventional skyhook control in the laboratory, using a base-excited semiactive system that includes a heavy-truck seat suspension. An evaluation of the damping force, seat acceleration, and the electrical currents supplied to a magnetorheological damper, which is used for this study, shows that the alternative implementations of skyhook control can entirely eliminate the damping-force discontinuities and the resulting dynamic jerks caused by conventional skyhook control. @DOI: 10.1115/1.1805001#

Global Active Noise Control of a One-Dimensional Acoustic Duct Using a Feedback Controller

The active noise control of a one-dimensional hard-walled duct with a partially dissipative boundary condition is addressed. Previous techniques have attacked this problem by developing adaptive filters designed to cancel acoustic noise at a single measurement location. The work presented here applies modern, state space, control theory to globally reduce noise levels in a one-dimensional acoustic enclosure rather than at a single location. This global control requires only the addition of a single response measurement microphone and control speaker to the open-loop system

Modeling Magnetorheological Dampers with Application of Nonparametric Approach

This article studies the application of nonparametric modeling approach to model magnetorheological (MR) dampers. For comparison purposes, another typical parametric modeling method for electrorheological (ER) and MR dampers is reviewed. The existing parametric MR damper model includes a stiff Bouc-Wen model that is not friendly for simulation study and real time implementation of model-based advanced control algorithms. In order to avoid the difficulties by using the existing parametric model, the test data from a commercialized MR damper is employed to develop nonparametric models, which can consist of a series of numerically efficient mathematic functions. In addition, the selected functions are required to be continuous and differentiable for potential model-based control algorithms. The results of the nonparametric models show that such different models are comparable. Furthermore, one nonparametric model is selected to be compared with a parametric model and the test data to illustrate the accuracy of the model. The comparison shows that the proposed nonparametric models are able to accurately predict the damper force characteristics, damper bilinear behavior, hysteresis, and electromagnetic saturation. It is further shown that the nonparametric models can be numerically solved with an integration step size of the order of 10 2 s, much faster than the parametric models of the order of 10 5 s, which clearly shows that the proposed nonparametric models are feasible even for real time model-based control algorithms.

Multibody dynamics modelling and system identification of a quarter-car test rig with McPherson strut suspension

In this paper, we develop a multibody dynamics model of a quarter-car test-rig equipped with a McPherson strut suspension and we apply a system identification technique on it. Constrained equations of motion in the Lagrange multiplier form are derived and employed to characterise the dynamic behaviour of the test rig modelled once as a linear system and once as a non-linear system. The system of differential algebraic equations is integrated using a Hilber–Hughes–Taylor integrator. The responses of both models (linear and non-linear) to a given displacement input are obtained and compared with the experimental response recorded using the physical quarter-car test rig equipped with a McPherson strut suspension. The system identification is performed for control purposes. The results, as well as the performance and area of applicability of the test rig models derived, are discussed.

Orthogonal Eigenstructure Control with Non-collocated Actuators and Sensors

An orthogonal eigenstructure control method with collocated actuators and sensors was recently developed by the authors. In this paper the application of the method is extended beyond the collocation of the actuators and sensors, including the cases that different numbers of actuators and sensors are used. Orthogonal eigenstructure control is an output feedback control for multi-input multi-output linear systems. This method uses singular value decomposition to find the matrix that spans the null space of the closed-loop eigenvectors. This method regenerates the open-loop system while simultaneously determines a set of eigenvectors that are orthogonal to the open-loop eigenvectors. This method does not attempt to place the eigenvalues of the closed-loop system, nor does it require defining a set of closed-loop eigenvectors. The closed-loop eigenvectors will be within the achievable eigenvector set and the closed-loop poles will be consistent with them. As a result, no extra constraints have been imposed to the system trying to place the closed-loop poles at certain locations such that excessive force in actuators is prevented. Since there are usually some limitations on the location of the actuators and sensors, collocation of actuators and sensors is not always possible. Also, some systems might not have equal number of actuators and sensors. The proposed method in this paper is based on adding virtual actuators and sensors to the closed-loop system in order to extend the application of the orthogonal eigenstructure control to the systems with non-collocated actuators and sensors as well as the systems with different numbers of actuators and sensors.

Analysis and Strategy for Superharmonics With Semiactive Suspension Control Systems

This paper focuses on an experimental implementation of a semiactive seat suspension using magnetorheological (MR) dampers. We first introduce the nonlinear dynamics phenomena induced by skyhook control. Skyhook control has been widely applied to applications ranging from structural vibration suppression to commercialized vehicle suspensions. Unfortunately, skyhook control generates superharmonic dynamics; yet, this issue has not been clearly addressed in such vibration control systems. This paper will attempt to explain how superharmonics are created with skyhook controls through analysis of test data. Furthermore, a nonlinear model-based adaptive control algorithm is developed and evaluated for reducing the negative impact of the superharmonics. Based on an empirical MR damper model, the adaptive algorithm is expanded mathematically, and the system stability is discussed. Then in the following sections, this paper describes implementation procedures such as modeling simplification and validation, and testing results. Through the laboratory testing, the adaptive suspension is compared to two passive suspensions: hard-damping (stiff) suspension with a maximum current of 1 A to the MR damper and low-damping (soft) suspension with a low current of 0 A, while broadband random excitations are applied with respect to the seat suspension resonant frequency in order to test the adaptability of the adaptive control. In two separate studies, both mass and spring rate are assumed known and unknown in order to investigate the capability of the adaptive algorithm with the simplified model. Finally, the comparison of test results is presented to show the effectiveness and feasibility of the proposed adaptive algorithm to eliminate the superharmonics from the MR seat suspension response.

Neural Control of Posture During Small Magnitude Perturbations: Effects of Aging and Localized Muscle Fatigue

This study investigated the effects of aging and localized muscle fatigue on the neural control of upright stance during small postural perturbations. Sixteen young (aged 18-24 years) and 16 older (aged 55-74 years) participants were exposed to small magnitude, anteriorly-directed postural perturbations before and after fatiguing exercises (lumbar extensors and ankle plantar flexors). A single degree of freedom model of the human body was used to simulate recovery kinematics following the perturbations. Central to the model was a simulated neural controller that multiplied time-delayed kinematics by invariant feedback gains. Feedback gains and time delay were optimized for each participant based on measured kinematics, and a novel delay margin analysis was performed to assess system robustness. A 10.9% longer effective time delay (p = 0.010) was found among the older group, who also showed a greater reliance upon velocity feedback information (31.1% higher differential gain, p = 0.001) to control upright stance. Based on delay margins, older participants adopted a more robust control scheme to accommodate the small perturbations, potentially compensating for longer time delays or degraded sensory feedback. No fatigue-induced changes in neural controller gains, time delay, or delay margin were found in either age group, indicating that integration of this feedback information was not altered by muscle fatigue. The sensitivity of this approach to changes with fatigue may have been limited by model simplifications.

Real-Time Parameter ID Using Polynomial Chaos Expansions

A novel real-time parameter identification algorithm has been developed that exploits polynomial chaos expansion (PCE) representations of uncertain parameters. Dynamic system models inevitably contain parameters whose values are rarely known with absolute certainty. In many cases, such parameters are either not measurable, or they are slowly time varying. In some cases, the dynamic system model is inadequate and parameter values are simply chosen to provide a “best fit” representation. For the method proposed here, we assume apriori knowledge of the probability distributions associated with the uncertain parameters. Within the PCE framework, the uncertain parameter distribution is explicitly propagated through the dynamic system equations using a Galerkin projection onto an orthogonal polynomial basis. The probabilistic PCE model is then collapsed to a deterministic model where an adaptive algorithm is designed to effectively reduce the uncertainty. For illustration, this algorithm is numerically demonstrated using a simple first order dynamic system with only a single uncertain parameter.Copyright

Active noise and vibration cancellation system

An active noise and vibration control system (20) for cancellation of noise or vibration. The system (20) provides a system whereby the adaptation path and feedforward path are implemented in separate hardware. As a result, the computational burden on the digital signal processor (DSP) (28) is reduced allowing the DSP (28) to handle multiple inputs (22), error sensors (34), and transducers (32). In one embodiment, the processing of the input signal from sensor (22) takes place in a waveform generator (24) comprising a phase-locked loop, a frequency divider, a shift register, and at least one switched capacitor filter. In another embodiment the input signal processing takes place in separate feedforward circuitry including a field programmable gate array (64).