Composite‐observer‐based asynchronous control for hidden Markov nonlinear systems with disturbances

International audience

This paper is concerned with the asynchronous dissipative control for discrete-time singular Markov jump systems with multiplicative noises. In a real set, noises always exist in the state or output measurement, which may cause some errors and decrease system performance. Without loss of generality, both state and output multiplicative noises are considered in this paper. The singular systems, which could widely describe more general systems and present traits of physical features, are discussed in this paper. Using a Markov chain to model the asynchronous jumps existing in system modes and controller modes. Through a method of LMI, the sufficient conditions are derived and the asynchronous controller is designed to ensure that the singular Markov jump systems are stochastically admissible and strictly dissipative.

This paper introduces a new scan control technique to realize low area overhead of scan-latches. Single transparent-latch is popularly used for register of high-throughput datapaths. For the scan-test of those kind of circuits, each transparent-latch is replaced with scan-latch. Conventional scan-latch cells controlled by synchronous signals consist of L1 latch and additional L2 latch, both of which function as master latch and slave latch respectively in scan mode. Apparently, additional L2 latch may result in area overhead. In order to avoid the area impact of such an additional L2 latch, we propose new timing methodology employing asynchronous control technique asP* protocol, and introduce asynchronous controlled scan-paths whose scan-latch employs only L1 latch. We evaluate the operation speed with HSPICE simulations and see they are practical. We also suggest DFT structure with our suggested asynchronous scan-paths, which is suitable for conventional synchronous test systems.

Synthesising an asynchronous DMA controller with Balsa

This paper investigates an innovative technology for asynchronous fuzzy preview control (FPC) in nonlinear Markov jump systems (MJS) with time-varying uncertainties and partly unknown transition probabilities using the Takagi-Sugeno (T-S) fuzzy model. It is assumed that the system modes cannot be obtained synchronously by the controller, but instead there is a detector that provides estimated values of the system modes. The proposed asynchronous FPC strategy depending on the detector mode considers time-changing uncertainties and previewable target signals. The asynchronous controller integrates a fuzzy feedback controller, a fuzzy preview controller, and an integrator to address the tracking control problem of target signals. The research constructs a T-S fuzzy augmented error model (AEM) using the error system method and state augmentation technique. Subsequently, the asynchronous FPC design challenge is transformed into a feedback stabilization problem of the AEM. Employing Lyapunov function and LMI techniques, the study derives sufficient conditions for the stability of the AEM in the form of a set of LMIs, presenting the design method of the asynchronous FPC law. The validity of the results is demonstrated through numerical simulations.

In recent years, the problems of asynchronous control and filtering for Markov jump systems (MJSs) have received great research attention from scientific and engineering communities. The asynchronous situation between the original MJSs and the controller/filter is modelled by the hidden Markov model (HMM). This work gives a survey on the recent development of the HMM-based asynchronous controller and filter design for different MJSs, covering linear MJSs, fuzzy MJSs, semi-MJSs and 2D MJSs. Finally, a conclusion is drawn and the potential future research directions of MJSs are also discussed.

Adaptive fuzzy fault-tolerant output feedback control of uncertain nonlinear systems with actuator faults based on dynamic surface technique

Adaptive fuzzy tracking control for a class of uncertain nonaffine nonlinear systems with dead-zone inputs

In this paper, an adaptive fuzzy output feedback control approach is investigated for a class of stochastic nonlinear strict-feedback systems without the requirement of states measurement. The stochastic nonlinear system addressed in this paper is assumed to possess unstructured uncertainties (unknown nonlinear functions) and, in the presence of unmodeled dynamics, dynamics disturbances. Fuzzy logic systems are used to approximate the unstructured uncertainties, and a fuzzy state observer is designed to estimate the unmeasured states. By combining the backstepping design technique with the stochastic small-gain approach, a new adaptive fuzzy output feedback control approach is developed. It is proved that the proposed control approach can guarantee that the closed-loop system is input-state-practically stability (ISpS) in probability, and the observer errors and the output of the system converge to a small neighborhood of the origin by appropriate choice of the design parameters. Simulation results are included to indicate that the proposed adaptive fuzzy control approach has a satisfactory control performance. In addition, the simulation comparisons with the previous methods show that the proposed adaptive fuzzy control approach has robustness to the dynamical uncertainties.

Most optimal control method aim at finding a control law to minimize the corresponding cost function, and the key problem is to solve the Hamilton–Jacobi–Bellman equation (HJBE). However, the solution of HJBE is hard and even unavailable to obtain for nonlinear system and, thus, the optimal control method can not be developed. To address this problem, an adaptive neural inverse optimal control method is proposed for nonlinear systems with external disturbances and actuator failures in this article. The cost function in this article is defined based on the controller and, therefore, once the controller is obtained, the corresponding cost function is minimized. Accordingly, the directly solution of HJBE is avoided. In more detail, the auxiliary system is constructed to develop the auxiliary controller, based on which the actual optimal fault-tolerant controller can be further obtained. The uncertainties in system dynamics are compensated by neural networks, and the unknown actuator failures are handled by adaptive control gain. It is proved that nonlinear system is input-to-state stabilizable (ISS) and the proposed controller can minimize the cost function that puts penalty on tracking error and system input. Two simulation examples are provided to demonstrate the effectiveness of the proposed control method.

Robust adaptive fuzzy filters output feedback control of strict-feedback nonlinear systemsIn this paper, an adaptive fuzzy robust output feedback control approach is proposed for a class of single input single output (SISO) strict-feedback nonlinear systems without measurements of states. The nonlinear systems addressed in this paper are assumed to possess unstructured uncertainties, unmodeled dynamics and dynamic disturbances, where the unstructured uncertainties are not linearly parameterized, and no prior knowledge of their bounds is available. In recursive design, fuzzy logic systems are used to approximate unstructured uncertainties, and K-filters are designed to estimate unmeasured states. By combining backstepping design and a small-gain theorem, a stable adaptive fuzzy output feedback control scheme is developed. It is proven that the proposed adaptive fuzzy control approach can guarantee the all the signals in the closed-loop system are uniformly ultimately bounded, and the output of the controlled system converges to a small neighborhood of the origin. The effectiveness of the proposed approach is illustrated by a simulation example and some comparisons.

In this paper, an adaptive fuzzy output feedback control approach is presented for a class of single-input single-output uncertain nonlinear systems with unknown backlash-like hysteresis and unmeasured states. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and a state filter observer is designed to estimate unmeasured states. Combining the backstepping recursive design with modular design techniques, a new adaptive fuzzy output control scheme is developed. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are semiglobally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of the origin. A simulation is included to illustrate the effectiveness of the proposed approach. The important feature of the proposed control approach is that it can solve the states immeasurable problem of nonlinear systems with unknown backlash-like hysteresis and the problem of unknown virtual control coefficients.

This paper presents a novel design method for asynchronous control logic targeting successive approximation register (SAR) analog-to-digital converters (ADCs). This work is based on modeling the control logic for SAR ADCs using signal transition graphs (STGs). Different from conventional synchronous controllers, the proposed method results in asynchronous controllers driven by the causality of signals rather than relying on clocks to control the conversion process. Moreover, the proposed asynchronous control logic can be modularized through the handshake protocol, making it possible to build ADCs of arbitrary precision based on single-bit control units. This work results in a formal, model-based asynchronous design flow for SAR ADC control, which is shown to produce resulting circuits of similar speeds but great power efficiency improvements.

The paper concerns the problem of reliable dissipative asynchronous controller design for a type of stochastic Markov jump systems (MJSs) with general conditional probabilities in the presence of the dynamic event-triggered rule. Since the mode information of the stochastic MJS is impossible to obtain, a reliable asynchronous controller is established such that the phenomena of nonsynchronous modes between the original stochastic MJS and the developed controller is formulated as a hidden Markov model. A novel dynamic event-triggered strategy is constructed to further decrease the data transmission frequency over the communication network. By taking the internal dynamic variable into account, the criteria of stochastically stable with the reliable dissipativity performance for the resulting closed-loop stochastic MJS are provided based on a collection of linear matrix inequalities. Further, the designs of reliable dissipative asynchronous controller and the dynamic event-triggered strategy are developed simultaneously. Lastly, a numerical instance is supplied to elucidate the superiority of proposed method.

In this note, the guaranteed cost control problem for discrete-time Markovian jump systems with state and input multiplicative noise is solved. Since the model mismatch problem often exists in system modes and controller modes, an asynchronous controller is presented to make the system stochastically stable. By using of the linear matrix inequality techniques, an optimization solution can be obtained, such that the quadratic cost function can be minimized, meanwhile the expectation of the state and control can be guaranteed in a constraint set.