Search arXivSearch

arXiv · 2504.14486

Online Optimal Parameter Compensation method of High-dimensional PID Controller for Robust stability

Abstract

Classical PID control is widely applied in an engineering system, with parameter regulation relying on a method like Trial - Error Tuning or the Ziegler - Nichols rule, mainly for a Single - Input Single - Output (SISO) system. However, the industrial nonlinear Multiple - Input Multiple - Output (MIMO) system demands a high - robustness PID controller due to strong state coupling, external disturbances, and faults. Existing research on PID parameter regulation for a nonlinear uncertain MIMO system has a significant drawback: it's limited to a specific system type, the control mechanism for a MIMO nonlinear system under disturbances is unclear, the MIMO PID controller over - relies on decoupled control, and lacks dynamic parameter compensation. This paper theoretically analyzes a high - dimensional PID controller for a disturbed nonlinear MIMO system, providing a condition for online dynamic parameter regulation to ensure robust stability. By transforming the parameter regulation into a two - stage minimum eigenvalue problem (EVP) solvable via the interior point method, it enables efficient online tuning. The experiment proves that the designed dynamic compensation algorithm can achieve online robust stability of system errors considering multi - channel input coupling, addressing the key limitation in the field.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zimao Sheng, Hong'an Yang. 2025-04-20. Online Optimal Parameter Compensation method of High-dimensional PID Controller for Robust stability. https://arxiv.org/abs/2504.14486

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Ensuring Stability of Non-Minimal Modes in Input-Output Data-Driven Representation

Many recent data-driven control approaches for linear time-invariant systems are based on output trajectory prediction using input-output data matrices. The system dynamics described by this predictor, which we refer to as the input-output data-driven representation, yields non-unique autoregressive with exogenous inputs (ARX) models having possibly unstable non-minimal modes. In this note, we show that the stability of these non-minimal modes is ensured by a certain choice of ARX model, which coincides with the minimum-norm least-squares predictor using the Moore-Penrose inverse of the data matrix. This stability guarantee holds regardless of the underlying system's stability. Moreover, the stability persists under sufficiently small noise in data when a suitably truncated Moore-Penrose inverse is used. Consequently, the ARX model need not be reduced to the true system order in order to avoid unstable additional modes.

eess.SY

Optimization-Based Formation Flight on Libration Point Orbits

A model predictive control (MPC) framework is developed for station-keeping in spacecraft formation flight along libration point orbits. At each control period, the MPC policy solves a multi-vehicle optimal control problem (MVOCP) that tracks a reference trajectory, while enforcing path constraints on the relative motion of the formation. The control policy makes use of a limited set of control nodes consistent with operational constraints that allow only a small number of maneuver opportunities per revolution. To promote recursive feasibility, path constraints are progressively tightened across the prediction horizon. An isoperimetric reformulation of the constraints is used to prevent inter-sample violations. The resulting MVOCP is a nonconvex program, which is solved via sequential convex programming. The proposed approach is evaluated in a high-fidelity ephemeris model under uncertainties for a formation along the near-rectilinear halo orbit (NRHO), and subject to path constraints on inter-spacecraft separation and relative Sun phase angle. The results demonstrate maintenance of a spacecraft formation that satisfies the path constraints with realistic cumulative propellant consumption.

eess.SY

Certificates Synthesis for A Class of Observational Properties in Stochastic Systems: A Unified Approach

In this paper, we investigate the probabilistic formal verification of stochastic dynamical systems over continuous state spaces. Motivated by problems in state estimation and information-flow security, we introduce the notion of observational properties, which characterize the inferences an external observer can draw from system outputs. These properties are formulated as probabilistic hyperproperties based on HyperLTL over finite traces, yielding a unified framework that subsumes several existing notions studied separately in the literature. We reduce the verification problem to reachability analysis over an augmented structure that integrates the system dynamics with an automaton representation of the specification. Building on this construction, we develop stochastic barrier certificates that provide probabilistic guarantees for property satisfaction while avoiding explicit state-space discretization. The effectiveness of the proposed framework is demonstrated through a case study.

eess.SY