Control Of MIMO Nonlinear Systems Research Articles

A Unified Approach for Tracking Control of MIMO Nonlinear Systems Under Unknown Control Directions and Irregular Constraints

A Unified Approach for Tracking Control of MIMO Nonlinear Systems Under Unknown Control Directions and Irregular Constraints

SDO-Based Command Filtered Adaptive Neural Tracking Control for MIMO Nonlinear Systems With Time-Varying Constraints.

In this article, an adaptive neural tracking control based on saturation disturbance observer (SDO) and command filter is studied for multiple-input-multiple-output nonlinear systems with time-varying constraints and system uncertainties. By employing neural networks (NNs), the system uncertainties are approximated. The SDO is proposed to estimate the composited disturbances which consist of NN approximation errors and the external bounded disturbances. Compared with the traditional disturbance observer, the SDO can reduce the estimation error to some extent. The control requirements are achieved based on the multiconstraints which contain three layers: 1) prescribed performance functions (PPFs); 2) actual constraints; and 3) virtual constraints. The errors remain within the prescribed small neighborhood of zero by using the PPFs, the error constraints ensure that the time-varying constraints are never violated even if the PPFs are not available, and the virtual constraints are applied in a new time-varying barrier Lyapunov function (TVBLF) to design virtual controllers and controller to solve the singularity problem of the traditional TVBLF. In addition, the command filter is introduced to solve the problem of "explosion of complexity." Finally, a numerical simulation verifies the effectiveness of the proposed scheme for a flight control of unmanned aerial vehicle.

Saturation-tolerant prescribed control of MIMO nonlinear systems with actuator faults

This paper addresses the tracking control problem of a class of multiple-input-multiple-output nonlinear systems subject to actuator faults. Achieving a balance between input saturation and performance constraints, rather than conducting isolated analyses, especially in the presence of frequently encountered unknown actuator faults, becomes an interesting yet challenging problem. First, to enhance the tracking performance, Tunnel Prescribed Performance (TPP) is proposed to provide narrow tunnel-shape constraints instead of the common over-relaxed trumpet-shape performance constraints. A pair of non-negative signals produced by an auxiliary system is then integrated into TPP, resulting in Saturation-tolerant Prescribed Performance (SPP) with flexible performance boundaries that account for input saturation situations. Namely, SPP can appropriately relax TPP when needed and decrease the conservatism of control design. With the help of SPP, our developed Saturation-tolerant Prescribed Control (SPC) guarantees finite-time convergence while satisfying both input saturation and performance constraints, even under serious actuator faults. Simulations are conducted to illustrate the effectiveness of the proposed SPC.

Learning-Based Adaptive Fuzzy Output Feedback Control for MIMO Nonlinear Systems With Deception Attacks and Input Saturation

Learning-Based Adaptive Fuzzy Output Feedback Control for MIMO Nonlinear Systems With Deception Attacks and Input Saturation

Observer-Based Adaptive Fuzzy Tracking Control for MIMO Nonlinear Systems with Asymmetric Time-Varying Constraints

Observer-Based Adaptive Fuzzy Tracking Control for MIMO Nonlinear Systems with Asymmetric Time-Varying Constraints

Singularity-Free Adaptive Fixed-Time Tracking Control for MIMO Nonlinear Systems With Dynamic Uncertainties

Singularity-Free Adaptive Fixed-Time Tracking Control for MIMO Nonlinear Systems With Dynamic Uncertainties

Adaptive fault-tolerant tracking control for MIMO nonlinear systems with time-varying full state constraints

In this article, an adaptive fault-tolerant tracking control algorithm is developed for the multi-input-multi-output (MIMO) pure-feedback nonlinear systems with full state constraints. The state constraints are time-varying, hence greatly improve the applicability of the result but significantly increase the difficulties and complexities of control system design. To overcome the feasibility conditions in barrier Lyapunov function for handling time-varying constraints, a novel nonlinear mapping method for MIMO nonlinear systems is developed. Adaptive techniques and Neural Networks (NNs) are applied to estimate unknown uncertainties and disturbances, which the tracking control accuracy is improved. It is proved that the designed control algorithm can not only achieve the control objectives but also guarantee the boundedness of all signals. Finally, the effectiveness of the developed control algorithm is demonstrated via a simulation example.

Observer-based adaptive backstepping control for Mimo nonlinear systems with unknown hysteresis: a nonlinear gain feedback approach

Observer-based adaptive backstepping control for Mimo nonlinear systems with unknown hysteresis: a nonlinear gain feedback approach

Command Filter-Based Adaptive Fuzzy Self-Triggered Control for MIMO Nonlinear Systems with Time-Varying Full-State Constraints

Command Filter-Based Adaptive Fuzzy Self-Triggered Control for MIMO Nonlinear Systems with Time-Varying Full-State Constraints

Finite-time-prescribed performance-based adaptive command filtering control for MIMO nonlinear systems with unknown hysteresis

Finite-time-prescribed performance-based adaptive command filtering control for MIMO nonlinear systems with unknown hysteresis

MTN-based recursive d-step-ahead predictive control of MIMO nonlinear systems with unknown input time-delay in industrial process

PurposeThis paper aims to provide a precise tracking control scheme for multi-input multi-output “MIMO” nonlinear systems with unknown input time-delay in industrial process.Design/methodology/approachThe predictive control scheme based on multi-dimensional Taylor network (MTN) model is proposed. First, for the unknown input time-delay, the cross-correlation function is used to identify the input time-delay through just the input and output data. And then, the scheme of predictive control is designed based on the MTN model. It goes as follows: a recursive d-step-ahead MTN predictive model is developed to compensate the influence of time-delay, and the extended Kalman filter (EKF) algorithm is applied for its learning; the multistep predictive objective function is designed, and the optimal controlled output is determined by iterative refinement; and the convergence of MTN predictive model and the stability of closed-loop system are proved.FindingsSimulation results show that the proposed scheme is of desirable generality and capable of performing the tracking control for MIMO nonlinear systems with unknown input time-delay in industrial process effectively, such as the continuous stirred tank reactor (CSTR) process, which provides a considerably improved performance and effectiveness. The proposed scheme promises strong robustness, low complexity and easy implementation.Research limitations/implicationsFor the limitations of proposed scheme, the time-invariant time-delay is only considered in time-delay identification and control schemes. And the CSTR process is only introduced to prove that the proposed scheme can adapt to practical industrial scenario.Originality/valueThe originality of the paper is that the proposed MTN control scheme has good tracking performance, which solves the influence of time-delay, coupling and nonlinearity and the real-time performance for MIMO nonlinear systems with unknown input time-delay.

A multi-objective criterion and stability analysis for neural adaptive control of nonlinear MIMO systems: an experimental validation

A multi-objective criterion and stability analysis for neural adaptive control of nonlinear MIMO systems: an experimental validation

Exponential tracking control of MIMO nonlinear systems with actuator failures and nonparametric uncertainties

AbstractExponential tracking control of multi‐input multi‐output nonlinear systems is of great importance to system performance, which however poses significant difficulty in control design. The problem becomes even more challenging if actuator failures and nonparametric uncertainties are present. In this article, we apply the so‐called exponential‐transformation to the nonlinear dynamics and derive a robust control algorithm based on the transformed dynamics to achieve exponential tracking. In contrast to most of the existing works which consider prescribed performance, the developed control design method possesses the following features: (1) it achieves robust exponential tracking against actuator failures and nonparametric uncertainties; (2) it allows arbitrarily assignable convergence rate which is independent of the initial conditions; (3) it easily handles actuator failures under very mild assumptions on the high‐frequency gain matrix, which significantly relaxes the traditional design methods; and (4) no approximation using neuro/fuzzy systems is required, making the control algorithm simple and easy to implement. A robotic manipulator control example is employed to validate the proposed methodology.

Adaptive event‐triggered control for MIMO nonlinear systems with asymmetric state constraints based on unified barrier functions

AbstractThe topic of adaptive event‐triggered control for multi‐input‐multi‐output nonlinear systems with asymmetric state constraints is considered in this article. First, by introducing unified barrier functions method, the initial system is transformed to a non‐constraints system, which brings that the requirement of feasibility conditions could be eliminated and the constraint functions could be relaxed effectively. Then, an adaptive tracking controller is designed by combining some excellent technology, where command filter is utilized to overcome the explosion of complexity, neural network is introduced to approximate unknown nonlinear function, and event‐triggered mechanism is proposed to save communication resources. The designed control scheme can make the outputs of the system to track the target trajectories within a small bounded error range, all signals in the closed‐loop system are bounded, and all states do not escape the state constraints. Finally, practical and comparative examples are given to verify the effectiveness of the method.

Model Predictive Control of Nonlinear MIMO Systems Based on Adaptive Orthogonal Polynomial Networks

This paper considers a new design of model predictive control based on specific models in the form of adaptive orthogonal polynomial networks, built around a specially tailored basis of generalized orthogonal functions. Polynomial model has a single layer structure and a smaller number of model parameters than classical neural networks, usually used for model predictive control design, leading to lower complexity and shorter calculation time. Desired property of adaptability of the model is achieved by using additional variable factors inside the orthogonal basis. The designed controller was applied in control of twin-rotor aero-dynamic system as a representative of nonlinear multiple input-multiple output systems and compared to the other state-of-the-art control algorithms.

Observer‐based fuzzy adaptive control for MIMO nonlinear systems with non‐constant control gain and input delay

Observer‐based fuzzy adaptive control for MIMO nonlinear systems with non‐constant control gain and input delay

Improved Active Disturbance Rejection-Based Decentralized Control for MIMO Nonlinear Systems: Comparison with The Decoupled Control Scheme

A decentralized control scheme is developed in this paper based on an improved active disturbance rejection control (IADRC) for output tracking of square Multi-Input-Multi-Output (MIMO) nonlinear systems and compared with the decoupled control scheme. These nonlinear MIMO systems were subjected to exogenous disturbances and composed of high couplings between subsystems, input couplings, and uncertain elements. In the decentralized control scheme, it was assumed that the input couplings and subsystem couplings were both parts of the generalized disturbance. Moreover, the generalized disturbance included other components, such as exogenous disturbances and system uncertainties, and it was estimated within the context of Active Disturbance rejection Control (ADRC) via a novel nonlinear higher order extended state observer (NHOESO) from the measured output and canceled from the input channel in a real-time fashion. Then, based on the designed NHOESO, a separate feedback control law was developed for each subsystem to achieve accurate output tracking for given reference input. With the proposed decentralized control scheme, the square MIMO nonlinear system was converted into approximately separate linear time invariant Single-Input-Single-Output (SISO) subsystems. Numerical simulations in a MATLAB environment showed the effectiveness of the proposed technique, where it was applied on a hypothetical MIMO nonlinear system with strong couplings and vast uncertainties. The proposed decentralized control scheme reduced the total control signal energy by 20.8% as compared to the decoupled control scheme using Conventional ADRC (CADRC), while the reduction was 27.18% using the IADRC.

Iterative Learning Control for MIMO Nonlinear Systems With Iteration-Varying Trial Lengths Using Modified Composite Energy Function Analysis.

Most works dealing with trajectory tracking problems in the iterative learning control (ILC) literature assume an iteration domain that has identical trial lengths. This fundamental assumption may not always hold in practical applications. To address iteration-varying trial lengths, a new structure of ILC laws has been presented in this article, based on the newly proposed modified composite energy function (mCEF) analysis. The proposed approach is a feedback control scheme that utilizes tracking errors in the current iteration, so that it can deal with iteration-varying system uncertainties. It is also a unified framework such that the traditional ILC problems with identical trial lengths are shown to be a special case of the more general problem considered in this article. Multi-input-multi-output (MIMO) nonlinear systems are considered, which can be subject to parametric system uncertainties and an unknown control input gain matrix function. We show that in the closed loop analysis, the proposed control scheme can guarantee asymptotic convergence on the full-state tracking error over the iteration domain, in the sense of the L2Tk norm, with Tk being the trial length of the k th iteration. In the end, a simulation example is shown to illustrate the efficacy of the proposed ILC algorithm.

Adaptive fuzzy control of MIMO nonlinear systems with fuzzy dead zones

In this paper, the problem of adaptive fuzzy tracking control is investigated for a class of multi-input multi-output nonlinear systems with fuzzy dead zones. The virtual control gain functions and uncertain functions considered in the studied system are all unknown. Fuzzy logic systems are employed to approximate the unknown functions. With the combination of adaptive backstepping design technique and dynamic surface control method, the problem caused by differentiating nonlinear functions repeatedly is avoided. Furthermore, only one adaptive parameter needs to be updated online for each subsystem, which reduces the computation burden considerably. The presented controller not only guarantees the desired control performance, but also guarantees the boundedness of all closed-loop signals. Simulation results are shown to demonstrate the effectiveness of the proposed algorithm.

Low‐complexity adaptive tracking control of MIMO nonlinear systems with unknown control directions

SummaryThis paper is concerned with the tracking control problem for a class of multiple‐input–multiple‐output systems with unmatched disturbances and the unknown additive and multiplicative nonlinearities. The objective is to provide a low‐complexity control solution in the sense that (i) approximating structures are not involved, despite unknown nonlinearities and (ii) iterative calculations of command derivatives are avoided in the backstepping design. A robust adaptive control strategy is proposed to fulfill the task. In the control design, a new‐type adaptive law is first developed to update Nussbaum gains to handle control direction uncertainties, while ensuring Nussbaum gains bounded. Then, the potential robustness of error constraint techniques is exploited to counteract the effects of unknown nonlinearities and disturbances and achieve predefined transient and steady‐state tracking performance. Finally, simulation results are given to illustrate the above theoretical findings.