83 research outputs found
Robust H2 optimal control of an active suspension
An active suspension control system design for a tractor-semitrailer transport vehicle is discussed. The design aims at optimality of the \Htwo-norm of
the generalized plant for a large range of semitrailer load conditions. Model parameters are taken from a real vehicle. Specifications for the disturbance input signal accounts for a stochastic road disturbance, but the design verification also considers a class of deterministic ones. To-be-controlled signal specifications,
related to driver and cargo comfort, limitations of the suspension, driving safety, and road damage, are translated in weighting transfer functions and incorporated in the generalized plant. A design for a single load condition is not satisfactory.
Two different approaches to assure robustness are followed:
(1) a simultaneous \Htwo optimal design for two plants, representing two extreme load conditions,
(2) adding an \Hinf, or better , stability constraint.
Both designs show acceptable closed loop behavior. To compute controllers,
use is made of a Multiobjective Control Design and a Linear Matrix Inequalities toolbox
Symbolic analysis and design for (nonaffine) nonlinear control systems
For the analysis and design of dynamical systems symbolic tools are a great help. In the past, several algorithms for nonlinear control systems have been implemented in MAPLE. This implementation has been extended to also handle nonaffine models and to include more algorithms. Three examples taken from the literature in this area illustrate the power and limitations of the implementation. For system models that are more than a little complex symbolic computation cannot be fully enjoyed due to the complexity of the algorithms that are more than polynomial in some measure of the problem siz
Improved mu-synthesis control design for an XY-table
The paper discusses the tracking control of uncertain nonlinear systems. The specific system studied is an XY-table. The control system consists of an exact linearizing state feedback and a linear robustifying controller in an outer loop. For the outer loop a controller is synthesized using the mu -methodology. Iterative improvements in the robustness of the controlled system are obtained by stepwise refinements in the structure of the mu -synthesis problem and in the choice of performance weights and uncertainty weights, at the cost of decreased performance. This uncovers the need for preciseness in the choice of design weight
Symbolic calculation of zero dynamics for nonlinear control systems
The calculation of the zero dynamics of a nonlinear system is of advantage in the design of controllers for this system. Because the calculation is difficult to do by hand, symbolic algebra programs are used. To access the usefulness of these programs and to solve some design problems, a MAPLE procedure, ZERODYN, is written to calculate the zero dynamics symbolically. The procedure can, however, not solve all problems, mainly because general symbolic algebra programs have insufficient capabilities to solve sets of nonlinear equations and partial differential equations. A realistic analysis problem shows thi
Acceleration assisted tracking control
This paper discusses the use of acceleration measurements to improve the performance and robustness of controllers for mechanical systems. To use acceleration signals there are at least two approaches: direct use in a feedback loop to improve the tracking error, and indirect use by an observer to improve the estimates of position and speed. Several proposals for the use of the acceleration in a feedback loop, resulting in slightly different controllers, are discussed. The design of the controllers for the experimental system, a flexible multiple degrees-of-freedom XY-table, uses a simplified two degrees-of-freedom model. The observer is of the predictive type to compensate for the time delay in the implementation, and its design is based in part on Kalman filter theory. Simulations and experiments show that both acceleration feedback and an acceleration assisted observer can improve the performance of the control system, but the robustness does not change significantly. A combination of both approaches did not give any improvement in the experiments, but some in the simulations. Disturbances in the acceleration signal (especially motor torque ripple), phase lag introduced by the signal processing equipment, time delay caused by the sampled data implementation of the controller and observer, and the non-colocated position and acceleration sensors are believed to be limitations for the usefulness of the acceleration signal. A performance improvement up to a factor of 1.5 was possible in the author's application, so to consider the use of acceleration measurements in tracking control is recommended
Collision-free trajectory planning for interventional healthcare robots:a model predictive control approach
Robust optimal sensor planning for occlusion handling in dynamic robotic environments
Optimal sensor planning for workspace detectionin robotic environments is hindered due to sensor occlusions.These occlusions are often dynamic. Probabilistic optimizationframeworks, which generally deal with the uncertain nature ofthese occlusions suffer from unreliability and/or unavailability ofprobability distribution functions. This paper proposes and analyses a robust optimization approach (minimax) which generatessensor configurations based on occlusion scenarios that causemaximum obstruction of the robotic workspace. The optimalsolution is independent of probability distribution functions andprovides a guaranteed level of workspace visibility regardlessof occluder positions, thus accounting for random occlusions.The method also allows the user to determine the impact of theworst-case occlusion scenarios leading to a broader perspective onsensor planning. Evaluation of the approach for a mobile medicalX-ray robotic system in a simulation healthcare environmentshows the effectiveness of the proposed metho
Input-output selection for planar tensegrity models
The input-output selection approach followed in this briefuses a rigorous and systematic procedure,efficiently selecting actuators and/or sensors that guaranteea desired level of performance, embedded in a heuristic.The procedure generates all so-called minimaldependent sets and uses a closed loop criterion.The heuristic is a divide-and-conquer one.This approach is applied to controlled tensegrity structures,using as criterion efficiently computableconditions for the existence of a stabilizing \Hinf-controller achievinga desired level of performance.Structural systems, like controlled tensegrities,are a prime example for application of techniquesthat address system design issues,because they present opportunitiesin choosing actuators/sensors andin choosing their mechanical structure.Results for a three-unit planar tensegrity structure,where all 26 tendons can be used as actuator or sensor devices,making up 52 devices from which to choose,demonstrate the approach.Two design specifications were explored, one is related to the dynamicalstiffness of the structure, the other to vibration isolation.The feasible sets of actuators and sensors depend on thespecifications and really differ for both, but are mostlycomposed of much less than 52 devices
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