Steady Tracking Performance Research Articles

Motion control of connected and automated vehicles in a lane‐changing scenario with guaranteed tracking performance

AbstractThe coupled, nonlinear vehicle dynamics and multi‐vehicle cooperative strategies make the lane‐changing maneuver control of vehicle platoons a challenging task. This paper presents the dynamic modeling and motion control of connected and automated vehicles (CAVs) for lane‐changing maneuver, utilizing the ideology of constraint‐following servo control. To guarantee a transient and steady tracking performance, a series of equality constraints and bilateral inequality constraints bounded by decaying functions or scalar constants are imposed on the dynamic state to formulate a constrained dynamical system. Since the original Udwadia‐Kalaba (UK) approach cannot handle inequality constraints and underactuated systems, a creative diffeomorphism method is applied to transform the bounded state into an unbounded one. Then, based on the UK approach and Lyapunov stability theory, a novel constraint‐following controller is designed to render the transformed states asymptotically stable in a full actuated system, and to achieve uniform stability performance in an underactuated system. Finally, numerical simulations are used to validate the effectiveness of the proposed control methodology.

Finite-Time Convergent Control of Electrohydraulic Velocity Servo System Under Uncertain Parameter and External Load

The disturbances suppression is one common control problem in electrohydraulic systems (EHSs) since both unknown external load and hydraulic parametric uncertainty often obviously degrade the tracking performance and bias the load pressure of EHS. In this paper, a finite-time convergent controller (FTCC) is tried to use in EHS to address this problem. Different from the asymptotic convergent controller, this FTCC not only improve the dynamic and steady tracking performance of the velocity servo system but also guarantee the system state error convergent to zero in a finite time. According to the finite-time stable principle, the FTCC is derived by backstepping and fractional-type Lyapunov techniques. The effectiveness of the proposed controller is verified by comparative simulation and experimental results with the other traditional and advanced controllers.

Event‐triggered control for a saturated nonlinear system with prescribed performance and finite‐time convergence

SummaryThis paper presents an event‐triggered controller for a class of saturated uncertain nonlinear systems. We develop a performance constrained finite‐time controller to guarantee that the tracking error converges at a prescribed convergence rate and does not exceed the given maximum overshoot. A smooth function is designed to replace the absolute and signum operators in existing finite‐time controllers that lead to nondifferentiable virtual controls. Then, a novel backstepping design consisting of an adaptive law and an auxiliary system governed by a smooth switching function is developed to compensate for the uncertainty, the triggering event threshold, and the saturation constraint. Theoretical analysis demonstrates that under the proposed controller, all closed‐loop signals are bounded and the Zeno behavior is avoided. Furthermore, the tracking error will converge toward a residual set in finite time, and the prescribed transient and steady tracking performance bounds are never violated. Results from a comparative simulation study illustrate the effectiveness and advantages of the proposed method.

Path Following of a Surface Vessel With Prescribed Performance in the Presence of Input Saturation and External Disturbances

This paper presents a path following controller of a surface vessel with a prescribed performance in the presence of input saturation and external disturbances. Based on the three degrees-of-freedom model of the surface vessel, the designed backstepping control scheme features three functional parts, namely, guidance, attitude control, and velocity control. To guarantee that the position errors are confined within the prescribed convergence rates and maximum overshoot, a performance constrained guidance law is formulated with an error transformed function. Command filters are incorporated in the control subsections to limit the magnitude of the virtual controls and simultaneously avoid arduous computations involving their time derivatives. Subsequently, auxiliary systems that are governed by smooth switching functions are developed in an unprecedented manner to compensate for the saturation constraints on actuators. Nonlinear disturbance observers are concurrently introduced to estimate the unknown external disturbances for increasing system's robustness. It is demonstrated that under the proposed control, the prescribed transient and steady tracking performance bounds are never violated, and all closed-loop signals remain uniformly ultimately bounded, despite the presence of input saturation and disturbances. Results from a comparative simulation study illustrate the effectiveness and advantages of the proposed method.

Dynamic learning from adaptive neural control with predefined performance for a class of nonlinear systems

In this paper, a neural learning mechanism is presented for a class of single-input–single-output (SISO) uncertain nonlinear systems, which can achieve knowledge acquisition, storage and reuse of the unknown system dynamics as well as the predefined tracking error behavior bound. Using the novel transformed function, the constrained tracking control problem of the original nonlinear system is transformed into the stabilization problem of an augmented system. By combining a filter tracking error with the universal approximation capabilities of radial basis function (RBF) neural networks (NNs), a stable adaptive neural control (ANC) scheme is proposed to guarantee the ultimate boundedness of all the signals in the closed-loop system and the prescribed transient and steady tracking control performance. In the steady control process, a partial persistent excitation (PE) condition of RBF NNs is satisfied during tracking control to recurrent reference orbits. Consequently, it is shown that the proposed ANC scheme can acquire and store knowledge of the unknown system dynamics. The stored knowledge is reused to develop neural learning control, so that the improved control performance with the faster tracking convergence rate and the less computational burden is achieved, while guaranteeing the prescribed transient and steady tracking performance when the initial condition satisfies the prescribed performance bound. Simulation studies are performed to demonstrate and verify the effectiveness of the proposed scheme.