Safety is the most fundamental problem of safety-critical systems. Safety control addresses the problem whether a given unsafe region of the state space can be avoided by a specific control-input. Moreover, linearly p...
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Safety is the most fundamental problem of safety-critical systems. Safety control addresses the problem whether a given unsafe region of the state space can be avoided by a specific control-input. Moreover, linearly parameterized dynamical system is a general assumption in most safety-critical adaptive control literature;however, unknown parameters in real systems are usually nonlinear. The control problem of nonlinearly parameterized systems is really difficult without linear-in-the-parameters (LIP) assumptions, which tends to be complicated and computationally intensive. In this paper, a novel model reference safety-critical adaptive control (MRAC) approach is proposed for a class of nonlinearly parameterized systems. The proposed approach involves a novel controller architecture with a modified update law, which specifically filters out the unsafe behavior (the system is in a state which the system cannot operate normally, i.e., the given unsafe region), while preserving favorable tracking capability and robustness. The novelty of this paper is that the nonlinearly parameterized systems can be enforced safety, without LIP assumptions and complex calculations. Most importantly, this approach is effective for nonlinearly parameterized systems as well as linearly parameterized systems. Finally, three illustrative numerical examples are presented to demonstrate the effectiveness of the proposed design approach.
A class of non-linear non-linear-in-the-parameters continuous-time systems are described. A least-squares parameter estimation scheme is derived and both non-recursive and recursive in time formulae are given. The cla...
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A class of non-linear non-linear-in-the-parameters continuous-time systems are described. A least-squares parameter estimation scheme is derived and both non-recursive and recursive in time formulae are given. The class of systems described includes a number of partially-known, or grey box, model structures and thus the method provides parameter identification techniques for such model structures. An example is given to illustrate the method and corresponding simulation results are presented.
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