This paper proposes an adaptive control for vehicle active suspensions with unknown nonlinearities (e.g., nonlinear springs and piecewise dampers). A prescribed performance function that characterizes the convergence rate, maximum overshoot, and steady-state error is incorporated into the control design to stabilize the vertical and pitch motions, such that both the transient and steady-state suspension response are guaranteed. Moreover, a novel adaptive law is used to achieve precise estimation of essential parameters (e.g., mass of vehicle body and moment of inertia for pitch motion), where the parameter estimation error is obtained explicitly and then used as a new leakage term. Theoretical studies prove the convergence of the estimated parameters, and compare the suggested controller with generic adaptive controllers using the gradient descent and e-modification schemes. In addition to motion displacements, dynamic tire loads and suspension travel constraints are also considered. Extensive comparative simulations on a dynamic simulator consisting of commercial vehicle simulation software Carsim 8.1 and MATLAB Simulink are provided to show the efficacy of the proposed control, and to illustrate the improved performance.

This paper presents a novel control strategy for nonlinear uncertain vehicle active suspension systems without using any function approximators [e.g., neural networks (NNs) or fuzzy logic systems (FLSs)]. Unlike previous results that neglect the effect of actuator dynamics, this paper incorporates the dynamics of a hydraulic actuator that is used to create the required active suspension forces into the controller design. To address the nonlinearities of this hydraulic system, an approximation-free control method is introduced. In this method, the widely used NNs and FLSs are not needed. This leads to reduced computational burden and complexity, and thus, it is more suited for practical applications. Moreover, by introducing a prescribed performance function and the associated error transform, the proposed controller can guarantee both the transient and steady-state suspension responses. The stability of the closed-loop system and the suspension performance requirements are rigorously proved. Finally, comparative simulations are conducted to validate the improved performance and robustness of the proposed method.

Most of the existing control methods for servo systems driven by hydraulic actuators have been developed by using backstepping scheme and assuming that all system states (including internal hydraulic signals) are measurable. In this paper, we propose a new control design method for high-order servo systems with hydraulic actuator dynamics, where the backstepping scheme is avoided and only the system output (e.g., motion displacement) is required for the control implementation. For this purpose, the system model is first transformed into a canonical form, where the unknown dynamics in the system are lumped as one term. Then, we introduce a simple robust unknown dynamics estimator (UDE) that has only one tuning parameter but achieves exponential error convergence to accommodate the lumped uncertainties. Therefore, the function approximators (e.g., neural network and fuzzy systems) can be avoided, leading to reduced computational costs, simpler parameter tuning and improved convergence as compared to backstepping methods. Extensive simulations and experiments based on a realistic test-rig are conducted to show the efficacy of the proposed control.

In this paper, we propose an alternative simple, yet efficient estimation method to handle unknown dynamics and external disturbances for motion control of robotic systems. An unknown system dynamics estimator (USDE) is first proposed by introducing filter operations and simple algebraic calculations, where the external disturbances and unknown Coriolis/gravity dynamics can be estimated simultaneously. In the specific case where only the external disturbance is unknown, a further modified unknown disturbance estimator (MUDE) is introduced. These proposed USDE and MUDE can be easily implemented and their parameter tuning is straightforward, compared with the nonlinear disturbance observer (NDO) that requires to calculate the inverse of the inertia matrix. The acceleration signal of robotic joints is not used in the design of estimators. Moreover, we also show that the proposed estimators can be incorporated into the design of composite controllers to achieve satisfactory motion tracking response. The closed-loop control system stability and convergence of both the tracking error and estimation error are all guaranteed. Finally, the effectiveness of the two proposed methods is validated by using simulations and experiments based on a SCARA robot test-rig.

This article proposes a novel control method for vehicle active suspension systems in the presence of time-varying input delay and unknown nonlinearities. An unknown system dynamics estimator (USDE), which employs first-order low-pass filter operations and has only one tuning parameter, is constructed to deal with unknown nonlinearities. With this USDE, the widely used function approximators (e.g., neural networks and fuzzy-logic systems) are not needed, and the intermediate variables and observer used in the traditional estimators are not required. This estimator has a reduced computational burden, trivial parameter tuning and guaranteed convergence. Moreover, a predictor-based compensation strategy is developed to handle the time-varying input delay. Finally, we combine the suggested USDE and predictor to design a feedback controller to attenuate the vibrations of vehicle body and retain the required suspension performances. Theoretical analysis is carried out via the Lyapunov–Krasovkii functional to prove the stability of the closed-loop system. Simulation results based on professional vehicle simulation software Carsim are provided to show the efficiency of the proposed control scheme.