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The filtered basis functions (FBF) approach is gaining interest for feedforward tracking control of linear, especially, nonminimum phase systems. It expresses the control input to the plant as a linear combination of basis functions with unknown coefficients. The basis functions are forward filtered through the plant dynamics and the coefficients are selected such that the tracking error is minimized. This article proposes a robust FBF approach for tracking control of linear time invariant systems with known uncertainty. A robust filter is formulated as the inverse of an optimal controller that minimizes a frequency-domain cost function over the known uncertainty. The proposed robust FBF approach filters the basis functions using the robust filter in lieu of the nominal plant dynamics. Stability issues associated with the robust filter are discussed, as are the incorporation of dynamic uncertainty into the robust filter. Applied to a vibration-prone desktop 3-D printer with dynamic uncertainty, significant improvements in tracking accuracy are demonstrated using the robust FBF approach compared to the standard FBF approach. [ABSTRACT FROM AUTHOR]

An optimal inversion-based iterative learning control (Opt-In ILC) approach for overactuated systems is proposed. The Opt-In ILC update law is formulated as a constrained optimization problem using the plant model. Specifically, the ILC update law is designed to minimize control effort subject to a user-specified error convergence rate. To achieve robust monotonic convergence (MC), a frequency-domain optimization framework is adopted to determine the best plant model and robustness filter for Opt-In ILC. Simulations and experiments on an overactuated coarse–fine stage are used to demonstrate optimal control effort allocation and optimal selection of plant model and robustness filter via the proposed Opt-In ILC approach. [ABSTRACT FROM AUTHOR]

Steer-by-wire (SBW) systems provide significant benefits to classical vehicles, and are indispensable for fully autonomous vehicles (AVs). A backup (emergency) strategy is needed to steer an AV equipped with SBW to safety if its steering actuator fails completely. Differential drive assisted steering (DDAS), which uses torque vectoring(TV) to steer a vehicle in the absence of a steering actuator, has been proposed as a backup strategy for SBW systems. However, vehicle stability control (VSC) – a required feature in most modern vehicles – also relies on TV to assist vehicles in maintaining stability. In this paper, it is shown through stability analyses on a linearized vehicle model that VSC enhances the stability of an AV equipped with a fully functional SBW system. Conversely, when combined with DDAS as a backup steering strategy for a failed SBW system, VSC typically deteriorates stability. Thus, the benefits of VSC in ensuring stability cannot be taken for granted for AVs, e.g., by designing it as a decoupled controller added on to DDAS. Instead, VSC and DDAS must be designed in an integrated manner to guarantee stability. To support this finding, simulations are conducted where a nonlinear AV model with either SBW or DDAS – both combined with VSC and implemented via model predictive control – are used to carry out an ISO-standard double lane change test at 80 km/h. Using the SBW system, the AV passes the test, both with VSC decoupled from and integrated with the SBW system. However, in the case of SBW failure, the AV with VSC decoupled from DDAS fails the test, while that with VSC integrated with DDAS completes the test successfully. [ABSTRACT FROM AUTHOR]

Over-actuated systems are characterized by a larger number of actuators compared with the degrees of freedom to be controlled. In such systems, it is often desirable to allocate control effort dynamically (i.e., over a broad range of frequencies) in an optimal manner, without sacrificing control performance. At present, this goal is achieved through computationally intensive real-time optimization or by using static redundancy models, which could significantly sacrifice optimality and/or control performance. In the context of dual-input, single-output over-actuated systems, this paper proposes a proxy-based approach for optimal dynamic control allocation without need for real-time optimization. Using factorization, a causally implementable optimal relationship between two control inputs is derived. It is shown that control energy is minimized indirectly by minimizing the deviation of control inputs from the derived relationship, which can be achieved using classical or advanced feedforward (FF) and/or feedback (FB) control techniques. A classical FF and FB approach for implementing the proposed proxy-based allocation scheme, alongside an approach for handling input constraints, is detailed and validated in simulations and experiments on an over-actuated hybrid feed drive. In comparison with an existing static-model-based dynamic allocation approach, large reductions in control energy without sacrificing control performance are demonstrated. [ABSTRACT FROM PUBLISHER]

This paper presents a method for designing a controller that achieves the best positioning performance while maximizing the energy efficiency of a redundantly actuated hybrid feed drive. A two-degree-of-freedom controller, consisting of a feedforward (FF) controller for tracking and a feedback (FB) controller for regulation, is assumed. It is shown that the ideal FF controller, which achieves perfect tracking with maximum efficiency, is not always stable. Therefore, a method for designing a stable FF controller that achieves perfect tracking with near optimal efficiency is proposed. Furthermore, the optimal relationship between FB control inputs, which guarantees maximum efficiency for any specified regulation performance, is derived. Two approaches for using the derived optimal relationship to synthesize a FB controller that achieves the best positioning performance while maximizing efficiency are proposed. Simulations and machining experiments are conducted to demonstrate the effectiveness of the proposed energy-efficient FF and FB controller design methods. Significant improvements in energy efficiency (without sacrificing positioning performance) are reported. [ABSTRACT FROM PUBLISHER]