Adaptive control allocation

Tjønnås, Johannes; Johansen, Tor Arne

doi:10.1016/j.automatica.2008.03.031

Cited by 115 publications

(63 citation statements)

References 18 publications

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“…An adaptive approach where uncertain parameters θ in the effector model h(u, x,t, θ ) are stably adapted using an adaptation law that is designed by augmenting the control Lyapunov-function in a standard way was proposed in (Tjønnås & Johansen 2005, Tjønnås & Johansen 2008). This framework was further extended to dynamically account for actuator dynamics within the control allocation in (Tjønnås & Johansen 2007b, Tjønnås & Johansen 2007a, and internal dynamics in the context of model reference adaptive control (Liao, Lum, Wang & Benosman 2009b, Liao, Lum, Wang & Benosman 2009a).…”

Section: Dynamic Optimum-seeking Methodsmentioning

confidence: 99%

Control allocation—A survey

2013

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The control algorithm hierarchy of motion control for over-actuated mechanical systems with a redundant set of effectors and actuators commonly includes three levels. First, a high-level motion control algorithm commands a vector of virtual control efforts (i.e. forces and moments) in order to meet the overall motion control objectives. Second, a control allocation algorithm coordinates the different effectors such that they together produce the desired virtual control efforts, if possible. Third, low-level control algorithms may be used to control each individual effector via its actuators. Control allocation offers the advantage of a modular design where the high-level motion control algorithm can be designed without detailed knowledge about the effectors and actuators. Important issues such as input saturation and rate constraints, actuator and effector fault tolerance, and meeting secondary objectives such as power efficiency and tear-and-wear minimization are handled within the control allocation algorithm. The objective of the present paper is to survey control allocation algorithms, motivated by the rapidly growing range of applications that have expanded from the aerospace and maritime industries, where control allocation has its roots, to automotive, mechatronics, and other industries. The survey classifies the different algorithms according to two main classes based on the use of linear or nonlinear models, respectively. The presence of physical constraints (e.g input saturation and rate constraints), operational constraints and secondary objectives makes optimization-based design a powerful approach. The simplest formulations allow explicit solutions to be computed using numerical linear algebra in combination with some logic and engineering solutions, while the more challenging formulations with nonlinear models or complex constraints and objectives call for iterative numerical optimization procedures. Experiences using the different methods in aerospace, maritime, automotive and other application areas are discussed. The paper ends with some perspectives on new applications and theoretical challenges.

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Section: Dynamic Optimum-seeking Methodsmentioning

confidence: 99%

Control allocation—A survey

2013

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“…Consider the control law (19). By construction, using (4), (5) and (1) with τ (t) = τ c (t), the derivative ofũ c (t) can be expressed asu…”

Section: B Finite-time Convergencementioning

confidence: 99%

“…Given the exogenous signal w(t), the control law (19), (23)-(24) guarantees thatũ(t) converges toũ c (t) in finite time. In particular, for any initial conditionũ(t ), there exists T > t such that the inputũ(t) driven by the control…”

Section: B Finite-time Convergencementioning

confidence: 99%

“…However, one key point for successfully re-allocating the control is the availability of adequate information about the faults that have occurred; indeed, some accurate fault estimation and/or a correct identification of the faulty actuators or effectors are necessary to address the reconfiguration. Recent results toward fault tolerant control allocation are based on slidingmode techniques [7] [14], adaptive control strategies [5] [18] [19] and unknown input observers [8] [9]. Further investigations on this topic, with a more application-oriented character, are proposed for reconfiguration in flight control [4], [22], [20] and fault accommodation in automated underwater vehicles [17].…”

Section: Introductionmentioning

confidence: 99%

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Fault-tolerant control allocation with actuator dynamics: Finite-time control reconfiguration

Cristofaro

Johansen

2014

53rd IEEE Conference on Decision and Control

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Abstract-This paper focuses on fault tolerant control allocation for overactuated systems with actuator dynamics. The proposed scheme for fault detection and isolation is based on unknown input observers and the main contribution of the paper consists in the presentation of a finite-time control reconfiguration technique which provides a successful recovering of system performances in spite of actuator faults. Simulation results support theoretical developments.

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“…In [35] an explicit piecewise linear approximate solution is created by using multi parametric programming and solving the optimization problem off-line, while in [32] a yaw stabilization scheme for an automotive vehicle using brakes, based on the dynamic optimizing control allocation approach from [17] and [34] was presented by the authors. This strategy offers the benefits of a modular approach combining convergence and stability properties for yaw rate tracking (high level control), optimality of the allocation problem and adaptation of the averaged maximal tire-road friction parameter.…”

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confidence: 99%

Stabilization of Automotive Vehicles Using Active Steering and Adaptive Brake Control Allocation

Tjønnås

Johansen

2010

IEEE Trans. Contr. Syst. Technol.

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214

114

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Abstract-In this work a dynamic control allocation approach is presented for an automotive vehicle yaw stabilization scheme. The stabilization strategy consists of a high level module that deals with the vehicle motion control objective (yaw rate reference generation and tracking), a low level module that handles the braking control for each wheel (longitudinal slip control and maximal tire-road friction parameter estimation) and an intermediate level dynamic control allocation module that generates the longitudinal slip reference for the low level brake control module and commands front wheel steering angle corrections. The control allocation design is such that the actual torque about the yaw axis tends to the desired torque calculated form the high level module, with desirable distribution of control forces satisfying actuator constraints and minimal control effort objectives.Conditions for uniform asymptotic stability are given for the case when the control allocation includes adaptation of the tire-road maximal friction coefficients, and the scheme has been implemented in a realistic non linear multi body vehicle simulation environment. The simulation cases show that the yaw control allocation strategy stabilizes the vehicle in extreme maneuvers where the non linear vehicle yaw dynamics otherwise (without active braking or active steering) becomes unstable in the sense of over-or under steering.The control allocation implementation is efficient and suitable for low cost automotive electronic control units.

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Adaptive control allocation

Cited by 115 publications

References 18 publications

Control allocation—A survey

Control allocation—A survey

Fault-tolerant control allocation with actuator dynamics: Finite-time control reconfiguration

Stabilization of Automotive Vehicles Using Active Steering and Adaptive Brake Control Allocation

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