Design of UAV flexible landing control system based on model reference adaptive control

In this paper, because the induction machines are described as the plants of highly nonlinear and parameters time-varying, in order to obtain a very well control performances that a conventional model reference adaptive inverse control (MRAIC) can not be gotten, a fuzzy neural network-based model reference adaptive inverse control strategy for induction motors is presented based on the rotor field oriented motion model of induction machines. The fuzzy neural network control (FNNC) is incorporated into the model reference adaptive control (MRAC), a fuzzy basis function network controller (FBNC) and a fuzzy neural network identifier (FNNI) for asynchronous motors adjustable speed system are designed. The proposed controller for asynchronous machines resolves the shortage of MRAC, and employs the advantages of FNNC and MRAC. Simulation results show that the proposed control strategy is of the feasibility, correctness and effectiveness.

In this paper, we propose a model reference adaptive control (MRAC) strategy for continuous‐time single‐input single‐output (SISO) linear time‐invariant (LTI) systems with unknown parameters, performing repetitive tasks. This is achieved through the introduction of a discrete‐type parametric adaptation law in the ‘iteration domain’, which is directly obtained from the continuous‐time parametric adaptation law used in standard MRAC schemes. In fact, at the first iteration, we apply a standard MRAC to the system under consideration, while for the subsequent iterations, the parameters are appropriately updated along the iteration‐axis, in order to enhance the tracking performance from iteration to iteration. This approach is referred to as the model reference adaptive iterative learning control (MRAILC). In the case of systems with relative degree one, we obtain a pointwise convergence of the tracking error to zero, over the whole finite time interval, when the number of iterations tends to infinity. In the general case, i.e. systems with arbitrary relative degree, we show that the tracking error converges to a prescribed small domain around zero, over the whole finite time interval, when the number of iterations tends to infinity. It is worth noting that this approach allows: (1) to extend existing MRAC schemes, in a straightforward manner, to repetitive systems; (2) to avoid the use of the output time derivatives, which are generally required in traditional iterative learning control (ILC) strategies dealing with systems with high relative degree; (3) to handle systems with multiple tracking objectives (i.e. the desired trajectory can be iteration‐varying). Finally, simulation results are carried out to support the theoretical development. Copyright © 2006 John Wiley & Sons, Ltd.

The design and implementation of a model reference adaptive velocity control system for a small programmable four-wheel robotic vehicle are presented. Modeling of the plant, hardware and software design approaches, and experimental results of the study are discussed. It has been demonstrated that a MRAC (model reference adaptive control) system can be easily implemented by programming the control law in a microcontroller. The designed PWM (pulse-width modulation) motor controller hardware performed as expected, providing a significant increase in the efficiency and controllability of the motor. The appropriate selection of the adaptive gains and other factors is crucial for the practical implementation of the MRAC control law. Computer simulation provides the necessary means for the adjustment of these parameters and the verification of the design. >

This paper analyzes and compares the model reference adaptive control (MRAC) and modified MRAC for turntables. The conventional MRAC is aimed at the first-order system for two-variable parameter adjustment. Most of plants are second-order systems, including turntables. The tracking performance of conventional MRAC applied to the second-order system is not ideal. Here, the MRAC is extended from the first order to the second order, and the control law of the MRAC for second-order system is given. The Modified MRAC is designed by a combination of MRAC and PID controller. It improves dynamic performance of the system. Simulation of the MRAC and modified MRAC is done in MATLAB Simulink environment. Different adaptive gains were selected for the MRAC for analysis and comparison. Analysis and comparison of the interference of the MRAC and the modified MRAC. It is found that the MRAC for second-order system can make the second-order system track the reference model and the performance of the modified MRAC is better. This paper has carried out a detailed analysis and comparison of the MRAC and modified MRAC for second-order system based on MIT rule.

This paper proposes a model reference adaptive speed controller based on artificial neural network for induction motor drives. The performance of traditional feedback controllers has been insufficient in speed control of induction motors due to nonlinear structure of the system, changing environmental conditions, and disturbance input effects. A successful speed control of induction motor requires a nonlinear control system. On the other hand, in recent years, it has been demonstrated that artificial intelligence based control methods were much more successful in the nonlinear system control applications. In this work, it has been developed an intelligent controller for induction motor speed control with combination of radial basis function type neural network (RBF) and model reference adaptive control (MRAC) strategy. RBF is utilized to adaptively compensate the unknown nonlinearity in the control system. The indirect field-oriented control (IFOC) technique and space vector pulse width modulation (SVPWM) methods which are widespread used in high performance induction motor drives has been preferred for drive method. In order to demonstrate the reliability of the control technique, the proposed adaptive controller has been tested under different operating conditions and compared performance of conventional PI controller. The results show that the proposed controller has got a clear superiority to the conventional linear controllers.

This paper concerned with different control strategies used to balance (control) inverted pendulum system. The often used control strategy is PID tuning, but in PID the main issue is parameter tuning according to the process parameter variation. So proposed strategy is MRAC, combination of model reference adaptive Control (MRAC) with fuzzy. The main aim is to find further improvement of traditional MRAC method and to provide more accurate control to the inverted pendulum and to minimize drawbacks of the traditional MRAC method. This is examined when combining the MRAC method with Fuzzy control. The performance of the application system is examined from the simulation results in MATLAB/SIMULINK. For showing its effectiveness, simulated results are compared with the traditional control strategies like fuzzy and MRAC. Incorporating application for inverted pendulum system, subjected to it finds the analogy with various control system applications like satellite launching system, robotic arm etc.

The paper explains a control method for turntable by feedback a PID controller with the traditional model reference adaptive controller (MRAC) based on the MIT rule. The traditional MRAC is designed for a first-order system with the adjustment of a two-variable parameter. However, the majority of plants, including turntable, are second-order systems. Traditional MRAC tracking performance for second-order systems is unsatisfactory. The control law for the second order system along with extension from the first to the second order of MRAC is derived. The modified MRAC i.e., MRAC-PID controller is designed for the application of turntable. It enhanced the system’s dynamic performance. To assess the performance of the proposed controller, MATLAB/Simulink software was used. The article incorporates a detailed analysis and comparison of PID, MRAC as well as MRAC-PID controllers based on the MIT rule for the turntable system. The robustness of the proposed controller is validated by introducing uncertainties in two aspects i.e., mistuning of the controller gains and turntable system dynamics change. The probabilistic design assessment for the mistuning is carried out through levels of uncertainty in controller gain. It is observed that PID and MRAC would track the reference model but modified MRAC has better performance in terms of tracking accuracy, adaptability, and rapidity. Several performance indexes such as integral absolute error (IAE), integral time absolute error (ITAE), and integral square error (ISE) were employed to justify the proposed controller superiority.

This paper presents a vehicle stability control method based on a multi-input multi-output (MIMO) model reference adaptive control (MRAC) strategy as an advanced driver assistance system (ADAS) to enhance the handling and yaw stability of the vehicle lateral dynamics. The corrective yaw moment and additive steering angle are generated using direct yaw moment control (DYC) and active front steering (AFS) at the upper control level in the hierarchical control algorithm. A nonlinear term is added to the conventional adaptive control laws to handle parametric uncertainties and disturbances. The desired yaw moment generated by the upper-level controller is converted to the brake forces and is distributed to the rear wheels by an optimal procedure at the lower-level. The major contribution of this study is the introduction of a nonlinear integrated adaptive control method based on a constraint optimization algorithm. To verify the effectiveness of the proposed control strategy, the nonlinear integrated adaptive controller, and linear time-varying MRAC are designed and used for comparison. Simulation results are performed for the J-turn and double lane change (DLC) manoeuvres at high speeds and low tyre-road friction coefficients. The desired performance of the proposed controller exhibited significant improvement compared to the conventional MRAC in terms of yaw rate tracking and handling of sideslip limitation.

The purpose of this chapter is to redesign the standard adaptive control schemes by using hybrid structure composed by Model Reference Adaptive Control (MRAC) or Adaptive Pole Placement Control (APPC) strategies, associated to Variable Structure (VS) schemes for achieving non-standard robust adaptive control strategies. The both control strategies is now on named VS-MRAC and VS-APPC. We start with the theoretical base of standard control strategies APPC and MRAC, discussing their structures, as how their parameters are identified by adaptive observers and their robustness properties for guaranteeing their stability. After that, we introduce the sliding mode control (variable structure) in each control scheme for simplifying their design procedure. These design procedure are based on stability analysis of each hybrid robust control scheme. With the definition of both hybrid control strategies, it is analyzed their behavior when controlling system plants with unmodeled disturbances and parameter variation. It is established how the adaptive laws compensates these unmodeled dynamics. Furthermore, by using simple systems examples it is realized a comparison study between the hybrid structures VS-APPC and VSMRAC and the standard schemes APPC and MRAC. As the hybrid structures use switching laws due to the sliding mode scheme, the effect of chattering is analyzed on the implementation and consequently effects on the digital control hardware where sampling times are limiting factor. For reducing these drawbacks it is also discussed possibilities which kind of modifications can employ. Finally, some practical considerations are discussed on an implementation on motor drive systems.

Two typical methods for model reference adaptive control are introduced. By integrating the Narendra adaptive control method and the variable structure model reference adaptive control method, a new variable model reference adaptive recursive control method is presented. The results of simulation computations show that the new method has the merits of the above two methods and is efficient and effective.

This chapter discusses limitations and weaknesses of model-reference adaptive control. Parameter drift is the result of the lack of a mathematical guarantee of boundedness of adaptive parameters. Systems with bounded external disturbances under feedback control actions using model-reference adaptive control can experience a signal growth of a control gain or an adaptive parameter even though both the state and control signals remain bounded. This signal growth associated with the parameter drift can cause instability of adaptive systems. Model-reference adaptive control for non-minimum phase systems presents a major challenge. Non-minimum phase systems have unstable zeros in the right half plane. Such systems cannot tolerate large control gain signals. Model-reference adaptive control attempts to seek the ideal property of asymptotic tracking. In so doing, an unstable pole-zero cancelation occurs that leads to instability. For non-minimum phase systems, adaptive control designers generally have to be aware of the limiting values of adaptive parameters in order to prevent instability. Time-delay systems are another source of challenge for model-reference adaptive control. Many real systems have latency which results in a time delay at the control input. Time delay is caused by a variety of sources such as communication bus latency, computational latency, transport delay, etc. Time-delay systems are a special class of non-minimum phase systems. Model-reference adaptive control of time-delay systems is sensitive to the amplitude of the time delay. As the time delay increases, robustness of model-reference adaptive control decreases. As a consequence, instability can occur. Model-reference adaptive control is generally sensitive to unmodeled dynamics. In a control system design, high-order dynamics of internal states of the system sometimes are neglected in the control design. The neglected internal dynamics, or unmodeled dynamics, can result in loss of robustness of adaptive control systems. The mechanism of instability for a first-order SISO system with a second-order unmodeled actuator dynamics is presented. The instability mechanism can be due to the frequency of a reference command signal or an initial condition of an adaptive parameter that coincides with the zero phase margin condition. Fast adaptation is referred to the use of a large adaptation rate to achieve the improved tracking performance. An analogy of an integral control action of a linear time-invariant system is presented. As the integral control gain increases, the cross-over frequency of the closed-loop system increases. As a consequence, the phase margin or time-delay margin of the system decreases. Fast adaptation of model-reference adaptive control is analogous to the integral control of a linear control system whereby the adaptation rate plays the equivalent role as the integral control gain. As the adaptation rate increases, the time-delay margin of an adaptive control system decreases. In the limit, the time-delay margin tends to zero as the adaptation rate tends to infinity. Thus, the adaptation rate has a strong influence on the closed-loop stability of an adaptive control system.

From adaptive control to variable structure systems – seeking harmony

This paper demonstrates the performance of Model Reference Adaptive Controller (MRAC) using Lyapunov and MIT schemes applied in steam distillation process in real-time and simulated conditions. The integral limits in MRAC were varied during the experiment. Simulated results shows that low SSE and RMSE were obtained when adaptation gain and integral limit were set to 0.003 and +0.01 respectively. However, in real time control, low SSE and RMSE were obtained when the integral limit was eliminated in MRAC Lyapunov and MRAC MIT control structure.

This paper is concerned with the combination of model reference adaptive control (MRAC) and PID. Incorporating application for inverted pendulum system, subjected to it finds the analogy with various control system applications like robotic arm, satellite launching system etc. The main aim is to find further improvement of traditional MRAC method and to provide more accurate control to the inverted pendulum and to minimize drawbacks of the traditional MRAC method. This is examined when combining the MRAC method with the PID control. The performance of the application system is examined from the simulation results in MATLAB/SIMULINK. For showing its effectiveness its simulated results are compared with the traditional control strategies like PID and MRAC.

This paper presents the analysis of two control methods used to increase the kinematic performance of two-axle bogie when the rail vehicle travels on curved track with constant speed. A holistic model for the bogie and wheelsets is developed using the multibody system approach. The mathematical model is implemented in MATLAB environment using Simulink toolbox. Then the bogie model is included in a closed loop and PID controller tuning is performed using Control Systems toolbox from MATLAB environment. The simulated values for lateral and yaw angular displacement of wheelsets are compared with those obtained by applying the Model Reference Adaptive Control (MRAC) method. The results of the sensitivity analysis for rise time, peak overshoot and settling time with respect to travel speed for rail vehicle show that MRAC method improves the time domain closed-loop performance. The proposed MRAC method applied to the bogie system within the rail vehicle multibody model represents an important contribution to the development of rail transportation systems where rolling stock is equipped with motion control systems with superior efficiency and reduced energy consumption.