High-gain Adaptive Control Research Articles

Electric Vehicle Traction System Performance Enhancement Using a High-Gain Adaptive Controller

Electric Vehicle Traction System Performance Enhancement Using a High-Gain Adaptive Controller

Modelling and high-gain adaptive control of chaotic yawing of ship under wave disturbance

Modelling and high-gain adaptive control of chaotic yawing of ship under wave disturbance

Stabilizing Unknown Nonlinear Systems via Decentralized High-gain Adaptive Control

In this paper, we study decentralized adaptive stabilization for unknown nonlinear systems with square input matrix-valued functions. This problem formulation arises, e.g., in the context of nonlinear networks, where each scalar subsystem can implement its local controller. We show that if the input matrix-valued function possesses a kind of diagonally dominant properties, decentralized stabilization can be achieved by making each local control gain sufficiently large without knowing the exact system dynamics. Furthermore, when each local gain is updated based on the corresponding local information, the boundedness and convergence of both system dynamics and adaptive local gains are guaranteed.

Stabilization by Adaptive Feedback Control for Positive Difference Equations with Applications in Pest Management

An adaptive feedback control scheme is proposed for stabilizing a class of forced nonlinear positive difference equations. The adaptive scheme is based on so-called high-gain adaptive controllers and contains substantial robustness with respect to model uncertainty as well as with respect to persistent forcing signals, including measurement errors. Our results take advantage of the underlying positive systems structure and ideas from input-to-state stability from nonlinear control theory. Our motivating application is to pest or weed control, and in this context the present work substantially strengthens previous work by the authors. The theory is illustrated with examples.

Event‐Triggered High Gain Adaptive Backstepping Control for Switched Uncertain Nonlinear System

AbstractThis paper is concerned with an event‐triggered controller deign based on high gain adaptive backstepping for switched uncertain nonlinear systems. The proposed controller can be applied to switched systems with uncertain control coefficient and does not have a compensation term for the error between the current control signal and last updated control signal. These advantages are obtained due to the proposed event‐triggered controller is designed based on the high gain adaptive feedback strategy. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Rendezvous with connectivity preservation for multi-robot systems with an unknown leader

ABSTRACTThis paper studies the leader-following rendezvous problem with connectivity preservation for multi-agent systems composed of uncertain multi-robot systems subject to external disturbances and an unknown leader, both of which are generated by a so-called exosystem with parametric uncertainty. By combining internal model design, potential function technique and adaptive control, two distributed control strategies are proposed to maintain the connectivity of the communication network, to achieve the asymptotic tracking of all the followers to the output of the unknown leader system, as well as to reject unknown external disturbances. It is also worth to mention that the uncertain parameters in the multi-robot systems and exosystem are further allowed to belong to unknown and unbounded sets when applying the second fully distributed control law containing a dynamic gain inspired by high-gain adaptive control or self-tuning regulator.

High-gain adaptive position control

High-gain adaptive position control is proposed for a stiff one-mass system (1MS) and an elastic two-mass system (2MS). The control objective is (load-side) position reference tracking and disturbance rejection (of load torques and friction). Position and speed are available for feedback. Two simple high-gain adaptive position control strategies are presented and applied to a laboratory setup: an adaptive λ-tracking controller and a funnel controller. Both controllers neither estimate nor identify the plant. The λ-tracking controller achieves tracking with prescribed asymptotic accuracy: for given λ > 0 (arbitrary small) the error approaches the interval [−λ, λ] asymptotically. Whereas the funnel controller assures tracking with prescribed transient accuracy: the error and its derivative are bounded by prescribed positive (possibly non-increasing) functions of time. A simple proportional-integral (PI)-like extension for the 1MS, and this extension in combination with a high-pass filter for the 2MS, allow for zero tracking errors in steady-state, respectively. Oscillations in the shaft of the 2MS can be suppressed.

High gain adaptive output feedback control of non-linear systems with a class of control uncertainties

This paper presents a design method of high gain adaptive output feedback controller for non-linear systems with higher order relative degree and some uncertainties in control input term. The controlled system is transformed by two virtual filters and the control input is designed so as to make the virtual filter’s signal a high gain adaptive control signal using backstepping strategy in the filter dynamics. The proposed method is useful in cases where only the output signals are available.

High gain adaptive control for a class of uncertain non-holonomic systems

It is well known that high gain feedback is one of the most powerful controller design technique for uncertain controlled systems. In this paper, a controller design method of high gain adaptive feedback for non-holonomic systems with unknown system parameters and a class of unbounded uncertainties and/or disturbances is proposed. The considered unbounded uncertainties and/or disturbances are decomposed by a known function and unknown constants. The designed adaptive controller is a rather simple structure, since it has only one adaptive adjusting term which is a feedback gain designed based on high gain technique. Further, the controller achieves not only the stability of the controlled system but also convergence to a small region around origin by increasing the design parameters. The application of the controller to a non-holonomic wheeled mobile robot is considered as an illustrative example.

High-Gain Adaptive Control: A Derivative-Based Approach

In this work, we propose an adaptive scheme which is a counterpart of existing high gain control techniques based on control Lyapunov functions. Given a control Lyapunov function, the main idea is that of tuning the feedback gain according to a suitably-chosen Lyapunov time-derivative. The control gain is not monotonically non-decreasing as in existing techniques, but it is increased or decreased depending on the imposed derivative, thus avoiding the well-known issue of actuator over-exploitation. We are able to show robust convergence of the proposed adaptive control scheme as well as other interesting properties. For instance, it is possible to guarantee an a-priori given upper bound for the transient mode of behavior during adaptation. Furthermore, if the control Lyapunov function is designed based on an optimal control problem, then the control action is nominally optimal, precisely it yields the optimal trajectory for any initial condition, if the actual plant matches the nominal system.

A Unified Approach to High-Gain Adaptive Controllers

It has been known for some time that proportional output feedback will stabilize MIMO, minimum-phase, linear time-invariant systems if the feedback gain is sufficiently large. High-gain adaptive controllers achieve stability by automatically driving up the feedback gain monotonically. More recently, it was demonstrated that sample-and-hold implementations of the high-gain adaptive controller also require adaptation of the sampling rate. In this paper, we use recent advances in the mathematical field of dynamic equations on time scales to unify and generalize the discrete and continuous versions of the high-gain adaptive controller. We prove the stability of high-gain adaptive controllers on a wide class of time scales.

B15 ハイゲイン適応フィードバックによる不確かな非ホロノミックシステムの制御(OS9 適応学習制御とその応用)

B15 ハイゲイン適応フィードバックによる不確かな非ホロノミックシステムの制御(OS9 適応学習制御とその応用)

Robust Inventory Control System

Further developments of the inventory control strategy are studied in this brief. We use high gain (sliding mode) adaptive control to handle the system uncertainties caused by modeling errors and unmeasured disturbances. It is proven that this control law makes the uncertain system globally stable. The parameters of the resulting controller are easy to tune. The simulation example and experiment studies illustrate the application of the novel method.

Internal models in sensorimotor integration: perspectives from adaptive control theory

Internal models and adaptive controls are empirical and mathematical paradigms that have evolved separately to describe learning control processes in brain systems and engineering systems, respectively. This paper presents a comprehensive appraisal of the correlation between these paradigms with a view to forging a unified theoretical framework that may benefit both disciplines. It is suggested that the classic equilibrium-point theory of impedance control of arm movement is analogous to continuous gain-scheduling or high-gain adaptive control within or across movement trials, respectively, and that the recently proposed inverse internal model is akin to adaptive sliding control originally for robotic manipulator applications. Modular internal models' architecture for multiple motor tasks is a form of multi-model adaptive control. Stochastic methods, such as generalized predictive control, reinforcement learning, Bayesian learning and Hebbian feedback covariance learning, are reviewed and their possible relevance to motor control is discussed. Possible applicability of a Luenberger observer and an extended Kalman filter to state estimation problems—such as sensorimotor prediction or the resolution of vestibular sensory ambiguity—is also discussed. The important role played by vestibular system identification in postural control suggests an indirect adaptive control scheme whereby system states or parameters are explicitly estimated prior to the implementation of control. This interdisciplinary framework should facilitate the experimental elucidation of the mechanisms of internal models in sensorimotor systems and the reverse engineering of such neural mechanisms into novel brain-inspired adaptive control paradigms in future.

Two scale high gain adaptive control

AbstractSimple adaptive controllers based on high gain output feedback suffer a lack of robustness with respect to bounded disturbances. Existing modifications achieve boundedness of all solutions but introduce solutions that, even in the absence of disturbances, do not achieve regulation. In this paper a new modification that achieves the desired robustness without the side‐effect of undesirable solutions is proposed. The modification features two data‐driven stability indicators. Intuitively, the first indicator drives the ‘closed‐loop poles’ to the closed right half plane while the latter provides fine‐tuning that prevents persistent imaginary axis poles and their associated undesired behaviour. Copyright © 2004 John Wiley & Sons, Ltd.

Adaptive high-gain λ-tracking with variable sampling rate

It is well known that proportional output feedback control can stabilize any relative-degree one, minimum-phase system if the sign of the feedback is correct and the proportional gain is high enough. Moreover, there exists simple adaptation laws for tuning the proportional gain (the so-called high-gain adaptive controllers) which are not based on system identification or plant parameter estimation algorithms or injection of probing signals. If tracking of signals is desired, then these simple controllers are also applicable without invoking an internal model if the tracking error is not necessarily supposed to converge to zero but towards a ball around zero of arbitrarily small but prespecified radius λ>0. In this note we consider a sampled version of the high-gain adaptive λ-tracking controller. The motivation for sampling arises from the possibility that the output of a system may not be available continuously, but only at discrete time instants. The problem is that the stiffness of the system increases as the proportional gain is increased. Our result shows that adaptive sampling tracking is possible if the product hk of the decreasing sampling rate h and the increasing proportional gain k decreases at a rate proportional to 1/ log k .

Adaptive sampling control of high-gain stabilizable systems

It is well known that proportional output feedback control can stabilize any relative-degree one, minimum-phase system if the sign of the feedback is correct and the proportional gain is high enough. Moreover, there exist simple adaptation laws for tuning the proportional gain (so-called high-gain adaptive controllers) which do not need to know the system and do not attempt to identify system parameters. The authors consider sampled versions of the high gain adaptive controller. The motivation for sampling arises from the possibility that the output of a system may not be available continuously, but only at sampled times. The main point of interest is the need to develop techniques for adapting the sampling rate, since the stiffness of the system increases as the proportional gain is increased. Our main result shows that adaptive sampling stabilization is possible if the product hk of the decreasing sampling interval h and the increasing proportional gain k decreases at a rate proportional to 1/log k.

Decentralized Sampled-Data Control of Interconnected Systems

This paper presents a decentralized adaptive stabilization scheme for a class of interconnected systems using high-gain adaptive controllers. The nominal subsystems are assumed to satisfy some mild conditions required by standard adaptive control schemes, and the interconnections certain structural conditions. The decentralized controllers operate on sampled values of local outputs. The sampling frequencies of the controllers also serve as their gains. The controllers are synchronized in that their sampling frequencies are integer multiples of a common base frequency. The base frequency (hence the common gain) is adjusted by a simple adaptation rule that results in a stable closed-loop system with bounded controller gains.

Adaptive Stabilization of Minimum Phase Plants with Relative Degree n or n+1

This paper proposes a robust high gain adaptive stabilization of minimum phase plants with its relative degree n or n+1. The proposed method uses the fact that any minimum phase plant with its relative degree zero or one can be stabilized by a high gain adaptive control and extends this result by using backstepping. The σ-modification is applied in order to avoid an unnecessarily high gain feedback.

Adaptive Stabilization of any Minimum Phase Plant with Relative Degree N or N + 1

This paper proposes a robust high gain adaptive stabilization of any minimum phase plant with its relative degree n or n + 1. The proposing method uses the fact that any minimum phase plant with its relative degree zero or one can be stabilized by a high gain adaptive control and extends this result by using backstepping. The σ-modification is applied in order to avoid an unnecessarily high gain feedback.