190 research outputs found
Asymptotic Tracking Control of Uncertain MIMO Nonlinear Systems with Less Conservative Controllability Conditions
For uncertain multiple inputs multi-outputs (MIMO) nonlinear systems, it is
nontrivial to achieve asymptotic tracking, and most existing methods normally
demand certain controllability conditions that are rather restrictive or even
impractical if unexpected actuator faults are involved. In this note, we
present a method capable of achieving zero-error steady-state tracking with
less conservative (more practical) controllability condition. By incorporating
a novel Nussbaum gain technique and some positive integrable function into the
control design, we develop a robust adaptive asymptotic tracking control scheme
for the system with time-varying control gain being unknown its magnitude and
direction. By resorting to the existence of some feasible auxiliary matrix, the
current state-of-art controllability condition is further relaxed, which
enlarges the class of systems that can be considered in the proposed control
scheme. All the closed-loop signals are ensured to be globally ultimately
uniformly bounded. Moreover, such control methodology is further extended to
the case involving intermittent actuator faults, with application to robotic
systems. Finally, simulation studies are carried out to demonstrate the
effectiveness and flexibility of this method
Quantized control of non-Lipschitz nonlinear systems: a novel control framework with prescribed transient performance and lower design complexity
A novel control design framework is proposed for a class of non-Lipschitz
nonlinear systems with quantized states, meanwhile prescribed transient
performance and lower control design complexity could be guaranteed. Firstly,
different from all existing control methods for systems with state
quantization, global stability of strict-feedback nonlinear systems is achieved
without requiring the condition that the nonlinearities of the system model
satisfy global Lipschitz continuity. Secondly, a novel barrier function-free
prescribed performance control (BFPPC) method is proposed, which can guarantee
prescribed transient performance under quantized states. Thirdly, a new
\textit{W}-function-based control scheme is designed such that virtual control
signals are not required to be differentiated repeatedly and the controller
could be designed in a simple way, which guarantees global stability and lower
design complexity compared with traditional dynamic surface control (DSC).
Simulation results demonstrate the effectiveness of our method
Adaptive Fuzzy Tracking Control with Global Prescribed-Time Prescribed Performance for Uncertain Strict-Feedback Nonlinear Systems
Adaptive fuzzy control strategies are established to achieve global
prescribed performance with prescribed-time convergence for strict-feedback
systems with mismatched uncertainties and unknown nonlinearities. Firstly, to
quantify the transient and steady performance constraints of the tracking
error, a class of prescribed-time prescribed performance functions are
designed, and a novel error transformation function is introduced to remove the
initial value constraints and solve the singularity problem in existing works.
Secondly, based on dynamic surface control methods, controllers with or without
approximating structures are established to guarantee that the tracking error
achieves prescribed transient performance and converges into a prescribed
bounded set within prescribed time. In particular, the settling time and
initial value of the prescribed performance function are completely independent
of initial conditions of the tracking error and system parameters, which
improves existing results. Moreover, with a novel Lyapunov-like energy
function, not only the differential explosion problem frequently occurring in
backstepping techniques is solved, but the drawback of the semi-global
boundedness of tracking error induced by dynamic surface control can be
overcome. The validity and effectiveness of the main results are verified by
numerical simulations on practical examples
Robust nonlinear control of vectored thrust aircraft
An interdisciplinary program in robust control for nonlinear systems with applications to a variety of engineering problems is outlined. Major emphasis will be placed on flight control, with both experimental and analytical studies. This program builds on recent new results in control theory for stability, stabilization, robust stability, robust performance, synthesis, and model reduction in a unified framework using Linear Fractional Transformations (LFT's), Linear Matrix Inequalities (LMI's), and the structured singular value micron. Most of these new advances have been accomplished by the Caltech controls group independently or in collaboration with researchers in other institutions. These recent results offer a new and remarkably unified framework for all aspects of robust control, but what is particularly important for this program is that they also have important implications for system identification and control of nonlinear systems. This combines well with Caltech's expertise in nonlinear control theory, both in geometric methods and methods for systems with constraints and saturations
Funnel Control Under Hard and Soft Output Constraints
This paper proposes a funnel control method under time-varying hard and soft
output constraints. First, an online funnel planning scheme is designed that
generates a constraint consistent funnel, which always respects hard (safety)
constraints, and soft (performance) constraints are met only when they are not
conflicting with the hard constraints. Next, the prescribed performance control
method is employed for designing a robust low-complexity funnel-based
controller for uncertain nonlinear Euler-Lagrangian systems such that the
outputs always remain within the planned constraint consistent funnels.
Finally, the results are verified with a simulation example of a mobile robot
tracking a moving object while staying in a box-constrained safe space.Comment: 9 pages, 6 figures, submitted to 61st IEEE Conference on Decision and
Control 202
Distributed Control of Multi-agent Systems with Unknown Time-varying Gains: A Novel Indirect Framework for Prescribed Performance
In this paper, a new yet indirect performance guaranteed framework is
established to address the distributed tracking control problem for networked
uncertain nonlinear strict-feedback systems with unknown time-varying gains
under a directed interaction topology. The proposed framework involves two
steps: In the first one, a fully distributed robust filter is constructed to
estimate the desired trajectory for each agent with guaranteed observation
performance that allows the directions among the agents to be non-identical. In
the second one, by establishing a novel lemma regarding Nussbaum function, a
new adaptive control protocol is developed for each agent based on backstepping
technique, which not only steers the output to asymptotically track the
corresponding estimated signal with arbitrarily prescribed transient
performance, but also largely extends the scope of application since the
unknown control gains are allowed to be time-varying and even state-dependent.
In such an indirect way, the underlying problem is tackled with the output
tracking error converging into an arbitrarily pre-assigned residual set
exhibiting an arbitrarily pre-defined convergence rate. Besides, all the
internal signals are ensured to be semi-globally ultimately uniformly bounded
(SGUUB). Finally, simulation results are provided to illustrate the
effectiveness of the co-designed scheme
Robust Distributed Control Protocols for Large Vehicular Platoons with Prescribed Transient and Steady State Performance
In this paper, we study the longitudinal control problem for a platoon of
vehicles with unknown nonlinear dynamics under both the predecessor-following
and the bidirectional control architectures. The proposed control protocols are
fully distributed in the sense that each vehicle utilizes feedback from its
relative position with respect to its preceding and following vehicles as well
as its own velocity, which can all be easily obtained by onboard sensors.
Moreover, no previous knowledge of model nonlinearities/disturbances is
incorporated in the control design, enhancing in that way the robustness of the
overall closed loop system against model imperfections. Additionally, certain
designer-specified performance functions determine the transient and
steady-state response, thus preventing connectivity breaks due to sensor
limitations as well as inter-vehicular collisions. Finally, extensive
simulation studies and a real-time experiment conducted with mobile robots
clarify the proposed control protocols and verify their effectiveness.Comment: IEEE Transactions on Control Systems Technology, accepte
Adaptive Control of Unknown Pure Feedback Systems with Pure State Constraints
This paper deals with the tracking control problem for a class of unknown
pure feedback system with pure state constraints on the state variables and
unknown time-varying bounded disturbances. An adaptive controller is presented
for such systems for the very first time. The controller is designed using the
backstepping method. While designing it, Barrier Lyapunov Functions is used so
that the state variables do not contravene its constraints. In order to cope
with the unknown dynamics of the system, an online approximator is designed
using a neural network with a novel adaptive law for its weight update. In the
stability analysis of the system, the time derivative of Lyapunov function
involves known virtual control coefficient with unknown direction and to deal
with such problem Nussbaum gain is used to design the control law. Furthermore,
to make the controller robust and computationally inexpensive, a novel
disturbance observer is designed to estimate the disturbance along with neural
network approximation error and the time derivative of virtual control input.
The effectiveness of the proposed approach is demonstrated through a simulation
study on the third-order nonlinear system
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