Robust observer design for nonlinear uncertain switched systems under asynchronous switching

Switching between the system and the associated observer or controller is in fact asynchronous in switched control systems. However, many times we assume it synchronous, for simplicity. In this paper, the robust observer design problems for a class of nonlinear uncertain switched systems for synchronous and asynchronous switching are addressed. At first, a robust observer under synchronous switching is proposed based on average dwell time approach. After that, the results are extended to robust observer design in the asynchronous case. In this case, two working modes are adopted to facilitate the studies on the issue. Finally, an extension case covering more practical applications is investigated under asynchronous switching. The designed observer cannot maintain the asymptotical stability of error state, but the eventual boundness is guaranteed. At the end, a numerical design example is given to illustrate our results.     

Iterative learning control for MIMO nonlinear systems with arbitrary relative degree and no states measurement

This article presents a state observer based iterative learning control to solve the trajectory tracking problem of a class of time‐varying Multi‐Input‐Multi‐Output nonlinear systems with arbitrary relative degree. For this purpose, an asymptotically stable observer is derived for the system under consideration. There after, this observer is integrated with the iterative learning controller by replacing the state in the control law with its estimation yielded by the state observer. Hence, the stability of the whole control (nonlinear system plus controller plus observer) is guaranteed. Simulation result on nonlinear system shows that the trajectory tracking error decreases through the iterations. © 2013 Wiley Periodicals, Inc. Complexity 19: 37–45, 2013     

Adaptive Constrained Control of Unknown Strict Feedback Nonlinear Systems With Dead Zone Input北大核心CSCD

研究了具有死区输入的预设约束未知高阶严格反馈非线性系统的控制问题,提出了一种基于免疫函数的自抗扰预设漏斗约束自适应控制策略。首先,针对系统内部的未知问题,采用免疫函数与扩张状态观测器结合对系统内部未知项进行观测;其次,通过Lyapunov方法与漏斗控制相结合设计控制器,使得跟踪误差能够维持在预先设定的漏斗约束范围内;同时,利用双曲正切函数速率变化快这一特性设计自适应控制律,引入指令滤波器避免反步法中重复求导问题,分析证明了闭环系统所有信号的有界性。仿真实例表明了控制方法的有效性。     

Adaptive output feedback asymptotic stabilization of nonholonomic systems with uncertainties

A global adaptive output feedback control strategy is presented for a class of nonholonomic systems in generalized chained form with drift nonlinearity and unknown virtual control parameters. The purpose is to design a nonlinear output feedback switching controller such that the closed-loop system is globally asymptotically stable. By using the input-state scaling technique and an integrator back-stepping approach, an output feedback controller is given. A filter of observer gain is introduced for state and parameter estimates. Meanwhile, in order to avoid the over-parameters, a tuning function technique is utilized. A novel switching control strategy based on the output measurement of the first subsystem rather than time is used to overcome the uncontrollability of the x₀-subsystem in the origin. The proposed controller can guarantee that all the system states globally converge to the origin, while other signals maintain bounded. The numerical simulation testifies the effectiveness.     

The Optimal Control of a Train

We consider the problem of determining an optimal driving strategy in a train control problem with a generalised equation of motion. We assume that the journey must be completed within a given time and seek a strategy that minimises fuel consumption. On the one hand we consider the case where continuous control can be used and on the other hand we consider the case where only discrete control is available. We pay particular attention to a unified development of the two cases. For the continuous control problem we use the Pontryagin principle to find necessary conditions on an optimal strategy and show that these conditions yield key equations that determine the optimal switching points. In the discrete control problem, which is the typical situation with diesel-electric locomotives, we show that for each fixed control sequence the cost of fuel can be minimised by finding the optimal switching times. The corresponding strategies are called strategies of optimal type and in this case we use the Kuhn–Tucker equations to find key equations that determine the optimal switching times. We note that the strategies of optimal type can be used to approximate as closely as we please the optimal strategy obtained using continuous control and we present two new derivations of the key equations. We illustrate our general remarks by reference to a typical train control problem.     

Active Disturbance Rejection Control of Quadrotor UAVs Based on Joint Observation and Feedforward Compensation北大核心CSCD

为解决模型参数不确定与外界干扰影响下,四旋翼无人机飞控作业中姿态与轨迹跟踪精度下降,反应迟缓的问题,利用拓展Kalman滤波应对非线性系统问题出色的适应能力和噪声抑制能力,对四旋翼状态信息进行初步估算来抑制高频信号干扰,从而降低了扩张状态观测器的估计负担.同时,与扩张状态观测器联合估计由系统不确定性参数与外界扰动联合组成的“总扰动”,使系统对于精确模型的依赖性降低,并利用扰动估计的微分值进行前馈补偿,以提高对突变扰动的跟踪精度,克服了突变干扰下的相位滞后现象.综合联合观测器、带前馈补偿的LESO及带误差补偿的PD控制律,形成了一种利用拓展Kalman滤波与前馈补偿后的扩张状态观测器联合观测扰动,能较大程度抑制高频噪声和突变扰动的改进型自抗扰控制器.仿真与实验结果表明,联合观测器能有效地减小观测误差幅值且能超前校正观测相位滞后,从而更好地得到更精确的状态信息,改进型自抗扰控制器能更好地满足四旋翼飞行器快速反应、高效稳定的控制要求,精准高效地完成复杂轨迹跟踪.     

Autonomous change of behavior for environmental context: An intermittent search model with misunderstanding search pattern

Although foraging patterns have long been predicted to autonomously adapt to environmental conditions, empirical evidence has been found in recent years. This evidence suggests that the search strategy of animals is open to change so that animals can flexibly respond to their environment. In this study, we began with a simple computational model that possesses the principal features of an intermittent strategy, ie, careful local searches separated by longer steps, as a mechanism for relocation, where an agent in the model follows a rule to switch between two phases, but it could misunderstand this rule, ie, the agent follows an ambiguous switching rule. Thanks to this ambiguity, the agent's foraging strategy can continuously change. First, we demonstrate that our model can exhibit an autonomous change of strategy from Brownian‐type to Lévy type depending on the prey density, and we investigate the distribution of time intervals for switching between the phases. Moreover, we show that the model can display higher search efficiency than a correlated random walk.     

An examination of HMM‐based investment strategies for asset allocation

We develop and analyse investment strategies relying on hidden Markov model approaches. In particular, we use filtering techniques to aid an investor in his decision to allocate all of his investment fund to either growth or value stocks at a given time. As this allows the investor to switch between growth and value stocks, we call this first strategy a switching investment strategy. This switching strategy is compared with the strategies of purely investing in growth or value stocks by tracking the quarterly terminal wealth of a hypothetical portfolio for each strategy. Using the data sets on Russell 3000 growth index and Russell 3000 value index compiled by Russell Investment Services for the period 1995–2008, we find that the overall risk‐adjusted performance of the switching strategy is better than that of solely investing in either one of the indices. We also consider a second strategy referred to as a mixed investment strategy which enables the investor to allocate an optimal proportion of his investment between growth and value stocks given a level of risk aversion. Numerical demonstrations are provided using the same data sets on Russell 3000 growth and value indices. The switching investment strategy yields the best or second best Sharpe ratio as compared with those obtained from the pure index strategies and mixed strategy in 14 intervals. The performance of the mixed investment strategy under the HMM setting is also compared with that of the classical mean–variance approach. To make the comparison valid, we choose the same level of risk aversion for each set‐up. Our findings show that the mixed investment strategy within the HMM framework gives higher Sharpe ratios in 5 intervals of the time series than that given by the standard mean–variance approach. The calculated weights through time from the strategy incorporating the HMM set‐up are more stable. A simulation analysis further shows a higher performance stability of the HMM strategies compared with the pure strategies and the mean–variance strategy. Copyright © 2009 John Wiley & Sons, Ltd.     

Model-based control strategies for systems with constraints of the program type

The paper presents a model-based tracking control strategy for constrained mechanical systems. Constraints we consider can be material and non-material ones referred to as program constraints. The program constraint equations represent tasks put upon system motions and they can be differential equations of orders higher than one or two, and be non-integrable. The tracking control strategy relies upon two dynamic models: a reference model, which is a dynamic model of a system with arbitrary order differential constraints and a dynamic control model. The reference model serves as a motion planner, which generates inputs to the dynamic control model. It is based upon a generalized program motion equations (GPME) method. The method enables to combine material and program constraints and merge them both into the motion equations. Lagrange’s equations with multipliers are the peculiar case of the GPME, since they can be applied to systems with constraints of first orders. Our tracking strategy referred to as a model reference program motion tracking control strategy enables tracking of any program motion predefined by the program constraints. It extends the “trajectory tracking” to the “program motion tracking”. We also demonstrate that our tracking strategy can be extended to a hybrid program motion/force tracking.     

Observer-based design of set-point tracking adaptive controllers for nonlinear chaotic systems

A gradient based approach for the design of set-point tracking adaptive controllers for nonlinear chaotic systems is presented. In this approach, Lyapunov exponents are used to select the controller gain. In the case of unknown or time varying chaotic plants, the Lyapunov exponents may vary during the plant operation. In this paper, an effective adaptive strategy is used for online identification of Lyapunov exponents and adaptive control of nonlinear chaotic plants. Also, a nonlinear observer for estimation of the states is proposed. Simulation results are provided to show the effectiveness of the proposed methodology.     

Adaptive tracking control for a class of switched uncertain nonlinear systems under a new state-dependent switching law

This paper deals with the problem of adaptive fuzzy tracking control for a class of switched uncertain nonlinear systems. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and the adaptive backstepping and dynamic surface control techniques are adopted. First, a new state-dependent switching method is proposed. By introducing convex combination technique and designing a state-dependent switching law, only the solvability of the adaptive tracking control problem for a convex combination of the subsystems is necessary. Second, a new common Lyapunov function with switched adaptive parameters is constructed to reduce the conservatism. Third, to avoid Zeno behavior, a modified state-dependent switching law with dwell time is proposed. It is shown that under the proposed control and switching laws, all the signals of the closed-loop system are bounded and all the state tracking errors can converge to a priori accuracy, even if some subsystems are uncontrollable. Finally, the effectiveness of the proposed method is illustrated through two simulation examples.     

Hybrid control for tracking of invariant manifolds

This paper presents a hybrid control method that controls to unstable equilibria of nonlinear systems by taking advantage of systems’ free dynamics. The approach uses a stable manifold tracking objective in a computationally efficient, optimization-based switching control design. Resulting nonlinear controllers are closed-loop and can be computed in real-time. Our method is validated for the cart–pendulum and the pendubot inversion problems. Results show the proposed approach conserves control effort compared to tracking the desired equilibrium directly. Moreover, the method avoids parameter tuning and reduces sensitivity to initial conditions. The resulting feedback map for the cart–pendulum has a switching structure similar to existing energy based swing-up strategies. We use the Lyapunov function from these prior works to numerically verify local stability for our feedback map. However, unlike the energy based swing-up strategies, our approach does not rely on pre-derived, system-specific switching controllers. We use hybrid optimization to automate switching control synthesis on-line for nonlinear systems.     

The Use of Real-World Computer Models for Computer-Aided Design of Tracking Control Schemes for Induction Motors

The design of tracking controllers for induction motors is usually developed by neglecting the presence of power-supply devices, such as inverters, and measurement apparatuses, e.g., encoders. However, these components represent unmodeled dynamics that are present during the real operating conditions of the induction motor. Since the development of a numerical simulation study represents a low-cost, safe, and fast test to validate the design of tracking control schemes, the need arises to build a computer model of the overall system (i.e., motor, power supply, measurement devices, and tracking controller) as realistic as possible. In this context, the paper describes a computer model for simulation of an induction motor under a tracking control scheme including many real-world effects; namely, encoder's quantization, current sensors' noise, stator current dynamics, presence of a current-controlled voltage-source inverter within a stator current regulator loop, flux observer dynamics, saturation of the control signal, and discrete-time implementation of the control algorithm. The developed computer model is finally used in a case study and the simulation results obtained for an induction motor driving a single-link robotic arm under an H8 tracking control scheme are reported.     

An entire strategy for precise system tracking control of a revolving thin flexural link,part I: Finite element modeling and direct tuning controller design

An entire control strategy including a design based model, controller design, and system output modification for a distributed parameter system is illuminated by application to feedback control of a revolving thin flexural link. In Part I, a very realizable actuator and a sensor, which uses a motor and a tachometer, are applied to design the control system. The finite element modeling and the state space representation are obtained for the purpose of control system analysis and computer simulation. Instead of relying on parameter identification subroutines, a controller design based on directly tuning the parameter of the gain makes the closed-loop absolutely stable and good for system tracking control. This control system design scheme is robust, insensitive to system parameter changes, and this algorithm cannot depend on traditionally priori knowledge such as the system dimension, exact model, or observer design. The performance included in the presence of all the high frequency dynamics can be effectively shown through the computer simulation, and one is led to speculate that this design scheme may perform quite well in the real world implementation.     

Stabilizing chaotic system on periodic orbits using multi-interval and modern optimal control strategies

In this paper, optimal approaches for controlling chaos is studied. The unstable periodic orbits (UPOs) of chaotic system are selected as desired trajectories, which the optimal control strategy should keep the system states on it. Classical gradient-based optimal control methods as well as modern optimization algorithm Particle Swarm Optimization (PSO) are utilized to force the chaotic system to follow the desired UPOs. For better performance, gradient-based is applied in multi-intervals and the results are promising. The Duffing system is selected for examining the proposed approaches. Multi-interval gradient-based approach can put the states on UPOs very fast and keep tracking UPOs with negligible control effort. The maximum control in PSO method is also low. However, due to its inherent random behavior, its control signal is oscillatory.     

Adaptive event-triggered dynamic distributed control of switched positive systems with switching faults

This paper designs the dynamic output-feedback controller of switched positive systems subject to switching faults using an improved adaptive event-triggering mechanism. An adaptive event-triggering condition is addressed in the form of 1-norm by virtue of the measurable outputs of distributed sensors and the corresponding error. An error-based closed-loop control system whose dynamic variable relies on a state observer is obtained. A multiple copositive Lyapunov function is constructed to deal with the positivity and stability of the systems. The matrix decomposition and linear programming approaches are used to design and compute the controller and observer gains. An improved average dwell time scheme is proposed to handle the switching faults. The contributions of this paper lie in that: (i) An adaptive event-triggering mechanism is established for switched positive systems, (ii) A framework on the fault of switching signal is constructed, and (iii) A dynamic distributed controller is proposed for the considered systems. Finally, two illustrative examples are given to verify the effectiveness of the obtained results.     

Multivariable adaptive variable structure disturbance rejection control for DFIG system

A novel variable structure and disturbance rejection control strategy for a wind turbines equipped with a double fed induction generator based on stator‐flux‐oriented vector control is presented. According to estimation of maximum power operation points of wind turbine under stochastic wind velocity profiles and tracking them using traditional offline gain, scheduling and innovative adaptive online method is necessary. To demonstrate the effectiveness of the proposed control strategy, the estimation of maximum operating power point of wind turbine and tracking it under stochastic wind velocity profiles has been considered as a test case. Simulation results show the validity of the proposed technique. © 2014 Wiley Periodicals, Inc. Complexity 21: 50–62, 2016     

Stabilizability of linear switching systems

A complete characterization of stabilizability for linear switching systems is not available in the literature. In this paper, we show that the asymptotic stabilizability of linear switching systems is equivalent to the existence of a hybrid Lyapunov function for the controlled system, for a suitable control strategy. Further, we prove that asymptotic stabilizability of a switching system with minimum dwell time, is equivalent to Input to State Stability (ISS) of the controlled switching system, with a stabilizing control law. We then derive some structural reductions of the hybrid state space, which allow a decomposition of the original problem into simpler subproblems. The relationships between this approach and the well-known Kalman decomposition of linear dynamic control systems are explored.     

Sliding Mode Techniques for Robust Trajectory Tracking as well as State and Parameter Estimation

Practical applications are often affected by uncertainties—more precisely bounded and stochastic disturbances. These have to be considered in robust control procedures to prevent a system from being unstable. Common sliding mode control strategies are often not able to cope with the mentioned impacts simultaneously, because they assume that the considered system is only affected by matched uncertainty. Another problem is the offline computation of the switching amplitude. Under these assumptions, important nonlinear system properties cannot be taken into account within the mathematical model of the system. Therefore, this paper presents sliding mode techniques, that on the one hand are able to consider bounded as well as stochastic uncertainties simultaneously, and on the other hand are not limited to the matched case. Firstly, a sliding mode control procedure taking into account both classes of uncertainty is shown. Additionally, a sliding mode observer for the simultaneous estimation of non-measurable system states and uncertain but bounded parameters is described despite stochastic disturbances. This is possible by using intervals for states and parameters in the resulting stochastic differential equations. Therefore, the Itô differential operator is involved and the system’s stability can be verified despite uncertainties and disturbances for both control and observer procedures. This operator is used for the online computation of the variable structure part gain (matrix of switching amplitudes) which is advantageous in contrast to common sliding mode procedures.     

Analytical failure-detection using adpative,secondary observers

The problem of designing analytical failure-detection systems, using adaptive observers, is addressed in this paper. Failure-detection systems can be applied to linear multi-input, multi-output systems and are related to the examination of then-dimensional observer error vector which carries the necessary information on possible failures. This approach leads toward the design of highly sensitive failure detection systems, obtaining a unique fingerprint for every possible failure (abrupt or soft). In order to keep the observer's false-alarm rate under a certain specified value, it is necessary to have an acceptable matching between the observer model and the system parameters. It is shown here that properly designed adaptive observers are able to detect abrupt changes in the system (actuator, sensor failures, etc.) with adequate reliability. Conditions for convergence for the adaptive observer algorithm are obtained. Good tracking performance with small observer output errors, coupled with accurate and fast parameter identification in both deterministic and stochastic cases, is obtained.Dedicated to G. LeitmannThis research was supported by a National Research Council Associateship at NASA Ames Research Center. The author is indebted to both NRC and NASA Ames Research Center.