Output feedback MIMO MRAC schemes with sensor uncertainty compensation

Partial-state feedback multivariable MRAC and reduced-order designs

The precise position control for electro-hydraulic servo system (EHSS) is a key technical problem for modern industry control. This paper presents a model reference adaptive control (MRAC) strategy combining fractional order calculus theory and neural network to solve the high nonlinearity and parameter uncertainties in the EHSS. The reference model in the MRAC strategy is a fractional second-order model and the controller with adaptive law is designed by a Wiener recurrent neural network (WRNN). Simulations indicate the validity and superiority of the proposed fractional MRAC strategy.

It is shown how to change the primary control structure of a model reference adaptive control (MRAC) scheme from the usual minimum settling time-type algorithm to a ripple-free response control (RFRC) scheme. RFRC does not try to compensate process zeros, avoiding ringing effects (like intersampling ripple). The main issue in the derivation of the RFRC-MRAC scheme is the establishment of a stable adaptive law which guarantees a ripple-free behavior if the error signal tends to zero. In the original MRAC scheme, the error signal consisted of the desired error plus a transient term which tends to zero when adaptation proceeds. In this MRAC scheme, the error consists of a filtered form of the desired RFRC error. Hence, if the error used in the adaption tends to zero, the RFRC behavior is achieved. The scheme avoids ringing effects and can control nonminimum-phase processes. The cost of these advantages is a slowing down of the process response by a number of samples equal to the order of the numerator polynomial. Simulations confirm the RFRC-like characteristics, and practical results on a water level system show the usability of the method.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Distributed model reference adaptive containment control of heterogeneous multi-agent systems with unknown uncertainties and directed topologies

On the hybrid MRAC-PID control: A comparison study

Recently, a class of adaptive control schemes called L1 Adaptive Control (L1-AC) has been proposed and widely advertised in aerospace control for achieving fast and robust adaptation and better performance than the existing Model Reference Adaptive Control (MRAC) Schemes. The L1-AC scheme is designed mainly for plants with full state measurement even though the name L1-AC has been used as an umbrella name for more general classes of plants. In this paper, we show that the L1-AC for plants with measured states is simply a standard MRAC with a low pass filter inserted in front of the control input. The analysis of the scheme is almost identical to that of MRAC as the same Lyapunov function is used to establish stability. The motivation for using the filter is the fact that for this class of adaptive schemes i.e. MRAC for plants with full state measurement the tracking error can be made arbitrarily small during transient by increasing the adaptive gain. A high adaptive gain however makes the differential equation of the adaptive law or estimator very stiff and leads to numerical problems that cause high oscillations in the estimated parameters leading to loss of adaptivity and deviations from what the theoretical properties dictate. The L1-AC approach mistook these numerical oscillations as properties of the adaptive scheme and inserted an input low pass filter in order to filter them out. While the filter helps reduce the frequency of these oscillations in the control law the price paid is high. First the numerical instability does not go away and the estimated parameters continue to oscillate without converging to the true parameters even in the presence of sufficiently rich signals. Second, due to the filter the tracking error is no longer guaranteed to converge to zero and the transient bounds for the tracking error also depend on the filter. As a result, the tracking properties of the L1-AC scheme are worse than what a simple MRAC scheme can generate with adaptive gains that could be high but away from the region of numerical instability. In addition, the presence of the filter reduces the robust stability margins in the presence of unmodeled dynamics and provides literally no advantage as simple robust MRAC techniques can solve the same problem achieving much better properties. The authors of L1-AC often compare the properties of a numerically unstable MRAC due to extremely high adaptive gains something that is prohibited by robust adaptive control with those of the filtered MRAC aka L1-AC to show that L1-AC performs better. Such comparisons are not only misleading but do not reveal what causes what giving the reader a false impression of a new theory that results to new performance and robustness improvements. The use of filters in adaptive control is not new and they are used to improve the performance and robustness of certain adaptive control schemes without destroying their ideal tracking properties. In this paper we present such a scheme that guarantees good transient performance and robustness and reveals the trade off between transient performance and robust stability.

This paper proposed three control schemes using model reference adaptive control (MRAC) and RBF neural network(RBFNN). Through comparision, it proposed a MRAC scheme with RBFNN compensator for speed control of high precision servo systems. The MRAC scheme is used to give better solutions with online adaptation and guarantee the stablity of the system. In the feedback channel, the parameter <i>K_p</i> makes it easier to design the system poles. By using a PI controller before <i>K_p</i>, the dynamic performance of the system is improved. As a speed compensate controller, the RBFNN is designed parallel with the model reference control. The RBFNN controller is able to online learn the unknown model dynamics, parameter variation and disturbance of the system. Thus, it is feasible to preserve favorable model-following characteristics under various conditions. The effectiveness of the proposed control scheme is demonstrated by simulation. It is found that the proposed scheme can reduce the plant's sensitivity to parameter variation and disturbance. High precision performance is obtained when given constant and sine wave disturbance at the same time.

Model reference adaptive fuzzy control (MRAFC) is proposed to control the active power filter (APF) and model the APF system caused by nonlinear characteristics, variable parameters and external interferences. T-S fuzzy theory is utilized on the approximation of nonlinear model and the current tracking control. APF system based on MRAFC can be guaranteed to be asymptotical stable. Simulation results are included to confirm the effectiveness of the proposed controller and comparisons with APF system based on model reference adaptive control (MRAC) demonstrate that MRAFC method is capable of reducing the modeling error and tracking reference current signal accurately. Moreover, APF based on the proposed method shows the dynamic and static control performance of stronger robustness, better suitability and higher accuracy than the one based on MRAC modus.

In this paper we propose a model reference adaptive control strategy using output feedback. When the control system is constructed using the proposed controller, the maximum value of the output error decreases as the value of the feedback gain of the output error increases. Moreover, the proposed control system has good performance that the maximum value of the control input does not increase even if the value of feedback gain increases and that high-freqency vibrations have difficulty appearing in the control input. A control system having such properties would be very useful for controlling actual plants.

It is required to adapt to the change of the dynamic characteristics in real-time so that robotics systems can achieve high-speed and high-accuracy motion. In the model reference adaptive control schemes (MRAC), a model with short time-constant must be chosen to realize fast response. However, it takes a long time to adapt to the model. Thus, a new approach for MRAC was proposed. In order to improve the transient behavior, the main idea of the new algorithm is the introduction of the state feedback to the model, and the availability was verified on the first-order systems. In this paper, the proposed methodology is extended to the second-order systems. Simulations show that the efficiency of this proposed method was verified.

Design of a digital adaptive algorithm for torque-controlled robotic joints

In an effort to increase the efficiency and power density of electric vehicles and aircraft, the automobile and aerospace industries are adopting variable-speed generators as part of the powertrain. Herein, the engine directly drives a permanent magnet synchronous machine, which generates a variable-frequency/amplitude output ac-voltage. More specifically, this unique ac-voltage profile serves as the input for an ac-dc power converter that energizes the main dc-bus of the powertrain. In this paper, a smart active rectifier for hybrid/all-electric powertrains is presented. A direct model reference adaptive control (MRAC) scheme is utilized to address the rapid changes in amplitude and frequency while regulating the dc-bus voltage at unity power factor. Moreover, the MRAC requires minimal tuning due to its capability of adjusting its gains adaptively with the input voltage variations. As a result, the MRAC exhibits seamless dynamics. The performance of the MRAC is verified experimentally under accelerating and decelerating variable-frequency/amplitude input voltage ramps using a laboratory-scale 2-kW 270-V SiC-MOSFET-based power converter supplied by a programmable grid emulator. Lastly, a classic proportional-integral (PI) control scheme is implemented experimentally with the same converter as a benchmark to highlight the merits of the proposed converter.

This paper presents development, implementation, experimental testing and comparison of two useful control approaches to a quadrotor Unmanned Aerial Vehicle (UAV) test-bed available at Concordia University for the purpose of enhancing reliability, safety and Fault-Tolerant Control (FTC) of the UAV. A Gain-Scheduled Proportional-Integral-Derivative (GS-PID) controller and a Model Reference Adaptive Control (MRAC) scheme are the main control techniques investigated in this paper based on their wide and popular applications in many engineering systems. Controllers are designed and implemented on the on-board single-chip micro-computer in order to keep the desired height of the helicopter in both normal (faultfree) and faulty flight conditions. In the MRAC case, in addition to height control, some other faulty cases are also considered for the trajectory tracking control with typical trajectories such as square shape trajectory. Finally, comparison results based on experimental testing of the two types of controllers on the UAV test-bed are presented. From the operational point of view, MRAC showed a promising performance for handling the fault imposed to all actuators as well as good fault-free control performance. On the other hand, the GS-PID controller with a linear transition between modes was able to react in a faster way than MRAC to maintain good control of the quadrotors height if the controller reaction/switching time is kept as short as possible. In other words, the GS-PID showed stronger fault-tolerant control capability than MRAC to keep the height of the helicopter if the switching time of the GS-PID controller gains after fault occurrence is kept close to zero.

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.

Robust Model Reference Adaptive Control in the Presence of Parasitics