Jimoh O. Pedro

Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system

Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system This paper presents the design of a neural network based feedback linearization (NNFBL) controller for a two degree-of-freedom (DOF), quarter-car, servo-hydraulic vehicle suspension system. The main objective of the direct adaptive NNFBL controller is to improve the systems ride comfort and handling quality. A feedforward, multi-layer perceptron (MLP) neural network (NN) model that is well suited for control by discrete input-output linearization (NNIOL) is developed using input-output data sets obtained from mathematical model simulation. The NN model is trained using the Levenberg-Marquardt optimization algorithm. The proposed controller is compared with a constant-gain PID controller (based on the Ziegler-Nichols tuning method) during suspension travel setpoint tracking in the presence of deterministic road disturbance. Simulation results demonstrate the superior performance of the proposed direct adaptive NNFBL controller over the generic PID controller in rejecting the deterministic road disturbance. This superior performance is achieved at a much lower control cost within the stipulated constraints.

Differential Evolution-Based PID Control of Nonlinear Full-Car Electrohydraulic Suspensions

This paper presents a differential-evolution- (DE-) optimized, independent multiloop proportional-integral-derivative (PID) controller design for full-car nonlinear, electrohydraulic suspension systems. The multiloop PID control stabilises the actuator via force feedback and also improves the system performance. Controller gains are computed using manual tuning and through DE optimization to minimise a performance index, which addresses suspension travel, road holding, vehicle handling, ride comfort, and power consumption constraints. Simulation results showed superior performance of the DE-optimized PID-controlled active vehicle suspension system (AVSS) over the manually tuned PID-controlled AVSS and the passive vehicle suspension system (PVSS).

A nonlinear dynamic inversion-based neurocontroller for unmanned combat aerial vehicles during aerial refuelling

The paper presents the development of modelling and control strategies for a six-degree-of-freedom, unmanned combat aerial vehicle with the inclusion of the centre of gravity position travel during the straight-leg part of an in-flight refuelling manoeuvre. The centre of gravity position travel is found to have a parabolic variation with an increasing mass of aircraft. A nonlinear dynamic inversion-based neurocontroller is designed for the process under investigation. Three radial basis function neural networks are exploited in order to invert the dynamics of the system, one for each control channel. Modal and time-domain analysis results show that the dynamic properties of the aircraft are strongly influenced during aerial refuelling. The effectiveness of the proposed control law is demonstrated through the use of simulation results for an F-16 aircraft. The longitudinal neurocontroller provided interesting results, and performed better than a baseline nonlinear dynamic inversion controller without neural network. On the other hand, the lateral-directional nonlinear dynamic inversion-based neurocontroller did not perform well as the longitudinal controller. It was concluded that the nonlinear dynamic inversion-based neurocontroller could be applied to control an unmanned combat aerial vehicle during aerial refuelling.

PID control of a nonlinear half-car active suspension system via force feedback

This paper presents the design of a two-loop, force/suspension travel PID control system, for a four degree-of-freedom (DOF), nonlinear, half-car active vehicle suspension system (AVSS). The two-loop system consists of an inner PID hydraulic actuator force control loop and an outer PID suspension travel control loop. Performance of the PID based AVSS is compared to a passive, nonlinear, half-car suspension system with the same model parameters. The simulation results showed the superior performance of the AVSS in the presence of the deterministic road disturbance.

Neural network-based model predictive control of a servo-hydraulic vehicle suspension system

This paper presents the design of a multi-layer feedforward neural network-based model predictive controller (NNMPC) for a two degree-of-freedom (DOF), quarter-car servo-hydraulic vehicle suspension system. The nonlinear dynamics of the servo-hydraulic actuator is incorporated in the suspension model and thus a suspension travel controller is developed to indirectly improve the ride comfort and handling quality of the suspension system. A SISO feedforward multi-layer perceptron (MLP) neural network (NN) model is developed using input-output data sets obtained from the mathematical model simulation. Levenberg-Marquandt algorithm was employed in training the NN model. The NNMPC was used to predict the future responses that are optimized in a sub-loop of the plant for cost minimization. The proposed controller is compared with an optimally tuned constant-gain PID controller (based on Ziegler-Nichols tuning method) during suspension travel setpoint tracking in the presence of deterministic road input disturbance. Simulation results demonstrate the superior performance of the NNMPC over the generic PID - based in adapting to the deterministic road disturbance.

Intelligent feedback linearization control of nonlinear electrohydraulic suspension systems using particle swarm optimization

Graphical abstractDisplay Omitted HighlightsWe design an active vehicle suspension system controller using computational intelligence technique.We compare the performance of this controller with the performance of passive, PID and non-optimized intelligent controllers.Robustness to parameter variations analysis is carried out in this paper.The proposed controller improved the vehicle ride comfort, road holding and disturbance rejection properties. The core factors governing the performance of active vehicle suspension systems (AVSS) are the inherent trade-offs involving suspension travel, ride comfort, road holding and power consumption. In addition to this, robustness to parameter variations is an essential issue that affects the effectiveness of highly nonlinear electrohydraulic AVSS. Therefore, this paper proposes a nonlinear control approach using dynamic neural network (DNN)-based input-output feedback linearization (FBL) for a quarter-car AVSS. The gains of the proposed controllers and the weights of the DNNs are selected using particle swarm optimization (PSO) algorithm while addressing simultaneously the AVSS trade-offs. Robustness and effectiveness of the proposed controller were demonstrated through simulations.

Direct adaptive neural control of a quarter-car active suspension system

This paper presents the design and implementation of a direct adaptive neural network (DANN) based feedback linearization controller for a two degree of freedom (2DOF), quarter-car active vehicle suspension system (AVSS). The main objective is to improve ride comfort and handling quality. The constant gain PID controller (based on Ziegler-Nichols tuning method) is used to benchmark the DANN controller during a suspension travel sinusoidal set point tracking in the presence of deterministic road disturbance. The maximum sprung mass acceleration of the DANN controller was about 2.723ms−2, for the PID, it was 2.676ms−2. The tire deflection was approximately 5mm and 4mm for the DANN and the PID controllers. The performance of the DANN controller was achieved at a marginally higher cost of the control input.

Nonlinear control of quadrotor UAV using Takagi-Sugeno fuzzy logic technique

A fuzzy logic controller was designed for a quadrotor unmanned aerial vehicle (UAV) with the aim of stabilizing it in the presence of atmospheric disturbances. Quadrotors are complex flying machines that are inherently unstable having both parametric and structural uncertainties. A mathematical model was designed and implemented into the MATLAB/Simulink environment in order to run simulations. The mathematical model proved to be a sufficient representation of the dynamics and physics of a quadrotor, incorporating all 6 degrees of freedom. A full system identification was performed in order to validate the three attitudes of the quadrotor, namely the roll, pitch and yaw angles. The Takagi-Sugeno (T-S) model used for the validation, together with the Band-Limited White noise signal ensured that all the dynamics of the system were excited. The T-S model yielded good results which gave the approval for the controller design. Simulation results showed that the performance of the controller designed was acceptable and can be improved by using global optimization techniques in the future.

Enhanced slip control performance using nonlinear passive suspension system

Antilock Brake System (ABS) controller maintains or controls the slip between tyre and road to maximize the braking torque to achieve a shorter braking distance and control of the steering wheel. This paper presents a PID slip controller performance that incorporates nonlinear passive suspension dynamics. Three scenarios were compared The first scenario is the performance of the controller in a vehicle model without any suspension dynamics, the second scenario incorporates linear passive vehicle suspension system (LPVSS) and the third scenario incorporates nonlinear passive vehicle suspension system (NLPVSS). The incorporation of the passive suspension dynamics enhanced the ABS performance.

Linear Slip Control Formulation for Vehicular Anti-Lock Braking System with Suspension Effects

Abstract This paper presents the formulation of a slip-control model for purposes of performing slip tracking of target slip. System modeling is performed to develop a braking model incorporating an active suspension. Linearisation of the highly non-linear multi-input multi-output developed Anti-lock Braking System model is performed by way of input-output feedback linearisation. Feedback linearisation is shown to provide a transformed linear ABS model while ensuring a verifiable stable state transformation. Lie algebra is used to find the stability of the internal dynamics through zero dynamics analysis. Simulation results demonstrate the validity of the approach along with the development of a stabilising condition for the linearisation approach.