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The problem of output feedback stabilization for a class of stochastic switched planar systems (Sto-SPS) subjected to asymmetric output constraints is investigated. A new tangent-type asymmetric barrier Lyapunov function (BLF) with quartic terms, which is independent of nonlinear terms of Sto-SPS and takes the pre-set asymmetric output constraint into consideration, is developed first. Next, the proposed BLF is exploited to renovate the adding a power integrator technique elegantly and state feedback stabilizers are presented systematically. Then, with the deliberately constructed stochastic reduced-order switched observers, an observer-based output feedback control strategy is thereby established while guaranteeing the pre-specified asymmetric output constraint successfully. Ascribing to its unified nature, the proposed scheme is effective for Sto-SPS whether output constraints are imposed on or not, which eliminates the need for additional modifications to the controllers’ structure. The presented simulations demonstrate the effectiveness of the proposed method.

This paper investigates the finite-time static output feedback control of Markovian switching systems, where quantization effects are taken into consideration from plant to controller and controller to actuator, simultaneously. The resulting system is more general, where asynchronous control, quantization, actuator failure, and external disturbance are covered. Furthermore, a descriptor representation method is employed to eliminate both the coupling term and the quantization effects. Owing to a hidden mode observation approach, sufficient conditions are achieved to guarantee the finite-time stochastic boundedness of the resulting system, and the finite-time output feedback controller is designed. Finally, a vehicle’s throttle actuator is exploited to confirm the feasibility of the proposed method.

In this paper, neural network control is presented for a rehabilitation robot with unknown system dynamics. To deal with the system uncertainties and improve the system robustness, adaptive neural networks are used to approximate the unknown model of the robot and adapt interactions between the robot and the patient. Both full state feedback control and output feedback control are considered in this paper. With the proposed control, uniform ultimate boundedness of the closed loop system is achieved in the context of Lyapunov’s stability theory and its associated techniques. The state of the system is proven to converge to a small neighborhood of zero by appropriately choosing design parameters. Extensive simulations for a rehabilitation robot with constraints are carried out to illustrate the effectiveness of the proposed control.

While control design objectives are formulated most commonly in terms of asymptotic behavior (as time goes to infinity) of signals in the closed-loop system, the recently developed notion of “prescribed-time” stabilization considers closed-loop signal behavior over a fixed (prescribed) time interval and addresses the problem of regulating the state to the origin in the prescribed time irrespective of the initial state. While prior results on prescribed-time stabilization considered a chain of integrators with uncertainties matched with the control input (i.e., normal form), we consider here a general class of nonlinear strict-feedback-like systems with state-dependent uncertainties allowed throughout the system dynamics including uncertain parameters (without requirement of any known bounds on the uncertain parameters). Furthermore, we address the output-feedback problem and show that a dynamic observer and controller can be designed based on our dual dynamic high gain scaling based design methodology along with a novel temporal transformation and form of the scaling dynamics with temporal forcing terms to achieve both state estimation and regulation in the prescribed time.

Traditional input–output feedback linearization requires full knowledge of system dynamics and assumes no disturbance at the input channel and no system’s uncertainties. In this paper, a model-free active input–output feedback linearization technique based on an improved active disturbance rejection control paradigm is proposed to design feedback linearization control law for a generalized nonlinear system with a known relative degree. The linearization control law is composed of a scaled generalized disturbance estimated by an improved nonlinear extended state observer with saturation-like behavior and the nominal control signal produced by an improved nonlinear state error feedback. The proposed active input–output feedback linearization cancels in real-time fashion the generalized disturbances which represent all the unwanted dynamics, exogenous disturbances, and system uncertainties and transforms the system into a chain of integrators up to the relative degree of the system, which is the only information required about the nonlinear system. Stability analysis has been conducted based on the Lyapunov functions and revealed the convergence of the improved nonlinear extended state observer and the asymptotic stability of the closed-loop system. Verification of the outcomes has been achieved by applying the proposed active input–output feedback linearization technique on the single-link flexible joint manipulator. The simulations results validated the effectiveness of the proposed active input–output feedback linearization tool based on improved active disturbance rejection control as compared to the conventional active disturbance rejection control–based active input–output feedback linearization and the traditional input–output feedback linearization techniques.

This paper is devoted to stabilizing the high-order uncertain nonlinear system in a fixed time by output feedback control. First, a novel settling time solution method is proposed by establishing an indirect double system and using the comparison principle. Then a fixed-time observer and a neural networked based adaptive law are constructed to estimate the state and the unknown disturbance for the high-order uncertain nonlinear system. Furthermore, a fixed-time output feedback controller is proposed via the homogeneity technique. The upper bound of the settling time is analyzed for the closed-loop system under the proposed output feedback control. Finally, simulation examples are given to illustrate the effectiveness of the theoretical results.

This paper is devoted to weighted H ∞ consensus design for continuous-time/discrete-time stochastic multi-agent systems with average dwell time (ADT) switching topologies and external disturbances via output feedback. By introducing a linear transformation, the closed-loop systems are changed into reduced-order systems and, at the same time, the issue of weighted H ∞ consensus design is transformed into a weighted H ∞ control problem. Then, Lyapunov conditions are established for the mean-square asymptotic stability and weighted H ∞ disturbance attenuation of the reduced-order systems. Based on them, two sufficient conditions are derived for the existence of desired output-feedback control protocols through the feasible solution of a series of linear matrix inequalities. Finally, two numerical examples are given to illustrate the effectiveness of the proposed results.

This paper presents an adaptive learning method to achieve the output-feedback robust tracking control of systems with uncertain dynamics, which uses the techniques developed for optimal control. An augmented system is first constructed using the system state and desired output trajectory. Then, the robust tracking control problem is equivalent to the optimal tracking control problem with an appropriate cost function. To design the output-feedback optimal tracking control, an output tracking algebraic Riccati equation (OTARE) is then constructed, which can be used in the online learning process. To obtain the solution of the derived OTARE, an online adaptive learning method is proposed, where the input gain matrix is removed. In this learning algorithm, only the system output information is required and the observers widely used in the output-feedback optimal control design are removed. Simulations based on the power system are given to test the proposed method.

This paper investigates an approximation-free output feedback prescribed performance control for a half-vehicle active suspension systems to improve driving comfort. Different from prior results that ignore actuator dynamics, this paper factored hydraulic actuators into the controller design. To solve the nonlinearities of the hydraulic active suspension system, an approximation-free, backstepping-free control scheme is developed, where function approximators (e.g. neural networks and fuzzy systems) and the explosion of complexity in backstepping design can be avoided. In this sense, the heavy computational burden can be removed. Moreover, by using a high-gain observer (HGO) and a prescribed performance function, the proposed controller simply requires the system outputs to be available and can achieve prescribed transient and steady-state performance of system outputs. To stop the propagation of peak phenomena caused by the HGO into the suspension system, the proposed controller is designed to saturate properly without affecting system performance attributes. The stability of the suspension system and the performance requirements of the system output are strictly proven. Finally, the comparative simulations are conducted to validate the effectiveness of the proposed method for improving suspension performance.