Adaptive regulation for minimum phase systems with unknown relative degree and uncertain exosystems

Robust Adaptive Control

Some recent results on output regulation for linear systems allow for unknown exosystems, provided that the systems are known, and propose an adaptive internal model. We present a new result which applies to uncertain minimum phase linear systems with known bounds, known relative degree and high frequency gain sign and for exosystems with unknown frequencies with known bounds. A new robust adaptive output error feedback control algorithm is proposed which achieves asymptotic regulation with bounded closed loop signals. Exponential regulation is obtained when all of the exosystem frequencies are excited.

We consider parameter-monotonic direct adaptive control for single-input-single-output minimum-phase linear time-invariant systems with knowledge of the sign of the high-frequency gain (first nonzero Markov parameter) and an upper bound on the magnitude of the high-frequency gain. The first part of the paper is devoted to fixed-gain analysis of single-parameter high-gain-stabilizing controllers. Two novel fixed-gain dynamic compensators are presented for stabilizing minimum-phase systems. One compensator stabilizes systems with arbitrary-but-known relative degree, while the other utilizes a Fibonacci series construction to stabilize systems with unknown-but-bounded relative degree. Next, we provide a general treatment of parameter-monotonic adaptive control, including a result that guarantees state convergence to zero. This result is then combined with the high-gain-stabilizing controllers to yield parameter-monotonic direct adaptive dynamic compensation for minimum-phase systems with either arbitrary-but-known or unknown-but-bounded relative degree

The design of an adaptive output feedback compensator is addressed to reject biased multi-sinusoidal disturbances of unknown amplitudes, phases and frequencies, acting on unknown stable single-input single-output linear systems of unknown order and relative degree. A single biased sinusoidal disturbance is first considered. It is shown that the knowledge of the sign of the real part (or the imaginary part) of its transfer function at zero and at the disturbance frequency is sufficient for the explicit design of an adaptive compensator which guarantees local exponential convergence to zero of the system output and of the frequency estimation error. This result is generalized to the case of biased multi-sinusoidal disturbances. As a special case, an adaptive notch filter which provides the unknown frequencies contained in the disturbance is obtained.

SummaryThis paper is devoted to the global stabilization via output feedback for a class of nonlinear systems with unknown relative degree, dynamics uncertainties, unknown control direction, and nonparametric uncertain nonlinearities. In particular, the unknown relative degree is without known upper bound, which renders us to research for a filter with varying dimension rather than the ones with over dimensions in the existing literature. In comparison with more popular but a bit stronger input‐to‐state stable or input‐to‐state practically stable requirement, only bounded‐input bounded‐state stable requirement is imposed on the dynamics uncertainties, which affect the systems in a persistent intensity rather than in a decaying one. In this paper, to compensate multiple serious system uncertainties and realize global output‐feedback stabilization, a design scheme via switching logic together with varying dimensional filter is developed. In this scheme, 2 switching sequences, which separately generate the gains of the controller and act as the varying dimensions of the filter, are designed to overcome unknown control direction, dynamics uncertainties and nonparametric uncertain nonlinearities, and unknown relative degree, respectively. A 2‐mass lumped‐parameter structure is provided to show the effectiveness of the proposed method in this paper.

In this article, a policy-iteration-based active disturbance rejection control (ADRC) is proposed for uncertain nonlinear systems to achieve real-time output tracking performance, regardless of the specific relative degree of the system. The approach integrates a partial control input generator with a policy-iteration-based reinforcement learning (RL) agent for degree weight adjustment. The partial control input generator includes each ith order partial control input, which is constructed following the ADRC design framework for an ith order system. The RL agent adjusts the degree weights (its actions) to enhance the dominance of the partial control input corresponding to the unknown relative degree through iterative policy refinement. The RL agent is designed to minimize the quadratic reward as the performance index function while enhancing the influence of the partial control input associated with the correct relative degree via the policy iteration procedure. All signals in the closed-loop system (including the time-varying degree weights) ensure semi-global uniformly ultimately boundness using the Lyapunov stability theorem and the affinely quadratically stable property. Consequently, the degree weight adjustments by the RL agent do not affect the closed-loop stability. The proposed method does not require system dynamics, specific relative degree, external disturbances, and other state variable sensing beyond output sensing. The performance of the proposed method was validated via simulations for two different-order uncertain nonlinear systems and experiments using a permanent magnet synchronous motor testbed.

Output-feedback binary adaptive control of MIMO systems using monitoring function

This paper presents the optimal control modification for linear uncertain plants. The Lyapunov analysis shows that the modification parameter has a limiting value depending on the nature of the uncertainty. The optimal control modification exhibits a linear asymptotic property that enables it to be analyzed in a linear time invariant framework for linear uncertain plants. The linear asymptotic property shows that the closed-loop plants in the limit possess a scaled input-output mapping. Using this property, we can derive an analytical closed-loop transfer function in the limit as the adaptive gain tends to infinity. The paper revisits the Rohrs counterexample problem that illustrates the nature of non-robustness of model-reference adaptive control in the presence of unmodeled dynamics. An analytical approach is developed to compute exactly the modification parameter for the optimal control modification that stabilizes the plant in the Rohrs counterexample. The linear asymptotic property is also used to address output feedback adaptive control for non-minimum phase plants with a relative degree 1.

This paper addresses the problem of designing an output error feedback control for single-input, single-output nonlinear systems with uncertain, smooth, output-dependent nonlinearities whose local Lipschitz constants are known. The considered systems are required to be observable, minimum phase with known relative degree and known high frequency gain sign: linear systems are included. The reference output signal is assumed to be smooth and periodic with known period. By developing in Fourier series expansion a suitable periodic input reference signal, an output error feedback adaptive learning control is designed which ldquolearnsrdquo the input reference signal by identifying its Fourier coefficients: bounded closed loop signals and exponential tracking of both input and output reference signals are obtained when the Fourier series expansion is finite, while arbitrary small tracking errors are exponentially achieved otherwise. The resulting control is not model based, is independent of the system order and depends only on the relative degree, the reference signal period and the high frequency gain sign.

For a single-output linear time-invariant system, output controllability is shown to be a necessary and sufficient condition for the existence of linear state variable feedback that freely assigns the eigenvalues of the closed-loop system that are observable from the output. An application of this result to the single-output linear servomechanism shows that the ability of such a system to track with zero steady-state error and an arbitrary transient response is equivalent to output controllability of the plant, and is independent of the class of command signals and disturbances.

SummaryThis paper investigates the robust resilient control problem for a class of parametric strict feedback nonlinear systems with prescribed output and virtual tracking errors performance. The resilience is governed by a continuously nonlinear control gains function, which endows the virtual and actual controllers with self‐adjusting abilities with respect to transformed error surfaces. The proposed control scheme is adaptation, estimation, and approximation‐free in the presence of unknown parameters and nonlinearities, and only a number of control gains, which is equal to the relative degree of the considered plants, need to be selected in applications. It is proved by rigorous analysis that the output tracking errors are confined in predefined prescribed performance functions under some nonrestrictive initial conditions, and the bounds of states are obtained characterized as control gains and systemic function‐associated constants. Finally, comparative illustrative examples are given to demonstrate the effectiveness of the proposed control scheme.

Recently, variable structure design (VSD) has been intensively researched in the literature of model reference adaptive control (MRAC) for its well-behaved transient response and appealing tracking performance. In the recent work by Hsu [6], parameter switching mechanism has been introduced in the design of model reference controller to obtain better transient behavior. By taking a different viewpoint, we apply VSD to the control channel, and develop a new robust model reference adaptive controller for a class of uncertain plants with arbitrary relative degree. It is shown that in the presence of additive, multiplicative unmodeled dynamics, and bounded disturbance, the output tracking error can still be guaranteed to converge to zero with an exponential rate. Since the parameter update law is introduced in the synthesis of this new adaptive control law, the magnitude of the switching control forces can be reduced to a more realistic level for implementation. To highlight the improved performance, simulation results are conducted with an uncertain plant with relative degree two.

Regulation of Linear Systems with Unknown Additive Sinusoidal Sensor Disturbances

We consider the problem of rejection of a sinusoidal disturbance of unknown frequency, phase and magnitude acting on an uncertain internally stable SISO linear system. We present a solution that extends our previous work on adaptive feedforward control of uncertain systems by disposing of the need to know the frequency of the harmonic disturbance. The proposed methodology reposes upon a switching-based combination of an adaptive feedforward control algorithm and a deadbeat frequency estimator. The method accounts for the presence of bounded sensor noise as well as imprecise frequency estimation; it is shown that the regulation error is bounded by a function of the norm of the noise that depends on the choice of the controller and the estimator gains.

In this paper, a new distributed model reference adaptive control (MRAC) design framework is proposed for containment control of heterogeneous uncertain multi-agent systems (MAS). Both groups of leaders and followers are considered to have general linear dynamics with the leaders subject to bounded external inputs and the followers subject to uncertain system dynamics. A novel distributed adaptive control protocol is developed under this new framework, which incorporates robust adaptive control with nonlinear compensator techniques to cope with unmeasurable bounded leaders' inputs and uncertain agent dynamics. Convergence of the containment control errors to an arbitrarily adjustable neighborhood of the origin is guaranteed based on the Lyapunov method. The proposed MRAC framework provides a promising alternative solution over the prevailing cooperative output regulation framework for heterogeneous linear MAS containment control. It enables us to handle more general system settings under more stringent control environments with limited accessibility of leaders' information and uncertain follower dynamics.