Multiple Model Adaptive Control Research Articles

Multiple-model adaptive explicit predictive control for nonlinear MIMO system

This work is to develop a blending-based multiple model adaptive explicit predictive control scheme for nonlinear MIMO systems that can handle parametric uncertainties. Here, for each identification model, an explicit nonlinear model predictive control (ENMPC) law is computed in advance for the corresponding model. The generated control inputs from the set of ENMPC controllers are being blended online using a weighting vector that is continuously updated by the proposed adaptive identification schemes. The proposed control scheme is used to govern the tracking of a highly nonlinear helicopter model known as the twin rotor MIMO system (TRMS). Here, an extended Kalman filter (EKF) is used to estimate the unavailable states of the TRMS. Finally, simulation and experimental results are presented to prove that the proposed controller gives better performance than some reported works in the literature. The effectiveness of the proposed controller is demonstrated by experimental studies of the TRMS model.

Improving Performance of Flight Control System in Presence of Uncertainties and Extreme Changes in Parameters using Multiple Model Adaptive Control

Improving Performance of Flight Control System in Presence of Uncertainties and Extreme Changes in Parameters using Multiple Model Adaptive Control

Exploring Unknown Plant Configurations under a Multiple Model Adaptive Control Framework

Multiple model adaptive control (MMAC) provides robust control guarantees for a range of plant configurations given a set of models that correspond to known operating conditions (modes). However, these guarantees may not hold in the face of unknown system configurations. This paper focuses on the first step of control under novel modes: the task of novel configuration detection. We analyze a change-point detection strategy for unknown system configurations using input/output data as input. We characterize the performance of the change-point detection strategy based on whether the performance loss is bounded or unbounded. We experimentally validate our algorithms using a quadcopter trajectory-tracking benchmark, comparing our approach to a Bayesian change-point detection strategy.

Automated controller tuning for Weighted Multiple Model Adaptive Control

Multiple model adaptive control (MMAC) uses models of different operating modes and optimizes a set of system controllers that can ensure stability over all modes. Traditionally, one controller is tuned for every operating mode but the number of operating modes and all their combinations increase exponentially. We present a novel convex-hull-based optimization algorithm that automatically generates a controller parameter set for a set of operating modes. The main contribution of the presented algorithm is that it can find a smaller controller set than the traditional one-controller per operating mode tuning approach, which we demonstrate empirically on a quadcopter trajectory tracking simulation using five different operating modes. The presented algorithm achieves this by constructing a convex hull in the controller parameter space to ensure the chosen parameters are affinely independent, which results in a set of only three controllers for five investigated operating modes without a significant loss in performance.

Multiple Model Adaptive Control of Nonlinear Systems Based on Dynamic Optimization

Multiple Model Adaptive Control of Nonlinear Systems Based on Dynamic Optimization

A switching-free multiple-model approach for adaptive FTC of non-Lipschitz nonlinear uncertain systems under persistent actuator failures

A novel actuator failure compensation scheme is proposed for affine nonlinear uncertain systems (not necessarily Lipschitz) subject to persistent/intermittent actuator faults/failures unknown in time, magnitude and pattern. The proposed control methodology satisfies the nonlinear separation principle through a modular backstepping control. The controller is then augmented with multiple estimation models to estimate failure induced parametric uncertainties and unknown system parameters. The output transient performance at start up and post-failure instances, is improved on account of a two layer adaptation which enhances the convergence speed and accuracy of parameter estimates. The proposed fault tolerant control (FTC) method yields a faithful accommodation of uncertain finite as well as infinite/intermittent/persistent actuator failures while ensuring satisfactory output transient and steady state performances. Further, compared to existing multiple model based adaptive fault tolerant control design for nonlinear systems, the proposed methodology circumvents the issues of stability due to switching between different models and utilizes a minimum number of estimation models for parameter estimation without compromising on the output performance. Consequently, the computational burden is also reduced. Compared to multiple model adaptive control based FTC strategies proposed earlier which assume finite actuator failures and Lipschitz nonlinear system, the proposed method is applicable to both Lipschitz and non-Lipschitz nonlinear systems affected by intermittent actuator failures. Using the concepts from stability analysis in random nonlinear impulsive systems, the L∞ and L2 bounds on tracking error for all future time are derived in the case of intermittent/persistent actuator failures obtained using the proposed fault-tolerant controller. The improvement of output transient performance in the proposed control scheme in comparison with controller with single identifier, is theoretically proved and quantified.

A method of multiple model adaptive control of affine systems and its application to aero-engines

This paper investigates the multiple model adaptive control problem of affine systems with unknown parameters. Firstly, an adaptive controller with resettable parameters and an adaptive law with projection function are designed to ensure the asymptotic tracking for the reference system and the boundedness of parameters. Secondly, a transformation of system is given to enable a finite-time parameter estimator to calculate the uncertain parameters in the system matrix and the affine item simultaneously. Then, a novel performance index to describe the error between the controlled plant and the identification model is given to orchestrate switchings among identification models aiming to choose the best one. Next, the sufficient condition of the asymptotic convergence for the system error is given. Finally, all designs are evaluated in a hardware-in-the-loop simulation platform of an aero-engine control system and compared with three other methods, the effectiveness and superiority are verified.

Multiple-model adaptive control architecture for a quadrotor with constant unknown mass and inertia

This paper proposes an architecture based on a multiple-model solution for height and yaw control of a quadrotor transporting an unknown constant load, added before the flight. Estimates of the inertial parameters (mass and z-axis inertia) and state variables (vertical position, velocity, yaw angle and rate) are obtained using data from the onboard sensors. A Multiple-Model Adaptive Controller (MMAC) architecture is proposed. The control of each partial model is based on a steady state Linear Quadratic Regulator (LQR), using integrative action for the height control. The resulting system is validated with load variations of up to 10% of the vehicle mass, both in simulation and experimentally, resorting to an off-the-shelf commercially available quadrotor.

Blending based multiple-model adaptive control of multivariable systems with application to lateral vehicle motion control

This paper develops multiple fixed model blending based adaptive parameter identification schemes for multi-input multi-output (MIMO) systems with polytopic parameter uncertainty. The developed identification schemes are proven to be asymptotically stable for uncertain linear time-invariant (LTI) MIMO systems, and is shown to provide fast adaptation for even uncertain linear time-varying (LTV) systems. Furthermore, utilizing the proposed parameter identification schemes, a linear-quadratic (LQ) optimal multiple-model adaptive control (MMAC) scheme is developed for linear MIMO systems with polytopic uncertainties. The proposed MMAC scheme is proven to be asymptotically stable for LTI MIMO systems and applied to tracking control of uncertain lateral vehicle dynamics. A set of simulation test results are presented to verify the stability, effectiveness, and comparison of the proposed MMAC scheme.

Nonlinear Multiple Models Adaptive Secondary Voltage Control of Microgrids

This article proposes a model-free secondary voltage control (SVC) for microgrids (MG) using nonlinear multiple models adaptive control. Firstly, a linear robust adaptive controller is designed to guarantee the voltage stability in the bounded-input-bounded-output (BIBO) manner so as to meet the operation requirements of MGs. Secondly, a nonlinear adaptive controller is developed to improve the voltage tracking performance with the help of artificial neural networks (ANNs). A switching mechanism for coordinating such two controllers is designed to guarantee the closed-loop stability while achieving accurate voltage tracking. By an online identification based on the input and output data of MGs, the proposed method does not resort to any apriori information of system model and primary control, thus exhibiting good robustness, ease of deployment and disturbance rejection.

Multiple model adaptive control based on switching/weighting intelligent fusion algorithm

Multiple model adaptive control based on switching/weighting intelligent fusion algorithm

Deep Reinforcement Learning and Randomized Blending for Control under Novel Disturbances

Abstract Enabling autonomous vehicles to maneuver in novel scenarios is a key unsolved problem. A well-known approach, Weighted Multiple Model Adaptive Control (WMMAC), uses a set of pre-tuned controllers and combines their control actions using a weight vector. Although WMMAC offers an improvement to traditional switched control in terms of smooth control oscillations, it depends on accurate fault isolation and cannot deal with unknown disturbances. A recent approach avoids state estimation by randomly assigning the controller weighting vector; however, this approach uses a uniform distribution for control-weight sampling, which is sub-optimal compared to state-estimation methods. In this article, we propose a framework that uses deep reinforcement learning (DRL) to learn weighted control distributions that optimize the performance of the randomized approach for both known and unknown disturbances. We show that RL-based randomized blending dominates pure randomized blending, a switched FDI-based architecture and pre-tuned controllers on a quadcopter trajectory optimisation task in which we penalise deviations in both position and attitude.

Weighted multiple model adaptive boundary control for a flexible manipulator.

In this article, a weighted multiple model adaptive boundary control scheme is proposed for a flexible manipulator with unknown large parameter uncertainties. First, the uncertainties are approximatively covered by a finite number of constant models. Second, based on Euler-Bernoulli beam theory and Hamilton principle, the distributed parameter model of the flexible manipulator is constructed in terms of partial differential equation for each local constant model. Correspondingly, local boundary controllers are designed to control the manipulator movement and suppress its vibration for each partial differential equation model, which are based on Lyapunov stability theory. Then, a novel weighted multiple model adaptive control strategy is developed based on an improved weighting algorithm. The stability of the overall closed-loop system is ensured by virtual equivalent system theory. Finally, numerical simulations are provided to illustrate the feasibility and effectiveness of the proposed control strategy.

Mitigation of Subsynchronous Resonance Induced by a Type III Wind System

This paper proposes an adaptive supplementary controller based on the multiple-model adaptive control (MMAC) approach to mitigate subsynchronous resonance (SSR) induced by a Type III wind system connected to a series-compensated transmission line. The proposed supplementary controller has two levels; the first level is a set of controllers designed using standard linear control methods, and the second level is a supervisory controller that selects the level-one controller based on the system conditions. A systematic approach is introduced for simultaneous coordination of parameters of the proposed supplementary controllers in a nonaggregated wind system. The aim of this coordination approach is to stabilize the system by moving the unstable modes to the left-hand plane while minimizing the controller gain values to reduce the sensitivity of the wind generator to the small disturbances. To evaluate the performance of the proposed controller, small-signal stability of the test system is studied using its comprehensive dynamic model. Time-domain simulation results show the effectiveness of the proposed supplementary controller to mitigate subsynchronous oscillations in a wide range of operating conditions.

Welding Process Tracking Control Based on Multiple Model Iterative Learning Control

The welding of the same parts has same welding trajectory, so welding process has strong repeatability. In this paper, aiming at the repeatability of welding process, an iterative learning controller is designed to achieve the control of weld quality. Due to the extremely variable welding environment and the presence of noise interferences and load disturbances, it is easy to cause the jumping change in parameters and even the structure of the welding system. Therefore, the idea of multiple model adaptive control (MMAC) is introduced into iterative learning control (ILC), and a multiple model iterative learning control (MMILC) algorithm is designed according to model of weld pool dynamic process in gas tungsten arc welding (GTAW). Besides, the convergence of the algorithm is analyzed for two cases: fixed parameters and jumping parameters. It turns out that the MMILC can not only utilize the repetitive information effectively in the welding process to achieve high precision tracking control of weld seam in limited time interval, but also realize the multiple model switching according to different working conditions to improve the welding quality.

Multiple-Model Adaptive Control of a Hybrid Solid Oxide Fuel Cell Gas Turbine Power Plant Simulator

A multiple model adaptive control (MMAC) methodology is used to control the critical parameters of a solid oxide fuel cell gas turbine (SOFC-GT) cyberphysical simulator, capable of characterizing 300 kW hybrid plants. The SOFC system is composed of a hardware balance of plant (BoP) component, and a high fidelity FC model implemented in software. This study utilizes empirically derived transfer functions (TFs) of the BoP facility to derive the MMAC gains for the BoP system, based on an estimation algorithm which identifies current operating points. The MMAC technique is useful for systems having a wide operating envelope with nonlinear dynamics. The practical implementation of the adaptive methodology is presented through simulation in the matlab/simulink environment.

Model reference adaptive control for switched linear systems using switched multiple models control strategy

In this paper, the multiple model strategy is applied to the adaptive control of switched linear systems to improve the transient performance. The solvability of the adaptive stabilization problem of each subsystem is not required. Firstly, the two-layer switching mechanism is designed. The state-dependent switching law with dwell time constraint is designed in the outer-layer switching to guarantee the stability of the switched systems. During the interval of dwell time constraint, the parameter resetting adaptive laws are designed in the inner-layer switching to improve the transient performance. Secondly, the minimum dwell time constraint providing enough time for multiple model adaptive control strategy to work fully and maintaining the stability of the switched systems is found. Finally, the proposed switched multiple model adaptive control strategy guarantees that all the closed-loop system signals remain bounded and the state tracking error converges to zero.

Multiple Model Adaptive Control of Flexible Arm

Multiple Model Adaptive Control of Flexible Arm

Survey and tutorial on multiple model methodologies in modelling, identification and control

Multiple model methodology is an important approach in modelling, identification and control of complicated systems with large uncertainties (parameter uncertainty or even model structure uncertainties). It agrees with the idea of 'divide and conquer' in solving engineering problems. There are two representative strategies with scheduling the multiple models, i.e., switching strategy and weighting strategy. Theses two strategies can be alternatively viewed as 'identification' of correct model/controller. Consequently, any reasonable control system design methods can be incorporated with switching or weighting methodology to formulate robust and adaptive control schemes. This survey paper gives a brief historical review of the development of multiple model adaptive estimation (MMAE) and multiple model adaptive control (MMAC), then moves focus onto the new progress of weighted MMAC (WMMAC) that has emerged in recent years including the weighting algorithm and stability analysis of WMMAC systems, based on virtual equivalent system (VES) theory.

Stable Weighted Multiple Model Adaptive Control of Continuous-Time Plant With Large Parameter Uncertainties

This paper presents a weighted multiple model adaptive control (WMMAC) scheme to deal with large parametric uncertainty of continuous-time plant. In this proposed scheme, each `local' controller is designed according to the mixed-μ-synthesis method to consider small uncertainty of plant parameters and disturbance; the weighting algorithm is directly based on model output errors rather than the residuals generated by multiple Kalman filters as in classical multiple model adaptive control (CMMAC). The closed-loop stability (signal boundedness) and tracking performance of the proposed WMMAC system are proved with the help of virtual equivalent system (VES) concept and methodology.