Multiple Model Adaptive Control Research Articles

Algorithms for Optimal Model Distributions in Adaptive Switching Control Schemes

Several multiple model adaptive control architectures have been proposed in the literature. Despite many advances in theory, the crucial question of how to synthesize the pairs model/controller in a structurally optimal way is to a large extent not addressed. In particular, it is not clear how to place the pairs model/controller is such a way that the properties of the switching algorithm (e.g., number of switches, learning transient, final performance) are optimal with respect to some criteria. In this work, we focus on the so-called multi-model unfalsified adaptive supervisory switching control (MUASSC) scheme; we define a suitable structural optimality criterion and develop algorithms for synthesizing the pairs model/controller in such a way that they are optimal with respect to the structural optimality criterion we defined. The peculiarity of the proposed optimality criterion and algorithms is that the optimization is carried out so as to optimize the entire behavior of the adaptive algorithm, i.e., both the learning transient and the steady-state response. A comparison is made with respect to the model distribution of the robust multiple model adaptive control (RMMAC), where the optimization considers only the steady-state ideal response and neglects any learning transient.

Adaptive control of rapidly time-varying discrete-time system using initial-training-free online extreme learning machine

While multiple model adaptive control (MMAC) scheme provides a solution to systems with unknown and rapidly time-varying parameters, many offline samples must be obtained beforehand, and the number of models is difficult to be found if no prior knowledge is given. This paper proposes a new adaptive control strategy to handle such systems. The principle is to use a change detection mechanism to check if there is an abrupt change, and immediately train a new model if a change is detected. A novel online identification algorithm, namely initial-training-free online extreme learning machine (ITF-OELM), is also proposed to allow the model to be trained anytime without concerns on prior data. With this strategy, only one model is necessary as compared to MMAC, resulting in reduction on computational complexity and memory usage. Simulation results show that the proposed strategy is effective. Besides, although the use of forgetting factor in ITF-OELM can accelerate the convergence speed for system identification, sometimes it may lead to ill-conditioned covariance matrix in the recursively updating process. This paper shows that such issue can be solved by the change detection mechanism.

Multiple Model Adaptive Tracking Control Based on Adaptive Dynamic Programming

Adaptive dynamic programming (ADP) has been tested as an effective method for optimal control of nonlinear system. However, as the structure of ADP requires control input to satisfy the initial admissible control condition, the control performance may be deteriorated due to abrupt parameter change or system failure. In this paper, we introduce the multiple models idea into ADP, multiple subcontrollers run in parallel to supply multiple initial conditions for different environments, and a switching index is set up to decide the appropriate initial conditions for current system. By taking this strategy, the proposed multiple model ADP achieves optimal control for system with jumping parameters. The convergence of multiple model adaptive control based on ADP is proved and the simulation shows that the proposed method can improve the transient response of system effectively.

柔性传动系统的多模型自适应控制

The control of a flexible transmission system under various conditions with various loads is an important challenge. This paper proposes a multiple-model adaptive control algorithm as a solution for the flexible transmission benchmark problem. Switching strategies and controller design methods are described in detail by means of the results of a stability analysis of the controller. To verify the effectiveness of the proposed algorithm, a flexible transmission experimental platform was employed to carry out comparison experiments using a robust controller, an adaptive controller, and the proposed controller. The experimental results showed that the proposed algorithm performed much better than the other controllers because in actual experiments, the other controllers failed under many conditions. The experimental parameter analysis of the proposed algorithm shows that the influence of parameters in the case of the proposed system is not significant when there is a sufficient number of models and reasonable dwell time. This means that the proposed algorithm can always guarantee stability control for flexible industrial transmission systems, and can quickly and stably track and adjust the target.

Wind Turbine Active Power Control Based on Multi-Model Adaptive Control

This paper proposes a multiple model adaptive control method of wind turbine active power that considers the complexity of the control model, nonlinear and strong coupling. The model is designed to reduce the negative influences of wind turbines in the process of active power control caused by different uncertain factors. We first build the multiple model of turbine operating by using subtracting cluster algorithm， based on data of a 1.5MW doubly-fed inductor generators (DFIGs) in a wind farm in Gansu, China. We use recursive least squares (RLS) algorithm to identify local model parameters. In addition， the controller is designed by adopting online optimal control model which based on a weighted index of output matching switching strategy. The controller is to realize multi-model adaptive control (MMAC). Results show that the proposed method has good control performance. The method can effectively solve the problems of wind turbines nonlinear modeling and active power control in operation.

English

English

Multiple-Model Adaptive Control for Spacecraft Under Sign Errors in Actuator Response

This paper addresses the attitude tracking control problem for spacecraft with unknown inertia parameters and sign errors in actuator response that are uncertain in occurring time instants and patterns. Either a software or hardware error may cause actuation sign errors, which may lead spacecraft to fail. A new multiple-model adaptive control scheme is developed to compensate the uncertainties of actuation sign errors and inertia parameters, which ensures the desired system global stability and asymptotic attitude tracking properties. Such a control scheme employs a set of adaptive estimators to generate parameter estimates, estimator states, and estimation errors, in which the uncertainties of inertia parameters are parameterized. Then, a set of control signals are designed by using such estimates and estimation errors, and a switching mechanism is established based on calculating multiple cost functions from the estimation errors to select the most appropriate control signal with the minimum cost function as the applied control signal. Simulation results of an application to a spacecraft model with four reaction wheels and comparison of results with a Nussbaum gain-based control design verify the effectiveness of the proposed control scheme.

Trajectory tracking of nonlinear system using multiple series-parallel dynamic neural networks

This paper presents a novel approach of adaptive control for unknown nonlinear continuous-time dynamic system using series-parallel dynamic neural networks (SPDNN) and multiple models. Dynamic neural networks are introduced into the multiple models adaptive control (MMAC), which can improve the adaptation ability of controllers for the plant with wide-range uncertain parameters. The adaptive law of SPDNN weight with unmodeled dynamics is derived from Lyapunov stability theory. In order to assure the effectivity of the controller, the projection algorithm is used to avoid the weight through zero. Multiple combinations of identification models based on SPDNN are used to cover the uncertainty of the plant. Based on the identification error, an effective switching scheme is applied to choose the best model and controller at every instant. The simulation results demonstrate that the proposed adaptive control using multiple dynamic neural networks models can achieve remarkable control performance for nonlinear continuous-time system.

Further results on stable weighted multiple model adaptive control: Discrete‐time stochastic plant

SummaryA new weighting algorithm is proposed to relax the convergence conditions and to improve the convergence rate for weighted multiple model adaptive control systems. The stability and convergence of the corresponding weighted multiple model adaptive control systems of two types of stochastic plants, one is linear time‐invariant system (LTI) with unknown parameters, the other is linear time‐varying system with jumping parameters, are proved. Finally, some simulation results are presented to verify the effectiveness of the proposed weighting algorithm and the performance of the closed‐loop control system. Copyright © 2015 John Wiley & Sons, Ltd.

Multiple model adaptive control using second level adaptation for a delta-wing aircraft

Purpose – The purpose of this paper is to design a stable controller such that the control input is applied to the delta-wing aircraft in order to adjust the roll dynamics. The controller must provide a desired tracking performance with minimum tracking error. Design/methodology/approach – In this paper, the second level adaptation (SLA) strategy is applied to control a delta-wing aircraft using multiple models. The implemented control structure is compared with the first level adaptation (FLA) and model reference adaptive control (MRAC) techniques. Findings – SLA architecture not only copes with a wide uncertainty domain caused by aerodynamic effects, but also its rapid and accurate convergence is one of its most important features. Furthermore, this strategy makes a smoother control signal with respect to FLA and MRAC even at the same initial times. It should be also noted that SLA using three models, copes with uncertainty that may occur to the aircraft at high Angle Of Attacks (AOAs) at the entire flight envelope. Originality/value – In this paper for the first time the application of this strategy is used to identify and control a delta-wing aircraft. Furthermore a systematic block diagram approach is proposed for the design.

Multiple model adaptive backstepping control for antilock braking system based on LuGre dynamic tyre model

Vehicle stability control systems play an essential role in helping the driver maintain vehicle control. This paper addresses a control scheme for antilock braking system (ABS). In this control scheme, first, a quarter model of the vehicle's longitudinal dynamic system is developed, which includes the LuGre tyre/road dynamic friction model. Then, a non-linear controller is developed using the multiple models adaptive controls (MMAC) methodology. The control system models are divided into high adhesion, middle adhesion, low adhesion fixed models and an adaptive model. Backstepping and adaptive backstepping controller are designed for fixed and adaptive models, respectively. The index function is used to switch the control rule by searching the minimum index value. Our proposed ABS controller's stability is proved by the Lyapunov stability theorem. Finally, the results of the simulation in the different surface conditions show that MMAC is better than other controllers and achieves great and robust braking performance.

Multiple Model Adaptive Control for a Class of Linear-Bounded Nonlinear Systems

This study proposes a novel multiple model adaptive control (MMAC) algorithm for a class of nonlinear discrete time systems. The controller consists of a linear indirect adaptive controller, a nonlinear indirect adaptive controller based on neural networks, and a switching mechanism. The control input is generated by the switching mechanism, which selects the candidate controller from the two controllers. The assumption of the nonlinear term is relaxed to linear-bounded when a modified adaptive law is introduced. The restraint that the nonlinear term of the plant should be linear with respect to the control input is removed by resorting to the pole-placement control scheme. The proposed control method can address the properties of non-minimum phase and open-loop instability in the linear part of the plant. The proposed MMAC algorithm can guarantee the bounded-input-bounded-output stability of the proposed closed-loop switching system. A simulation example is presented to demonstrate the effectiveness of the proposed method.

Design of a multiple-model adaptive controller for a fexible transmission system

The control of a flexible transmission system under various conditions with various loads is an important challenge. This paper proposes a multiple-model adaptive control algorithm as a solution for the flexible transmission benchmark problem. Switching strategies and controller design methods are described in detail by means of the results of a stability analysis of the controller. To verify the effectiveness of the proposed algorithm, a flexible transmission experimental platform was employed to carry out comparison experiments using a robust controller, an adaptive controller, and the proposed controller. The experimental results showed that the proposed algorithm performed much better than the other controllers because in actual experiments, the other controllers failed under many conditions. The experimental parameter analysis of the proposed algorithm shows that the influence of parameters in the case of the proposed system is not significant when there is a sufficient number of models and reasonable dwell time. This means that the proposed algorithm can always guarantee stability control for flexible industrial transmission systems, and can quickly and stably track and adjust the target.

Weighted Multiple Model Adaptive Control of Time-varying Systems

This paper is concerned with the weighted multiple model adaptive control (WMMAC) of stochastic plant with time-varying parameters, such as slowly time-varying plant or parameter jump plant. An improved weighting algorithm is proposed for this kind of control systems. The effectiveness (sharp and correct 0-1 convergence) of the weighting algorithm and the robust stability of the resulting closed-loop WMMAC system has been verified through many simulation results.

Multiple characteristic model‐based golden‐section adaptive control: Stability and optimization

SummaryThe characteristic model‐based golden‐section adaptive control (CM‐GSAC) law has been developed for over 20 years in China with a broad range of applications in various fields. However, quite a few theoretical problems remain open despite its satisfying performance in practice. This paper revisits the stability of the CM‐GSAC from its very beginning and explores the underlying implications of the so‐called golden‐section parameter l2≈0.618. The closed‐loop system, which consists of the CM and the GSAC, is a discrete time‐varying system, and its stability is discussed from three perspectives. First, attentions have been paid to select the optimal controller coefficients such that the closed‐loop system exhibits the best transient performance in the worst case. Second, efforts are made to improve the robustness in the presence of parameter estimation errors, which provide another choice when designing the adaptive controller. Finally, by measuring the slowly time‐varying nature in an explicit inequality form, a bridge is built between the instantaneous stability and the time‐varying stability. In order to relax the constraints on the parameter bounds of the CM, the GSAC is further extended to multiple CMs, which shows more satisfying tracking performance than that of the traditional multiple model adaptive control method. Copyright © 2014 John Wiley & Sons, Ltd.

Delay-Dependent Stability of Cellular Neural Networks with Multiple Delays

Back propagation (BP) neural network is used to approximate the dynamic character of nonlinear discrete-time system. Considering the unmodeling dynamics of the system, the weights of neural network are updated by using a dead-zone algorithm and a robust adaptive controller based on the BP neural network is proposed. For the situation that jumping change parameters exist, multiple neural networks with multiple weights are built to cover the uncertainty of parameters, and multiple controllers based on these models are set up. At every sample time, a performance index function based on the identification error will be used to choose the optimal model and the corresponding controller. Different kinds of combinations of fixed model and adaptive model will be used for robust multiple models adaptive control (MMAC). The proof of stability and convergence of MMAC are given, and the significant efficacy of the proposed methods is tested by simulation. This paper deals with the problem of delay-dependent stability criterion of delay-difference system with multiple delays of cellular neural networks. Based on quadratic Lyapunov functional approach and free-weighting matrix approach, some linear matrix inequality criteria are found to guarantee delay-dependent asymptotical stability of these systems. And one example illustrates the exactness of the proposed criteria.

Study on Fuzzy Multiple Reference Model Adaptive Control Strategies

A new fuzzy multiple reference model adaptive control method combined with fuzzy select and conventional adaptive control is presented. To overcome the control difficulties which due to significant and unpredictable system parameter variations, fuzzy logic rules are designed to choose the suitable reference model. The new method is applied to control the speed servo system of dynamic model of BLDCM, and the simulation results show it works well with high dynamic performance and control precision under the condition of great change in reference speed and load torque.

Multiple Models Adaptive Control Based on Time Series for a Class of Nonlinear Systems

For a class of nonlinear discrete time systems,a multiple models adaptive controller(MMAC) based on time series is proposed.It uses clustering method to establish some linear local models,utilizes time series and directional derivative to establish a weighted model to approximate the real system when its working point jumps abruptly,and adds one global adaptive model with a re-initialized adaptive model to get the multiple models.Then a switching mechanism is designed to select the optimal controller to realize the control.Finally,in the simulation result,it can be seen that the proposed controller not only improves the transient response and speed up the control effect,but also reduces the number of the multiple model sets greatly,especially for a similar control response.

High-speed train control based on multiple-model adaptive control with second-level adaptation

Speed uplift has become the leading trend for the development of current railway traffic. Ideally, under the high-speed transportation infrastructure, trains run at specified positions with designated speeds at appointed times. In view of the faster adaptation ability of multiple-model adaptive control with second-level adaptation (MMAC-SLA), we propose one type of MMAC-SLA for a class of nonlinear systems such as cascaded vehicles. By using an input decomposition technique, the corresponding stability proof is solved for the proposed MMAC-SLA, which synthesises the control signals from the weighted multiple models. The control strategy is utilised to challenge the position and speed tracking of high-speed trains with uncertain parameters. The simulation results demonstrate that the proposed MMAC-SLA can achieve small tracking errors with moderate in-train forces incurred under the control of flattening input signals with practical enforceability. This study also provides a new idea for the control of in-train forces by tracking the positions and speeds of cars while considering power constraints.

On Multiple Model Adaptive Control Strategy Research of Ball and Plate System

The ball and plate system is a multi-variable and strongly nonlinear-controlled object of which is a system of two-dimensional extension with the variables amid having strong coupling function, and simultaneously it demands the closed loop control system owning good transient process and transient response. From perspective of international and domestic scale, ball and plate system has not figured out effective-control scheme. Take into account the ball and plate system possesses trait of nonlinearity and high requirements for transient response, etc. This paper proposes multiple model adaptive control strategy which is very suitable for such system, as well as the direction and guidance of such research.