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Nonlinear control

About: Nonlinear control is a(n) research topic. Over the lifetime, 22457 publication(s) have been published within this topic receiving 600038 citation(s). more


Open accessBook
01 Jan 1991-
Abstract: Covers in a progressive fashion a number of analysis tools and design techniques directly applicable to nonlinear control problems in high performance systems (in aerospace, robotics and automotive areas). more

Topics: Nonlinear control (54%), Robotics (52%), Aerospace (51%)

15,143 Citations

Open accessBook
01 Jan 1985-
Abstract: : The principal goal of this three years research effort was to enhance the research base which would support efforts to systematically control, or take advantage of, dominant nonlinear or distributed parameter effects in the evolution of complex dynamical systems. Such an enhancement is intended to support the development of flight controllers for increasing the high angle of attack or high agility capabilities of existing and future generations of aircraft and missiles. The principal investigating team has succeeded in the development of a systematic methodology for designing feedback control laws solving the problems of asymptotic tracking and disturbance rejection for nonlinear systems with unknown, or uncertain, real parameters. Another successful research project was the development of a systematic feedback design theory for solving the problems of asymptotic tracking and disturbance rejection for linear distributed parameter systems. The technical details which needed to be overcome are discussed more fully in this final report. more

Topics: Nonlinear control (57%), Feedback linearization (55%), Control theory (54%) more

8,498 Citations

Journal ArticleDOI: 10.1016/S0005-1098(99)00214-9
01 Jun 2000-Automatica
Abstract: Model predictive control is a form of control in which the current control action is obtained by solving, at each sampling instant, a finite horizon open-loop optimal control problem, using the current state of the plant as the initial state; the optimization yields an optimal control sequence and the first control in this sequence is applied to the plant. An important advantage of this type of control is its ability to cope with hard constraints on controls and states. It has, therefore, been widely applied in petro-chemical and related industries where satisfaction of constraints is particularly important because efficiency demands operating points on or close to the boundary of the set of admissible states and controls. In this review, we focus on model predictive control of constrained systems, both linear and nonlinear and discuss only briefly model predictive control of unconstrained nonlinear and/or time-varying systems. We concentrate our attention on research dealing with stability and optimality; in these areas the subject has developed, in our opinion, to a stage where it has achieved sufficient maturity to warrant the active interest of researchers in nonlinear control. We distill from an extensive literature essential principles that ensure stability and use these to present a concise characterization of most of the model predictive controllers that have been proposed in the literature. In some cases the finite horizon optimal control problem solved on-line is exactly equivalent to the same problem with an infinite horizon; in other cases it is equivalent to a modified infinite horizon optimal control problem. In both situations, known advantages of infinite horizon optimal control accrue. more

Topics: Optimal control (63%), Model predictive control (62%), Linear-quadratic-Gaussian control (61%) more

7,336 Citations

Open accessBook
01 Jan 1995-
Abstract: From the Publisher: Using a pedagogical style along with detailed proofs and illustrative examples, this book opens a view to the largely unexplored area of nonlinear systems with uncertainties. The focus is on adaptive nonlinear control results introduced with the new recursive design methodology--adaptive backstepping. Describes basic tools for nonadaptive backstepping design with state and output feedbacks. more

Topics: Backstepping (60%), Strict-feedback form (58%), Nonlinear control (56%) more

6,912 Citations

Journal ArticleDOI: 10.1109/TIE.2008.2011621
Jingqing Han1Institutions (1)
Abstract: Active disturbance rejection control (ADRC) can be summarized as follows: it inherits from proportional-integral-derivative (PID) the quality that makes it such a success: the error driven, rather than model-based, control law; it takes from modern control theory its best offering: the state observer; it embraces the power of nonlinear feedback and puts it to full use; it is a useful digital control technology developed out of an experimental platform rooted in computer simulations ADRC is made possible only when control is taken as an experimental science, instead of a mathematical one It is motivated by the ever increasing demands from industry that requires the control technology to move beyond PID, which has dominated the practice for over 80 years Specifically, there are four areas of weakness in PID that we strive to address: 1) the error computation; 2) noise degradation in the derivative control; 3) oversimplification and the loss of performance in the control law in the form of a linear weighted sum; and 4) complications brought by the integral control Correspondingly, we propose four distinct measures: 1) a simple differential equation as a transient trajectory generator; 2) a noise-tolerant tracking differentiator; 3) the nonlinear control laws; and 4) the concept and method of total disturbance estimation and rejection Together, they form a new set of tools and a new way of control design Times and again in experiments and on factory floors, ADRC proves to be a capable replacement of PID with unmistakable advantage in performance and practicality, providing solutions to pressing engineering problems of today With the new outlook and possibilities that ADRC represents, we further believe that control engineering may very well break the hold of classical PID and enter a new era, an era that brings back the spirit of innovations more

Topics: Active disturbance rejection control (61%), PID controller (60%), Control theory (55%) more

3,310 Citations

No. of papers in the topic in previous years

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Topic's top 5 most impactful authors

Zhong-Ping Jiang

120 papers, 5.3K citations

Romeo Ortega

82 papers, 5K citations

Mingcong Deng

75 papers, 898 citations

Jie Huang

72 papers, 4.5K citations

Miroslav Krstic

62 papers, 10.3K citations

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